From 1e583f3d5fe4ff783f2474dcc99a3aa649246bb4 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 11:27:53 +0200 Subject: [PATCH 01/15] Add Missing Data Imputation Methods #146 Adde some automated tests --- .../missing_data/gen_air_passengers_tests.py | 16 ++++++ tests/missing_data/gen_ozone_tests.py | 16 ++++++ ...ta_air_passengers_DiscardRow_DiscardRow.py | 3 ++ ...a_air_passengers_DiscardRow_Interpolate.py | 3 ++ ...ing_data_air_passengers_DiscardRow_None.py | 3 ++ ...a_air_passengers_Interpolate_DiscardRow.py | 3 ++ ..._air_passengers_Interpolate_Interpolate.py | 3 ++ ...ng_data_air_passengers_Interpolate_None.py | 3 ++ ...ing_data_air_passengers_None_DiscardRow.py | 3 ++ ...ng_data_air_passengers_None_Interpolate.py | 3 ++ ...t_missing_data_air_passengers_None_None.py | 3 ++ ...est_missing_data_air_passengers_generic.py | 54 +++++++++++++++++++ ...issing_data_ozone_DiscardRow_DiscardRow.py | 3 ++ ...ssing_data_ozone_DiscardRow_Interpolate.py | 3 ++ ...test_missing_data_ozone_DiscardRow_None.py | 3 ++ ...ssing_data_ozone_Interpolate_DiscardRow.py | 3 ++ ...sing_data_ozone_Interpolate_Interpolate.py | 3 ++ ...est_missing_data_ozone_Interpolate_None.py | 3 ++ ...test_missing_data_ozone_None_DiscardRow.py | 3 ++ ...est_missing_data_ozone_None_Interpolate.py | 3 ++ .../test_missing_data_ozone_None_None.py | 3 ++ .../test_missing_data_ozone_generic.py | 54 +++++++++++++++++++ 22 files changed, 194 insertions(+) create mode 100644 tests/missing_data/gen_air_passengers_tests.py create mode 100644 tests/missing_data/gen_ozone_tests.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_DiscardRow_None.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_Interpolate_None.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_None_DiscardRow.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_None_Interpolate.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_None_None.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_generic.py create mode 100644 tests/missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py create mode 100644 tests/missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py create mode 100644 tests/missing_data/test_missing_data_ozone_DiscardRow_None.py create mode 100644 tests/missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py create mode 100644 tests/missing_data/test_missing_data_ozone_Interpolate_Interpolate.py create mode 100644 tests/missing_data/test_missing_data_ozone_Interpolate_None.py create mode 100644 tests/missing_data/test_missing_data_ozone_None_DiscardRow.py create mode 100644 tests/missing_data/test_missing_data_ozone_None_Interpolate.py create mode 100644 tests/missing_data/test_missing_data_ozone_None_None.py create mode 100644 tests/missing_data/test_missing_data_ozone_generic.py diff --git a/tests/missing_data/gen_air_passengers_tests.py b/tests/missing_data/gen_air_passengers_tests.py new file mode 100644 index 000000000..fd41eb6a9 --- /dev/null +++ b/tests/missing_data/gen_air_passengers_tests.py @@ -0,0 +1,16 @@ + + + + + +def gen_all_air_passengers(): + lDir = "tests/missing_data" + for iTimeImp in [None , "DiscardRow", "Interpolate"]: + for iSigImp in [None , "DiscardRow", "Interpolate"]: + filename = lDir + "/test_missing_data_air_passengers_" + str(iTimeImp) + "_" + str(iSigImp) + ".py"; + with open(filename, "w") as outfile: + print("WRTITING_FILE" , filename) + outfile.write("import tests.missing_data.test_missing_data_air_passengers_generic as gen\n\n") + outfile.write("gen.test_air_passengers_missing_data" + str((iTimeImp , iSigImp)) + "\n") + +# gen_all_air_passengers() diff --git a/tests/missing_data/gen_ozone_tests.py b/tests/missing_data/gen_ozone_tests.py new file mode 100644 index 000000000..75697eb22 --- /dev/null +++ b/tests/missing_data/gen_ozone_tests.py @@ -0,0 +1,16 @@ + + + + + +def gen_all_ozone(): + lDir = "tests/missing_data" + for iTimeImp in [None , "DiscardRow", "Interpolate"]: + for iSigImp in [None , "DiscardRow", "Interpolate"]: + filename = lDir + "/test_missing_data_ozone_" + str(iTimeImp) + "_" + str(iSigImp) + ".py"; + with open(filename, "w") as outfile: + print("WRTITING_FILE" , filename) + outfile.write("import tests.missing_data.test_missing_data_ozone_generic as gen\n\n") + outfile.write("gen.test_ozone_missing_data" + str((iTimeImp , iSigImp)) + "\n") + +# gen_all_ozone() diff --git a/tests/missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py new file mode 100644 index 000000000..47ada5ad5 --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('DiscardRow', 'DiscardRow') diff --git a/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py new file mode 100644 index 000000000..72150968d --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('DiscardRow', 'Interpolate') diff --git a/tests/missing_data/test_missing_data_air_passengers_DiscardRow_None.py b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_None.py new file mode 100644 index 000000000..b9678f274 --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_None.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('DiscardRow', None) diff --git a/tests/missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py b/tests/missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py new file mode 100644 index 000000000..7b413aadf --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('Interpolate', 'DiscardRow') diff --git a/tests/missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py b/tests/missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py new file mode 100644 index 000000000..5bc1ec2ac --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('Interpolate', 'Interpolate') diff --git a/tests/missing_data/test_missing_data_air_passengers_Interpolate_None.py b/tests/missing_data/test_missing_data_air_passengers_Interpolate_None.py new file mode 100644 index 000000000..ff29049a2 --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_Interpolate_None.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('Interpolate', None) diff --git a/tests/missing_data/test_missing_data_air_passengers_None_DiscardRow.py b/tests/missing_data/test_missing_data_air_passengers_None_DiscardRow.py new file mode 100644 index 000000000..87005d286 --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_None_DiscardRow.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data(None, 'DiscardRow') diff --git a/tests/missing_data/test_missing_data_air_passengers_None_Interpolate.py b/tests/missing_data/test_missing_data_air_passengers_None_Interpolate.py new file mode 100644 index 000000000..f83addccc --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_None_Interpolate.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data(None, 'Interpolate') diff --git a/tests/missing_data/test_missing_data_air_passengers_None_None.py b/tests/missing_data/test_missing_data_air_passengers_None_None.py new file mode 100644 index 000000000..61d42e66a --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_None_None.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data(None, None) diff --git a/tests/missing_data/test_missing_data_air_passengers_generic.py b/tests/missing_data/test_missing_data_air_passengers_generic.py new file mode 100644 index 000000000..15480932c --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_generic.py @@ -0,0 +1,54 @@ + +import pandas as pd +import numpy as np + + +import pyaf.ForecastEngine as autof +import pyaf.Bench.TS_datasets as tsds + +def add_some_missing_data_in_signal(df, col): + lRate = 0.2 + df.loc[df.sample(frac=lRate, random_state=1960).index, col] = np.nan + return df + +def add_some_missing_data_in_time(df, col): + lRate = 0.2 + df.loc[df.sample(frac=lRate, random_state=1960).index, col] = np.nan + return df + + +def test_air_passengers_missing_data(iTimeMissingDataImputation, iSignalMissingDataImputation): + + b1 = tsds.load_airline_passengers() + df = b1.mPastData + + if(iTimeMissingDataImputation is not None): + df = add_some_missing_data_in_time(df, b1.mTimeVar) + if(iSignalMissingDataImputation is not None): + df = add_some_missing_data_in_signal(df, b1.mSignalVar) + + lEngine = autof.cForecastEngine() + H = b1.mHorizon; + lEngine.mOptions.mMissingDataOptions.mTimeMissingDataImputation = iTimeMissingDataImputation + lEngine.mOptions.mMissingDataOptions.mSignalMissingDataImputation = iSignalMissingDataImputation + lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H); + lEngine.getModelInfo(); + print(lEngine.mSignalDecomposition.mTrPerfDetails.head()); + + dfapp_in = df.copy(); + dfapp_in.tail() + dfapp_out = lEngine.forecast(dfapp_in, H); + #dfapp_out.to_csv("outputs/ozone_apply_out.csv") + dfapp_out.tail(2 * H) + print("Forecast Columns " , dfapp_out.columns); + Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']] + print(Forecast_DF.info()) + print("Forecasts\n" , Forecast_DF.tail(H)); + + print("\n\n") + print(lEngine.to_json()); + print("\n\n") + print("\n\n") + print(Forecast_DF.tail(2*H).to_json(date_format='iso')) + print("\n\n") + diff --git a/tests/missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py b/tests/missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py new file mode 100644 index 000000000..130dd630a --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('DiscardRow', 'DiscardRow') diff --git a/tests/missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py b/tests/missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py new file mode 100644 index 000000000..5325951af --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('DiscardRow', 'Interpolate') diff --git a/tests/missing_data/test_missing_data_ozone_DiscardRow_None.py b/tests/missing_data/test_missing_data_ozone_DiscardRow_None.py new file mode 100644 index 000000000..b90b1824c --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_DiscardRow_None.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('DiscardRow', None) diff --git a/tests/missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py b/tests/missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py new file mode 100644 index 000000000..9dde69fcb --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('Interpolate', 'DiscardRow') diff --git a/tests/missing_data/test_missing_data_ozone_Interpolate_Interpolate.py b/tests/missing_data/test_missing_data_ozone_Interpolate_Interpolate.py new file mode 100644 index 000000000..754d6f94c --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_Interpolate_Interpolate.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('Interpolate', 'Interpolate') diff --git a/tests/missing_data/test_missing_data_ozone_Interpolate_None.py b/tests/missing_data/test_missing_data_ozone_Interpolate_None.py new file mode 100644 index 000000000..87c26c9e1 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_Interpolate_None.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('Interpolate', None) diff --git a/tests/missing_data/test_missing_data_ozone_None_DiscardRow.py b/tests/missing_data/test_missing_data_ozone_None_DiscardRow.py new file mode 100644 index 000000000..cb9e6e0df --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_None_DiscardRow.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data(None, 'DiscardRow') diff --git a/tests/missing_data/test_missing_data_ozone_None_Interpolate.py b/tests/missing_data/test_missing_data_ozone_None_Interpolate.py new file mode 100644 index 000000000..376eaa6a0 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_None_Interpolate.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data(None, 'Interpolate') diff --git a/tests/missing_data/test_missing_data_ozone_None_None.py b/tests/missing_data/test_missing_data_ozone_None_None.py new file mode 100644 index 000000000..9a5e0e0fc --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_None_None.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data(None, None) diff --git a/tests/missing_data/test_missing_data_ozone_generic.py b/tests/missing_data/test_missing_data_ozone_generic.py new file mode 100644 index 000000000..506c90697 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_generic.py @@ -0,0 +1,54 @@ + +import pandas as pd +import numpy as np + + +import pyaf.ForecastEngine as autof +import pyaf.Bench.TS_datasets as tsds + +def add_some_missing_data_in_signal(df, col): + lRate = 0.2 + df.loc[df.sample(frac=lRate, random_state=1960).index, col] = np.nan + return df + +def add_some_missing_data_in_time(df, col): + lRate = 0.2 + df.loc[df.sample(frac=lRate, random_state=1960).index, col] = np.nan + return df + + +def test_ozone_missing_data(iTimeMissingDataImputation, iSignalMissingDataImputation): + + b1 = tsds.load_ozone() + df = b1.mPastData + + if(iTimeMissingDataImputation is not None): + df = add_some_missing_data_in_time(df, b1.mTimeVar) + if(iSignalMissingDataImputation is not None): + df = add_some_missing_data_in_signal(df, b1.mSignalVar) + + lEngine = autof.cForecastEngine() + H = b1.mHorizon; + lEngine.mOptions.mMissingDataOptions.mTimeMissingDataImputation = iTimeMissingDataImputation + lEngine.mOptions.mMissingDataOptions.mSignalMissingDataImputation = iSignalMissingDataImputation + lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H); + lEngine.getModelInfo(); + print(lEngine.mSignalDecomposition.mTrPerfDetails.head()); + + dfapp_in = df.copy(); + dfapp_in.tail() + dfapp_out = lEngine.forecast(dfapp_in, H); + #dfapp_out.to_csv("outputs/ozone_apply_out.csv") + dfapp_out.tail(2 * H) + print("Forecast Columns " , dfapp_out.columns); + Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']] + print(Forecast_DF.info()) + print("Forecasts\n" , Forecast_DF.tail(H)); + + print("\n\n") + print(lEngine.to_json()); + print("\n\n") + print("\n\n") + print(Forecast_DF.tail(2*H).to_json(date_format='iso')) + print("\n\n") + From f08054045e711a07612b21807c5f4a5ac155c578 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 11:30:45 +0200 Subject: [PATCH 02/15] Add Missing Data Imputation Methods #146 Updated a Makefile --- tests/Makefile | 119 +++++++++++++++++++++++++++++++++++++++++- tests/gen_makefile.py | 4 +- 2 files changed, 119 insertions(+), 4 deletions(-) diff --git a/tests/Makefile b/tests/Makefile index 1a5ca51ed..047068230 100644 --- a/tests/Makefile +++ b/tests/Makefile @@ -891,6 +891,121 @@ hierarchical/test_hierarchy_AU_TD.py : hierarchical : hierarchical/test_hierarchy_AU_TD.py hierarchical/test_hierarchy_AU_OC.py hierarchical/test_hierarchy_AU_MO.py hierarchical/test_hierarchy_AU_BU.py hierarchical/test_hierarchy_AU_AllMethods_Exogenous_per_node.py hierarchical/test_hierarchy_AU_AllMethods_Exogenous_all_nodes.py hierarchical/test_hierarchy_AU_AllMethods.py hierarchical/test_hierarchy_AU.py hierarchical/test_grouped_signals_TD.py hierarchical/test_grouped_signals_OC.py hierarchical/test_grouped_signals_MO.py hierarchical/test_grouped_signals_BU.py hierarchical/test_grouped_signals_AllMethods_Exogenous_per_node.py hierarchical/test_grouped_signals_AllMethods_Exogenous_all_nodes.py hierarchical/test_grouped_signals_AllMethods_2.py hierarchical/test_grouped_signals_AllMethods.py hierarchical/test_grouped.py +missing_data/gen_air_passengers_tests.py : + $(PYTHON) tests/missing_data/gen_air_passengers_tests.py > logs/missing_data_gen_air_passengers_tests.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_gen_air_passengers_tests.log logs/missing_data_gen_air_passengers_tests.log > logs/missing_data_gen_air_passengers_tests.log.diff + tail -10 logs/missing_data_gen_air_passengers_tests.log.diff + +missing_data/gen_ozone_tests.py : + $(PYTHON) tests/missing_data/gen_ozone_tests.py > logs/missing_data_gen_ozone_tests.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_gen_ozone_tests.log logs/missing_data_gen_ozone_tests.log > logs/missing_data_gen_ozone_tests.log.diff + tail -10 logs/missing_data_gen_ozone_tests.log.diff + +missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py > logs/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log.diff + +missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py > logs/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log.diff + +missing_data/test_missing_data_air_passengers_DiscardRow_None.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_DiscardRow_None.py > logs/missing_data_test_missing_data_air_passengers_DiscardRow_None.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_None.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_None.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_None.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_DiscardRow_None.log.diff + +missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py > logs/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log logs/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log.diff + +missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py > logs/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log logs/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log.diff + +missing_data/test_missing_data_air_passengers_Interpolate_None.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_Interpolate_None.py > logs/missing_data_test_missing_data_air_passengers_Interpolate_None.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_None.log logs/missing_data_test_missing_data_air_passengers_Interpolate_None.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_None.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_Interpolate_None.log.diff + +missing_data/test_missing_data_air_passengers_None_DiscardRow.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_None_DiscardRow.py > logs/missing_data_test_missing_data_air_passengers_None_DiscardRow.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_DiscardRow.log logs/missing_data_test_missing_data_air_passengers_None_DiscardRow.log > logs/missing_data_test_missing_data_air_passengers_None_DiscardRow.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_None_DiscardRow.log.diff + +missing_data/test_missing_data_air_passengers_None_Interpolate.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_None_Interpolate.py > logs/missing_data_test_missing_data_air_passengers_None_Interpolate.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_Interpolate.log logs/missing_data_test_missing_data_air_passengers_None_Interpolate.log > logs/missing_data_test_missing_data_air_passengers_None_Interpolate.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_None_Interpolate.log.diff + +missing_data/test_missing_data_air_passengers_None_None.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_None_None.py > logs/missing_data_test_missing_data_air_passengers_None_None.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_None.log logs/missing_data_test_missing_data_air_passengers_None_None.log > logs/missing_data_test_missing_data_air_passengers_None_None.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_None_None.log.diff + +missing_data/test_missing_data_air_passengers_generic.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_generic.py > logs/missing_data_test_missing_data_air_passengers_generic.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_generic.log logs/missing_data_test_missing_data_air_passengers_generic.log > logs/missing_data_test_missing_data_air_passengers_generic.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_generic.log.diff + +missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py > logs/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log logs/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log > logs/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log.diff + +missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py > logs/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log logs/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log > logs/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log.diff + +missing_data/test_missing_data_ozone_DiscardRow_None.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_DiscardRow_None.py > logs/missing_data_test_missing_data_ozone_DiscardRow_None.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_None.log logs/missing_data_test_missing_data_ozone_DiscardRow_None.log > logs/missing_data_test_missing_data_ozone_DiscardRow_None.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_DiscardRow_None.log.diff + +missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py > logs/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log logs/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log > logs/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log.diff + +missing_data/test_missing_data_ozone_Interpolate_Interpolate.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_Interpolate_Interpolate.py > logs/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log logs/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log > logs/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log.diff + +missing_data/test_missing_data_ozone_Interpolate_None.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_Interpolate_None.py > logs/missing_data_test_missing_data_ozone_Interpolate_None.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_None.log logs/missing_data_test_missing_data_ozone_Interpolate_None.log > logs/missing_data_test_missing_data_ozone_Interpolate_None.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_Interpolate_None.log.diff + +missing_data/test_missing_data_ozone_None_DiscardRow.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_None_DiscardRow.py > logs/missing_data_test_missing_data_ozone_None_DiscardRow.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_DiscardRow.log logs/missing_data_test_missing_data_ozone_None_DiscardRow.log > logs/missing_data_test_missing_data_ozone_None_DiscardRow.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_None_DiscardRow.log.diff + +missing_data/test_missing_data_ozone_None_Interpolate.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_None_Interpolate.py > logs/missing_data_test_missing_data_ozone_None_Interpolate.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_Interpolate.log logs/missing_data_test_missing_data_ozone_None_Interpolate.log > logs/missing_data_test_missing_data_ozone_None_Interpolate.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_None_Interpolate.log.diff + +missing_data/test_missing_data_ozone_None_None.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_None_None.py > logs/missing_data_test_missing_data_ozone_None_None.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_None.log logs/missing_data_test_missing_data_ozone_None_None.log > logs/missing_data_test_missing_data_ozone_None_None.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_None_None.log.diff + +missing_data/test_missing_data_ozone_generic.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_generic.py > logs/missing_data_test_missing_data_ozone_generic.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_generic.log logs/missing_data_test_missing_data_ozone_generic.log > logs/missing_data_test_missing_data_ozone_generic.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_generic.log.diff + + + + missing_data : missing_data/test_missing_data_ozone_generic.py missing_data/test_missing_data_ozone_None_None.py missing_data/test_missing_data_ozone_None_Interpolate.py missing_data/test_missing_data_ozone_None_DiscardRow.py missing_data/test_missing_data_ozone_Interpolate_None.py missing_data/test_missing_data_ozone_Interpolate_Interpolate.py missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py missing_data/test_missing_data_ozone_DiscardRow_None.py missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py missing_data/test_missing_data_air_passengers_generic.py missing_data/test_missing_data_air_passengers_None_None.py missing_data/test_missing_data_air_passengers_None_Interpolate.py missing_data/test_missing_data_air_passengers_None_DiscardRow.py missing_data/test_missing_data_air_passengers_Interpolate_None.py missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py missing_data/test_missing_data_air_passengers_DiscardRow_None.py missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py missing_data/gen_ozone_tests.py missing_data/gen_air_passengers_tests.py + + model_control/test_ozone_all_models_enabled.py : $(PYTHON) tests/model_control/test_ozone_all_models_enabled.py > logs/model_control_test_ozone_all_models_enabled.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/model_control_test_ozone_all_models_enabled.log logs/model_control_test_ozone_all_models_enabled.log > logs/model_control_test_ozone_all_models_enabled.log.diff @@ -1384,9 +1499,9 @@ xgb/test_ozone_xgbx_exogenous.py : # ********************************************** -all: artificial basic_checks bugs cross_validation croston exog expsmooth func HeartRateTimeSeries heroku hierarchical HourOfWeek model_control perf svr transformations neuralnet real-life time_res perfs demos xgb xeon-phi-parallel sampling temporal_hierarchy WeekOfMonth +all: artificial basic_checks bugs cross_validation croston exog expsmooth func HeartRateTimeSeries heroku hierarchical HourOfWeek model_control perf svr transformations neuralnet real-life time_res perfs demos xgb xeon-phi-parallel sampling temporal_hierarchy WeekOfMonth missing_data -build-test : demos basic_checks cross_validation croston exog heroku hierarchical model_control perfs svr transformations func real-life time_res xgb sampling HourOfWeek WeekOfMonth +build-test : demos basic_checks cross_validation croston exog heroku hierarchical model_control perfs svr transformations func real-life time_res xgb sampling HourOfWeek WeekOfMonth missing_data diff --git a/tests/gen_makefile.py b/tests/gen_makefile.py index e3a6a8afd..b621a6ae9 100644 --- a/tests/gen_makefile.py +++ b/tests/gen_makefile.py @@ -27,7 +27,7 @@ def add_makefile_entry(subdir1): return test_target; -str1 = "artificial basic_checks bugs cross_validation croston exog expsmooth func HeartRateTimeSeries heroku hierarchical HourOfWeek model_control perf svr transformations neuralnet real-life time_res perfs demos xgb xeon-phi-parallel sampling temporal_hierarchy WeekOfMonth"; +str1 = "artificial basic_checks bugs cross_validation croston exog expsmooth func HeartRateTimeSeries heroku hierarchical HourOfWeek model_control perf svr transformations neuralnet real-life time_res perfs demos xgb xeon-phi-parallel sampling temporal_hierarchy WeekOfMonth missing_data"; subdirs = str1.split(); print("PYTHON=python3\n\n"); @@ -45,6 +45,6 @@ def add_makefile_entry(subdir1): print("all: " , str1 , "\n\t\n"); -str2 = "demos basic_checks cross_validation croston exog heroku hierarchical model_control perfs svr transformations func real-life time_res xgb sampling HourOfWeek WeekOfMonth"; +str2 = "demos basic_checks cross_validation croston exog heroku hierarchical model_control perfs svr transformations func real-life time_res xgb sampling HourOfWeek WeekOfMonth missing_data"; print("build-test : " , str2 , "\n\t\n"); From 9882f2fdb8da62c7509436a188d6718b236f2a23 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 11:49:53 +0200 Subject: [PATCH 03/15] Corrected standardPlots() Detected by travis-ci --- pyaf/TS/SignalDecomposition.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pyaf/TS/SignalDecomposition.py b/pyaf/TS/SignalDecomposition.py index 3246b34fe..169cdae07 100644 --- a/pyaf/TS/SignalDecomposition.py +++ b/pyaf/TS/SignalDecomposition.py @@ -745,7 +745,10 @@ def standardPlots(self, name = None, format = 'png'): start_time = time.time() logger.info("START_PLOTTING") for lSignal in self.mSignals: - self.mBestModels[lSignal].standardPlots(name + "_" + lSignal, format); + lName = name + if(name is not None): + lName = str(name) + "_" + str(lSignal) + self.mBestModels[lSignal].standardPlots(lName, format); lPlotTime = time.time() - start_time; logger.info("END_PLOTTING_TIME_IN_SECONDS " + str(lPlotTime)) From 66d8dd8503987c5a0c8d68e2c1e1281546f7aacd Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 17:53:37 +0200 Subject: [PATCH 04/15] Add Missing Data Imputation Methods #146 Added more signal imputation methods --- pyaf/TS/MissingData.py | 21 +++++++++++++++++++++ pyaf/TS/Options.py | 3 ++- 2 files changed, 23 insertions(+), 1 deletion(-) diff --git a/pyaf/TS/MissingData.py b/pyaf/TS/MissingData.py index fc40857f3..8d5e780a6 100644 --- a/pyaf/TS/MissingData.py +++ b/pyaf/TS/MissingData.py @@ -55,6 +55,27 @@ def apply_signal_imputation_method(self, iInputDS, iSignal): elif(self.mOptions.mMissingDataOptions.mSignalMissingDataImputation == "Interpolate"): lSignal = self.interpolate_signal_if_needed(iInputDS , iSignal) iInputDS[iSignal] = lSignal + + elif(self.mOptions.mMissingDataOptions.mSignalMissingDataImputation == "Constant"): + lSignal = iInputDS[iSignal].fillna(self.mOptions.mMissingDataOptions.mConstant, method=None) + iInputDS[iSignal] = lSignal + + elif(self.mOptions.mMissingDataOptions.mSignalMissingDataImputation == "Mean"): + lMean = iInputDS[iSignal].median() + lSignal = iInputDS[iSignal].fillna(lMean, method=None) + iInputDS[iSignal] = lSignal + + elif(self.mOptions.mMissingDataOptions.mSignalMissingDataImputation == "Median"): + lMedian = iInputDS[iSignal].median() + lSignal = iInputDS[iSignal].fillna(lMedian, method=None) + iInputDS[iSignal] = lSignal + + elif(self.mOptions.mMissingDataOptions.mSignalMissingDataImputation == "PreviousValue"): + lSignal = iInputDS[iSignal].fillna(method='ffill') + # replace the first empty values with the first known value + lSignal = lSignal.fillna(method='bfill') + iInputDS[iSignal] = lSignal + return iInputDS def interpolate_time_if_needed(self, iInputDS , iTime): diff --git a/pyaf/TS/Options.py b/pyaf/TS/Options.py index af967f5c3..67c2a3e84 100644 --- a/pyaf/TS/Options.py +++ b/pyaf/TS/Options.py @@ -126,8 +126,9 @@ def __init__(self): class cMissingDataOptions: def __init__(self): - self.mSignalMissingDataImputation = None # [None , "DiscardRow", "Interpolate"] + self.mSignalMissingDataImputation = None # [None , "DiscardRow", "Interpolate", "Mean", "Median" , "Constant" , "PreviousValue"] self.mTimeMissingDataImputation = None # [None , "DiscardRow", "Interpolate"] + self.mConstant = 0.0 class cSignalDecomposition_Options(cModelControl): From f3200a4ccdf5bb9e3f65397e28c878f02776ea04 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 17:54:39 +0200 Subject: [PATCH 05/15] Add Missing Data Imputation Methods #146 Added more signal imputation methods --- tests/missing_data/gen_air_passengers_tests.py | 2 +- tests/missing_data/gen_ozone_tests.py | 2 +- .../test_missing_data_air_passengers_DiscardRow_Constant.py | 3 +++ .../test_missing_data_air_passengers_DiscardRow_Mean.py | 3 +++ .../test_missing_data_air_passengers_DiscardRow_Median.py | 3 +++ ...est_missing_data_air_passengers_DiscardRow_PreviousValue.py | 3 +++ .../test_missing_data_air_passengers_Interpolate_Constant.py | 3 +++ .../test_missing_data_air_passengers_Interpolate_Mean.py | 3 +++ .../test_missing_data_air_passengers_Interpolate_Median.py | 3 +++ ...st_missing_data_air_passengers_Interpolate_PreviousValue.py | 3 +++ .../test_missing_data_air_passengers_None_Constant.py | 3 +++ .../missing_data/test_missing_data_air_passengers_None_Mean.py | 3 +++ .../test_missing_data_air_passengers_None_Median.py | 3 +++ .../test_missing_data_air_passengers_None_PreviousValue.py | 3 +++ .../test_missing_data_ozone_DiscardRow_Constant.py | 3 +++ tests/missing_data/test_missing_data_ozone_DiscardRow_Mean.py | 3 +++ .../missing_data/test_missing_data_ozone_DiscardRow_Median.py | 3 +++ .../test_missing_data_ozone_DiscardRow_PreviousValue.py | 3 +++ .../test_missing_data_ozone_Interpolate_Constant.py | 3 +++ tests/missing_data/test_missing_data_ozone_Interpolate_Mean.py | 3 +++ .../missing_data/test_missing_data_ozone_Interpolate_Median.py | 3 +++ .../test_missing_data_ozone_Interpolate_PreviousValue.py | 3 +++ tests/missing_data/test_missing_data_ozone_None_Constant.py | 3 +++ tests/missing_data/test_missing_data_ozone_None_Mean.py | 3 +++ tests/missing_data/test_missing_data_ozone_None_Median.py | 3 +++ .../missing_data/test_missing_data_ozone_None_PreviousValue.py | 3 +++ 26 files changed, 74 insertions(+), 2 deletions(-) create mode 100644 tests/missing_data/test_missing_data_air_passengers_DiscardRow_Constant.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_DiscardRow_Mean.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_DiscardRow_Median.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_DiscardRow_PreviousValue.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_Interpolate_Constant.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_Interpolate_Mean.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_Interpolate_Median.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_Interpolate_PreviousValue.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_None_Constant.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_None_Mean.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_None_Median.py create mode 100644 tests/missing_data/test_missing_data_air_passengers_None_PreviousValue.py create mode 100644 tests/missing_data/test_missing_data_ozone_DiscardRow_Constant.py create mode 100644 tests/missing_data/test_missing_data_ozone_DiscardRow_Mean.py create mode 100644 tests/missing_data/test_missing_data_ozone_DiscardRow_Median.py create mode 100644 tests/missing_data/test_missing_data_ozone_DiscardRow_PreviousValue.py create mode 100644 tests/missing_data/test_missing_data_ozone_Interpolate_Constant.py create mode 100644 tests/missing_data/test_missing_data_ozone_Interpolate_Mean.py create mode 100644 tests/missing_data/test_missing_data_ozone_Interpolate_Median.py create mode 100644 tests/missing_data/test_missing_data_ozone_Interpolate_PreviousValue.py create mode 100644 tests/missing_data/test_missing_data_ozone_None_Constant.py create mode 100644 tests/missing_data/test_missing_data_ozone_None_Mean.py create mode 100644 tests/missing_data/test_missing_data_ozone_None_Median.py create mode 100644 tests/missing_data/test_missing_data_ozone_None_PreviousValue.py diff --git a/tests/missing_data/gen_air_passengers_tests.py b/tests/missing_data/gen_air_passengers_tests.py index fd41eb6a9..b35e5eaf3 100644 --- a/tests/missing_data/gen_air_passengers_tests.py +++ b/tests/missing_data/gen_air_passengers_tests.py @@ -6,7 +6,7 @@ def gen_all_air_passengers(): lDir = "tests/missing_data" for iTimeImp in [None , "DiscardRow", "Interpolate"]: - for iSigImp in [None , "DiscardRow", "Interpolate"]: + for iSigImp in [None , "DiscardRow", "Interpolate", "Mean", "Median" , "Constant" , "PreviousValue"]: filename = lDir + "/test_missing_data_air_passengers_" + str(iTimeImp) + "_" + str(iSigImp) + ".py"; with open(filename, "w") as outfile: print("WRTITING_FILE" , filename) diff --git a/tests/missing_data/gen_ozone_tests.py b/tests/missing_data/gen_ozone_tests.py index 75697eb22..cad48fd5f 100644 --- a/tests/missing_data/gen_ozone_tests.py +++ b/tests/missing_data/gen_ozone_tests.py @@ -6,7 +6,7 @@ def gen_all_ozone(): lDir = "tests/missing_data" for iTimeImp in [None , "DiscardRow", "Interpolate"]: - for iSigImp in [None , "DiscardRow", "Interpolate"]: + for iSigImp in [None , "DiscardRow", "Interpolate", "Mean", "Median" , "Constant" , "PreviousValue"]: filename = lDir + "/test_missing_data_ozone_" + str(iTimeImp) + "_" + str(iSigImp) + ".py"; with open(filename, "w") as outfile: print("WRTITING_FILE" , filename) diff --git a/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Constant.py b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Constant.py new file mode 100644 index 000000000..e25c6d518 --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Constant.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('DiscardRow', 'Constant') diff --git a/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Mean.py b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Mean.py new file mode 100644 index 000000000..72d5e417e --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Mean.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('DiscardRow', 'Mean') diff --git a/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Median.py b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Median.py new file mode 100644 index 000000000..e95efdca1 --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_Median.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('DiscardRow', 'Median') diff --git a/tests/missing_data/test_missing_data_air_passengers_DiscardRow_PreviousValue.py b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_PreviousValue.py new file mode 100644 index 000000000..47a8ffe21 --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_DiscardRow_PreviousValue.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('DiscardRow', 'PreviousValue') diff --git a/tests/missing_data/test_missing_data_air_passengers_Interpolate_Constant.py b/tests/missing_data/test_missing_data_air_passengers_Interpolate_Constant.py new file mode 100644 index 000000000..d71eeccae --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_Interpolate_Constant.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('Interpolate', 'Constant') diff --git a/tests/missing_data/test_missing_data_air_passengers_Interpolate_Mean.py b/tests/missing_data/test_missing_data_air_passengers_Interpolate_Mean.py new file mode 100644 index 000000000..d9be0c64d --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_Interpolate_Mean.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('Interpolate', 'Mean') diff --git a/tests/missing_data/test_missing_data_air_passengers_Interpolate_Median.py b/tests/missing_data/test_missing_data_air_passengers_Interpolate_Median.py new file mode 100644 index 000000000..d513ad0fa --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_Interpolate_Median.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('Interpolate', 'Median') diff --git a/tests/missing_data/test_missing_data_air_passengers_Interpolate_PreviousValue.py b/tests/missing_data/test_missing_data_air_passengers_Interpolate_PreviousValue.py new file mode 100644 index 000000000..614b88085 --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_Interpolate_PreviousValue.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data('Interpolate', 'PreviousValue') diff --git a/tests/missing_data/test_missing_data_air_passengers_None_Constant.py b/tests/missing_data/test_missing_data_air_passengers_None_Constant.py new file mode 100644 index 000000000..da97bf65c --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_None_Constant.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data(None, 'Constant') diff --git a/tests/missing_data/test_missing_data_air_passengers_None_Mean.py b/tests/missing_data/test_missing_data_air_passengers_None_Mean.py new file mode 100644 index 000000000..c7f52184e --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_None_Mean.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data(None, 'Mean') diff --git a/tests/missing_data/test_missing_data_air_passengers_None_Median.py b/tests/missing_data/test_missing_data_air_passengers_None_Median.py new file mode 100644 index 000000000..2a181eddd --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_None_Median.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data(None, 'Median') diff --git a/tests/missing_data/test_missing_data_air_passengers_None_PreviousValue.py b/tests/missing_data/test_missing_data_air_passengers_None_PreviousValue.py new file mode 100644 index 000000000..8aaa95406 --- /dev/null +++ b/tests/missing_data/test_missing_data_air_passengers_None_PreviousValue.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_air_passengers_generic as gen + +gen.test_air_passengers_missing_data(None, 'PreviousValue') diff --git a/tests/missing_data/test_missing_data_ozone_DiscardRow_Constant.py b/tests/missing_data/test_missing_data_ozone_DiscardRow_Constant.py new file mode 100644 index 000000000..68e6cc5a1 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_DiscardRow_Constant.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('DiscardRow', 'Constant') diff --git a/tests/missing_data/test_missing_data_ozone_DiscardRow_Mean.py b/tests/missing_data/test_missing_data_ozone_DiscardRow_Mean.py new file mode 100644 index 000000000..e6a6b8ee0 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_DiscardRow_Mean.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('DiscardRow', 'Mean') diff --git a/tests/missing_data/test_missing_data_ozone_DiscardRow_Median.py b/tests/missing_data/test_missing_data_ozone_DiscardRow_Median.py new file mode 100644 index 000000000..02d9deaa6 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_DiscardRow_Median.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('DiscardRow', 'Median') diff --git a/tests/missing_data/test_missing_data_ozone_DiscardRow_PreviousValue.py b/tests/missing_data/test_missing_data_ozone_DiscardRow_PreviousValue.py new file mode 100644 index 000000000..2b0210dd1 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_DiscardRow_PreviousValue.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('DiscardRow', 'PreviousValue') diff --git a/tests/missing_data/test_missing_data_ozone_Interpolate_Constant.py b/tests/missing_data/test_missing_data_ozone_Interpolate_Constant.py new file mode 100644 index 000000000..bb6378dcc --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_Interpolate_Constant.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('Interpolate', 'Constant') diff --git a/tests/missing_data/test_missing_data_ozone_Interpolate_Mean.py b/tests/missing_data/test_missing_data_ozone_Interpolate_Mean.py new file mode 100644 index 000000000..932a5bdef --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_Interpolate_Mean.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('Interpolate', 'Mean') diff --git a/tests/missing_data/test_missing_data_ozone_Interpolate_Median.py b/tests/missing_data/test_missing_data_ozone_Interpolate_Median.py new file mode 100644 index 000000000..eae36394d --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_Interpolate_Median.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('Interpolate', 'Median') diff --git a/tests/missing_data/test_missing_data_ozone_Interpolate_PreviousValue.py b/tests/missing_data/test_missing_data_ozone_Interpolate_PreviousValue.py new file mode 100644 index 000000000..eec334b62 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_Interpolate_PreviousValue.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data('Interpolate', 'PreviousValue') diff --git a/tests/missing_data/test_missing_data_ozone_None_Constant.py b/tests/missing_data/test_missing_data_ozone_None_Constant.py new file mode 100644 index 000000000..8cb5123d9 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_None_Constant.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data(None, 'Constant') diff --git a/tests/missing_data/test_missing_data_ozone_None_Mean.py b/tests/missing_data/test_missing_data_ozone_None_Mean.py new file mode 100644 index 000000000..de2847fa6 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_None_Mean.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data(None, 'Mean') diff --git a/tests/missing_data/test_missing_data_ozone_None_Median.py b/tests/missing_data/test_missing_data_ozone_None_Median.py new file mode 100644 index 000000000..f3300dd41 --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_None_Median.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data(None, 'Median') diff --git a/tests/missing_data/test_missing_data_ozone_None_PreviousValue.py b/tests/missing_data/test_missing_data_ozone_None_PreviousValue.py new file mode 100644 index 000000000..028a4c4ed --- /dev/null +++ b/tests/missing_data/test_missing_data_ozone_None_PreviousValue.py @@ -0,0 +1,3 @@ +import tests.missing_data.test_missing_data_ozone_generic as gen + +gen.test_ozone_missing_data(None, 'PreviousValue') From ec3a149442a72781fad707aa62e2bbd104f19aa6 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 17:57:37 +0200 Subject: [PATCH 06/15] Add Missing Data Imputation Methods #146 Updated a Makefile --- tests/Makefile | 122 ++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 121 insertions(+), 1 deletion(-) diff --git a/tests/Makefile b/tests/Makefile index 047068230..eb3f4c765 100644 --- a/tests/Makefile +++ b/tests/Makefile @@ -901,6 +901,11 @@ missing_data/gen_ozone_tests.py : $(PYTHON) scripts/num_diff.py tests/references/missing_data_gen_ozone_tests.log logs/missing_data_gen_ozone_tests.log > logs/missing_data_gen_ozone_tests.log.diff tail -10 logs/missing_data_gen_ozone_tests.log.diff +missing_data/test_missing_data_air_passengers_DiscardRow_Constant.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_DiscardRow_Constant.py > logs/missing_data_test_missing_data_air_passengers_DiscardRow_Constant.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Constant.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_Constant.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_Constant.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_DiscardRow_Constant.log.diff + missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py : $(PYTHON) tests/missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py > logs/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log.diff @@ -911,11 +916,31 @@ missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py : $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log.diff tail -10 logs/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log.diff +missing_data/test_missing_data_air_passengers_DiscardRow_Mean.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_DiscardRow_Mean.py > logs/missing_data_test_missing_data_air_passengers_DiscardRow_Mean.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Mean.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_Mean.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_Mean.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_DiscardRow_Mean.log.diff + +missing_data/test_missing_data_air_passengers_DiscardRow_Median.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_DiscardRow_Median.py > logs/missing_data_test_missing_data_air_passengers_DiscardRow_Median.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Median.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_Median.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_Median.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_DiscardRow_Median.log.diff + missing_data/test_missing_data_air_passengers_DiscardRow_None.py : $(PYTHON) tests/missing_data/test_missing_data_air_passengers_DiscardRow_None.py > logs/missing_data_test_missing_data_air_passengers_DiscardRow_None.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_None.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_None.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_None.log.diff tail -10 logs/missing_data_test_missing_data_air_passengers_DiscardRow_None.log.diff +missing_data/test_missing_data_air_passengers_DiscardRow_PreviousValue.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_DiscardRow_PreviousValue.py > logs/missing_data_test_missing_data_air_passengers_DiscardRow_PreviousValue.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_PreviousValue.log logs/missing_data_test_missing_data_air_passengers_DiscardRow_PreviousValue.log > logs/missing_data_test_missing_data_air_passengers_DiscardRow_PreviousValue.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_DiscardRow_PreviousValue.log.diff + +missing_data/test_missing_data_air_passengers_Interpolate_Constant.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_Interpolate_Constant.py > logs/missing_data_test_missing_data_air_passengers_Interpolate_Constant.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Constant.log logs/missing_data_test_missing_data_air_passengers_Interpolate_Constant.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_Constant.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_Interpolate_Constant.log.diff + missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py : $(PYTHON) tests/missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py > logs/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log logs/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log.diff @@ -926,11 +951,31 @@ missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py : $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log logs/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log.diff tail -10 logs/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log.diff +missing_data/test_missing_data_air_passengers_Interpolate_Mean.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_Interpolate_Mean.py > logs/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log logs/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log.diff + +missing_data/test_missing_data_air_passengers_Interpolate_Median.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_Interpolate_Median.py > logs/missing_data_test_missing_data_air_passengers_Interpolate_Median.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Median.log logs/missing_data_test_missing_data_air_passengers_Interpolate_Median.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_Median.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_Interpolate_Median.log.diff + missing_data/test_missing_data_air_passengers_Interpolate_None.py : $(PYTHON) tests/missing_data/test_missing_data_air_passengers_Interpolate_None.py > logs/missing_data_test_missing_data_air_passengers_Interpolate_None.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_None.log logs/missing_data_test_missing_data_air_passengers_Interpolate_None.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_None.log.diff tail -10 logs/missing_data_test_missing_data_air_passengers_Interpolate_None.log.diff +missing_data/test_missing_data_air_passengers_Interpolate_PreviousValue.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_Interpolate_PreviousValue.py > logs/missing_data_test_missing_data_air_passengers_Interpolate_PreviousValue.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_Interpolate_PreviousValue.log logs/missing_data_test_missing_data_air_passengers_Interpolate_PreviousValue.log > logs/missing_data_test_missing_data_air_passengers_Interpolate_PreviousValue.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_Interpolate_PreviousValue.log.diff + +missing_data/test_missing_data_air_passengers_None_Constant.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_None_Constant.py > logs/missing_data_test_missing_data_air_passengers_None_Constant.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_Constant.log logs/missing_data_test_missing_data_air_passengers_None_Constant.log > logs/missing_data_test_missing_data_air_passengers_None_Constant.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_None_Constant.log.diff + missing_data/test_missing_data_air_passengers_None_DiscardRow.py : $(PYTHON) tests/missing_data/test_missing_data_air_passengers_None_DiscardRow.py > logs/missing_data_test_missing_data_air_passengers_None_DiscardRow.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_DiscardRow.log logs/missing_data_test_missing_data_air_passengers_None_DiscardRow.log > logs/missing_data_test_missing_data_air_passengers_None_DiscardRow.log.diff @@ -941,16 +986,36 @@ missing_data/test_missing_data_air_passengers_None_Interpolate.py : $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_Interpolate.log logs/missing_data_test_missing_data_air_passengers_None_Interpolate.log > logs/missing_data_test_missing_data_air_passengers_None_Interpolate.log.diff tail -10 logs/missing_data_test_missing_data_air_passengers_None_Interpolate.log.diff +missing_data/test_missing_data_air_passengers_None_Mean.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_None_Mean.py > logs/missing_data_test_missing_data_air_passengers_None_Mean.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_Mean.log logs/missing_data_test_missing_data_air_passengers_None_Mean.log > logs/missing_data_test_missing_data_air_passengers_None_Mean.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_None_Mean.log.diff + +missing_data/test_missing_data_air_passengers_None_Median.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_None_Median.py > logs/missing_data_test_missing_data_air_passengers_None_Median.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_Median.log logs/missing_data_test_missing_data_air_passengers_None_Median.log > logs/missing_data_test_missing_data_air_passengers_None_Median.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_None_Median.log.diff + missing_data/test_missing_data_air_passengers_None_None.py : $(PYTHON) tests/missing_data/test_missing_data_air_passengers_None_None.py > logs/missing_data_test_missing_data_air_passengers_None_None.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_None.log logs/missing_data_test_missing_data_air_passengers_None_None.log > logs/missing_data_test_missing_data_air_passengers_None_None.log.diff tail -10 logs/missing_data_test_missing_data_air_passengers_None_None.log.diff +missing_data/test_missing_data_air_passengers_None_PreviousValue.py : + $(PYTHON) tests/missing_data/test_missing_data_air_passengers_None_PreviousValue.py > logs/missing_data_test_missing_data_air_passengers_None_PreviousValue.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_None_PreviousValue.log logs/missing_data_test_missing_data_air_passengers_None_PreviousValue.log > logs/missing_data_test_missing_data_air_passengers_None_PreviousValue.log.diff + tail -10 logs/missing_data_test_missing_data_air_passengers_None_PreviousValue.log.diff + missing_data/test_missing_data_air_passengers_generic.py : $(PYTHON) tests/missing_data/test_missing_data_air_passengers_generic.py > logs/missing_data_test_missing_data_air_passengers_generic.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_air_passengers_generic.log logs/missing_data_test_missing_data_air_passengers_generic.log > logs/missing_data_test_missing_data_air_passengers_generic.log.diff tail -10 logs/missing_data_test_missing_data_air_passengers_generic.log.diff +missing_data/test_missing_data_ozone_DiscardRow_Constant.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_DiscardRow_Constant.py > logs/missing_data_test_missing_data_ozone_DiscardRow_Constant.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_Constant.log logs/missing_data_test_missing_data_ozone_DiscardRow_Constant.log > logs/missing_data_test_missing_data_ozone_DiscardRow_Constant.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_DiscardRow_Constant.log.diff + missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py : $(PYTHON) tests/missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py > logs/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log logs/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log > logs/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log.diff @@ -961,11 +1026,31 @@ missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py : $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log logs/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log > logs/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log.diff tail -10 logs/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log.diff +missing_data/test_missing_data_ozone_DiscardRow_Mean.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_DiscardRow_Mean.py > logs/missing_data_test_missing_data_ozone_DiscardRow_Mean.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_Mean.log logs/missing_data_test_missing_data_ozone_DiscardRow_Mean.log > logs/missing_data_test_missing_data_ozone_DiscardRow_Mean.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_DiscardRow_Mean.log.diff + +missing_data/test_missing_data_ozone_DiscardRow_Median.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_DiscardRow_Median.py > logs/missing_data_test_missing_data_ozone_DiscardRow_Median.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_Median.log logs/missing_data_test_missing_data_ozone_DiscardRow_Median.log > logs/missing_data_test_missing_data_ozone_DiscardRow_Median.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_DiscardRow_Median.log.diff + missing_data/test_missing_data_ozone_DiscardRow_None.py : $(PYTHON) tests/missing_data/test_missing_data_ozone_DiscardRow_None.py > logs/missing_data_test_missing_data_ozone_DiscardRow_None.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_None.log logs/missing_data_test_missing_data_ozone_DiscardRow_None.log > logs/missing_data_test_missing_data_ozone_DiscardRow_None.log.diff tail -10 logs/missing_data_test_missing_data_ozone_DiscardRow_None.log.diff +missing_data/test_missing_data_ozone_DiscardRow_PreviousValue.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_DiscardRow_PreviousValue.py > logs/missing_data_test_missing_data_ozone_DiscardRow_PreviousValue.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_DiscardRow_PreviousValue.log logs/missing_data_test_missing_data_ozone_DiscardRow_PreviousValue.log > logs/missing_data_test_missing_data_ozone_DiscardRow_PreviousValue.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_DiscardRow_PreviousValue.log.diff + +missing_data/test_missing_data_ozone_Interpolate_Constant.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_Interpolate_Constant.py > logs/missing_data_test_missing_data_ozone_Interpolate_Constant.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_Constant.log logs/missing_data_test_missing_data_ozone_Interpolate_Constant.log > logs/missing_data_test_missing_data_ozone_Interpolate_Constant.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_Interpolate_Constant.log.diff + missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py : $(PYTHON) tests/missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py > logs/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log logs/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log > logs/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log.diff @@ -976,11 +1061,31 @@ missing_data/test_missing_data_ozone_Interpolate_Interpolate.py : $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log logs/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log > logs/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log.diff tail -10 logs/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log.diff +missing_data/test_missing_data_ozone_Interpolate_Mean.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_Interpolate_Mean.py > logs/missing_data_test_missing_data_ozone_Interpolate_Mean.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_Mean.log logs/missing_data_test_missing_data_ozone_Interpolate_Mean.log > logs/missing_data_test_missing_data_ozone_Interpolate_Mean.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_Interpolate_Mean.log.diff + +missing_data/test_missing_data_ozone_Interpolate_Median.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_Interpolate_Median.py > logs/missing_data_test_missing_data_ozone_Interpolate_Median.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_Median.log logs/missing_data_test_missing_data_ozone_Interpolate_Median.log > logs/missing_data_test_missing_data_ozone_Interpolate_Median.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_Interpolate_Median.log.diff + missing_data/test_missing_data_ozone_Interpolate_None.py : $(PYTHON) tests/missing_data/test_missing_data_ozone_Interpolate_None.py > logs/missing_data_test_missing_data_ozone_Interpolate_None.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_None.log logs/missing_data_test_missing_data_ozone_Interpolate_None.log > logs/missing_data_test_missing_data_ozone_Interpolate_None.log.diff tail -10 logs/missing_data_test_missing_data_ozone_Interpolate_None.log.diff +missing_data/test_missing_data_ozone_Interpolate_PreviousValue.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_Interpolate_PreviousValue.py > logs/missing_data_test_missing_data_ozone_Interpolate_PreviousValue.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_Interpolate_PreviousValue.log logs/missing_data_test_missing_data_ozone_Interpolate_PreviousValue.log > logs/missing_data_test_missing_data_ozone_Interpolate_PreviousValue.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_Interpolate_PreviousValue.log.diff + +missing_data/test_missing_data_ozone_None_Constant.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_None_Constant.py > logs/missing_data_test_missing_data_ozone_None_Constant.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_Constant.log logs/missing_data_test_missing_data_ozone_None_Constant.log > logs/missing_data_test_missing_data_ozone_None_Constant.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_None_Constant.log.diff + missing_data/test_missing_data_ozone_None_DiscardRow.py : $(PYTHON) tests/missing_data/test_missing_data_ozone_None_DiscardRow.py > logs/missing_data_test_missing_data_ozone_None_DiscardRow.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_DiscardRow.log logs/missing_data_test_missing_data_ozone_None_DiscardRow.log > logs/missing_data_test_missing_data_ozone_None_DiscardRow.log.diff @@ -991,11 +1096,26 @@ missing_data/test_missing_data_ozone_None_Interpolate.py : $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_Interpolate.log logs/missing_data_test_missing_data_ozone_None_Interpolate.log > logs/missing_data_test_missing_data_ozone_None_Interpolate.log.diff tail -10 logs/missing_data_test_missing_data_ozone_None_Interpolate.log.diff +missing_data/test_missing_data_ozone_None_Mean.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_None_Mean.py > logs/missing_data_test_missing_data_ozone_None_Mean.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_Mean.log logs/missing_data_test_missing_data_ozone_None_Mean.log > logs/missing_data_test_missing_data_ozone_None_Mean.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_None_Mean.log.diff + +missing_data/test_missing_data_ozone_None_Median.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_None_Median.py > logs/missing_data_test_missing_data_ozone_None_Median.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_Median.log logs/missing_data_test_missing_data_ozone_None_Median.log > logs/missing_data_test_missing_data_ozone_None_Median.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_None_Median.log.diff + missing_data/test_missing_data_ozone_None_None.py : $(PYTHON) tests/missing_data/test_missing_data_ozone_None_None.py > logs/missing_data_test_missing_data_ozone_None_None.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_None.log logs/missing_data_test_missing_data_ozone_None_None.log > logs/missing_data_test_missing_data_ozone_None_None.log.diff tail -10 logs/missing_data_test_missing_data_ozone_None_None.log.diff +missing_data/test_missing_data_ozone_None_PreviousValue.py : + $(PYTHON) tests/missing_data/test_missing_data_ozone_None_PreviousValue.py > logs/missing_data_test_missing_data_ozone_None_PreviousValue.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_None_PreviousValue.log logs/missing_data_test_missing_data_ozone_None_PreviousValue.log > logs/missing_data_test_missing_data_ozone_None_PreviousValue.log.diff + tail -10 logs/missing_data_test_missing_data_ozone_None_PreviousValue.log.diff + missing_data/test_missing_data_ozone_generic.py : $(PYTHON) tests/missing_data/test_missing_data_ozone_generic.py > logs/missing_data_test_missing_data_ozone_generic.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/missing_data_test_missing_data_ozone_generic.log logs/missing_data_test_missing_data_ozone_generic.log > logs/missing_data_test_missing_data_ozone_generic.log.diff @@ -1003,7 +1123,7 @@ missing_data/test_missing_data_ozone_generic.py : - missing_data : missing_data/test_missing_data_ozone_generic.py missing_data/test_missing_data_ozone_None_None.py missing_data/test_missing_data_ozone_None_Interpolate.py missing_data/test_missing_data_ozone_None_DiscardRow.py missing_data/test_missing_data_ozone_Interpolate_None.py missing_data/test_missing_data_ozone_Interpolate_Interpolate.py missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py missing_data/test_missing_data_ozone_DiscardRow_None.py missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py missing_data/test_missing_data_air_passengers_generic.py missing_data/test_missing_data_air_passengers_None_None.py missing_data/test_missing_data_air_passengers_None_Interpolate.py missing_data/test_missing_data_air_passengers_None_DiscardRow.py missing_data/test_missing_data_air_passengers_Interpolate_None.py missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py missing_data/test_missing_data_air_passengers_DiscardRow_None.py missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py missing_data/gen_ozone_tests.py missing_data/gen_air_passengers_tests.py + missing_data : missing_data/test_missing_data_ozone_generic.py missing_data/test_missing_data_ozone_None_PreviousValue.py missing_data/test_missing_data_ozone_None_None.py missing_data/test_missing_data_ozone_None_Median.py missing_data/test_missing_data_ozone_None_Mean.py missing_data/test_missing_data_ozone_None_Interpolate.py missing_data/test_missing_data_ozone_None_DiscardRow.py missing_data/test_missing_data_ozone_None_Constant.py missing_data/test_missing_data_ozone_Interpolate_PreviousValue.py missing_data/test_missing_data_ozone_Interpolate_None.py missing_data/test_missing_data_ozone_Interpolate_Median.py missing_data/test_missing_data_ozone_Interpolate_Mean.py missing_data/test_missing_data_ozone_Interpolate_Interpolate.py missing_data/test_missing_data_ozone_Interpolate_DiscardRow.py missing_data/test_missing_data_ozone_Interpolate_Constant.py missing_data/test_missing_data_ozone_DiscardRow_PreviousValue.py missing_data/test_missing_data_ozone_DiscardRow_None.py missing_data/test_missing_data_ozone_DiscardRow_Median.py missing_data/test_missing_data_ozone_DiscardRow_Mean.py missing_data/test_missing_data_ozone_DiscardRow_Interpolate.py missing_data/test_missing_data_ozone_DiscardRow_DiscardRow.py missing_data/test_missing_data_ozone_DiscardRow_Constant.py missing_data/test_missing_data_air_passengers_generic.py missing_data/test_missing_data_air_passengers_None_PreviousValue.py missing_data/test_missing_data_air_passengers_None_None.py missing_data/test_missing_data_air_passengers_None_Median.py missing_data/test_missing_data_air_passengers_None_Mean.py missing_data/test_missing_data_air_passengers_None_Interpolate.py missing_data/test_missing_data_air_passengers_None_DiscardRow.py missing_data/test_missing_data_air_passengers_None_Constant.py missing_data/test_missing_data_air_passengers_Interpolate_PreviousValue.py missing_data/test_missing_data_air_passengers_Interpolate_None.py missing_data/test_missing_data_air_passengers_Interpolate_Median.py missing_data/test_missing_data_air_passengers_Interpolate_Mean.py missing_data/test_missing_data_air_passengers_Interpolate_Interpolate.py missing_data/test_missing_data_air_passengers_Interpolate_DiscardRow.py missing_data/test_missing_data_air_passengers_Interpolate_Constant.py missing_data/test_missing_data_air_passengers_DiscardRow_PreviousValue.py missing_data/test_missing_data_air_passengers_DiscardRow_None.py missing_data/test_missing_data_air_passengers_DiscardRow_Median.py missing_data/test_missing_data_air_passengers_DiscardRow_Mean.py missing_data/test_missing_data_air_passengers_DiscardRow_Interpolate.py missing_data/test_missing_data_air_passengers_DiscardRow_DiscardRow.py missing_data/test_missing_data_air_passengers_DiscardRow_Constant.py missing_data/gen_ozone_tests.py missing_data/gen_air_passengers_tests.py model_control/test_ozone_all_models_enabled.py : From 4d1323ac0368d14b5ac7e2091ae039cfe8f10171 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 18:56:08 +0200 Subject: [PATCH 07/15] Hierarchical models now use one Forecast Engine (parallelization improvement) --- tests/bugs/issue_55/grouping_issue_55_notebook.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/bugs/issue_55/grouping_issue_55_notebook.py b/tests/bugs/issue_55/grouping_issue_55_notebook.py index 2b68e272d..c8638b5c8 100644 --- a/tests/bugs/issue_55/grouping_issue_55_notebook.py +++ b/tests/bugs/issue_55/grouping_issue_55_notebook.py @@ -114,7 +114,7 @@ French_Wine_Export_in_Euros_DF.info() -lInfo = lEngine.to_json() +lInfo = lEngine.to_dict() print(lInfo.keys()) print(lInfo['Structure']) @@ -133,7 +133,7 @@ lEngine.mSignalHierarchy.plot() -CN_Engine = lEngine.mSignalHierarchy.mModels[2]['__CN'] # __CN is at hierarchical level 2 +CN_Engine = lEngine.mSignalHierarchy.mModels # __CN is at hierarchical level 2 CN_Engine.getModelInfo() From c52986d933e7db29c74c724e37a2c70705bd4e78 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 19:46:14 +0200 Subject: [PATCH 08/15] Updated these logs --- ...TimeSeries_HeartRateTimeSeries_series1.log | 221 +- ...TimeSeries_HeartRateTimeSeries_series2.log | 219 +- ...TimeSeries_HeartRateTimeSeries_series3.log | 213 +- ...TimeSeries_HeartRateTimeSeries_series4.log | 215 +- ...rOfWeek_test_Business_Hourly_LunchTime.log | 266 +- ...eek_test_Business_Hourly_MondayMorning.log | 265 +- ...ourOfWeek_test_Business_Hourly_WeekEnd.log | 266 +- ...WeekOfMonth_test_Business_DayOfNthWeek.log | 264 +- .../WeekOfMonth_test_Business_WeekOfMonth.log | 265 +- .../references/basic_checks_test_pearson.log | 4 +- .../bugs_issue_106_insurance_exog.log | 95 +- .../bugs_issue_106_insurance_exog_svrx.log | 77 +- .../bugs_issue_106_insurance_exog_xgbx.log | 75 +- .../bugs_issue_106_submitted_script.log | 29 +- ...ugs_issue_19_issue_19_no_interpolation.log | 15 +- ...ue_21_test_artificial_1024__poly_7__20.log | 125 +- ...bugs_issue_21_test_ozone_max_autoreg_5.log | 155 +- ..._artificial_1024_cumsum_constant_5__20.log | 89 +- tests/references/bugs_issue_29_test_mem_1.log | 136 +- .../bugs_issue_32_bug_test_ozone_heroku_1.log | 106 +- ...gs_issue_32_bug_test_ozone_heroku_1_ws.log | 202 +- tests/references/bugs_issue_34_issue_34.log | 159 +- tests/references/bugs_issue_34_issue_34_1.log | 127 +- ...34_test_artificial_1024__poly_7_12_100.log | 144 +- ...34_test_artificial_1024_diff_poly_0__0.log | 65 +- ..._test_artificial_1024_diff_poly_0__100.log | 65 +- ...st_artificial_1024_inv_constant_30__20.log | 139 +- ...st_artificial_1024_inv_linear_7_12_100.log | 125 +- ...st_artificial_1024_log_linear_30_12_20.log | 166 +- ...st_artificial_1024_log_linear_5_12_100.log | 159 +- ..._test_artificial_1024_sqr_poly_5_12_20.log | 153 +- ...artificial_1024_sqrt_constant_7_12_100.log | 125 +- ...t_artificial_1024_sqrt_linear_30_12_20.log | 166 +- .../bugs_issue_36_display_version_info.log | 2 +- .../bugs_issue_36_issue_36_version_info.log | 47 +- tests/references/bugs_issue_4_issue_4.log | 15 +- ...gs_issue_55_grouping_issue_55_notebook.log | 1977 ++- .../bugs_issue_56_issue_56_order1.log | 312 +- .../bugs_issue_56_issue_56_order2.log | 312 +- ...ue_58_issue_58_1_categorical_exogenous.log | 326 +- ...sue_69_issue_69_example_1_no_rectifier.log | 61 +- ...e_69_issue_69_example_1_with_rectifier.log | 59 +- ...0_test_artificial_filter_seasonals_day.log | 383 +- ..._test_artificial_filter_seasonals_hour.log | 315 +- ...0_test_artificial_filter_seasonals_min.log | 885 +- ...est_artificial_filter_seasonals_second.log | 759 +- ...test_artificial_keep_all_seasonals_day.log | 541 +- ...est_artificial_keep_all_seasonals_hour.log | 845 +- ...t_artificial_keep_all_seasonals_minute.log | 1011 +- ...t_artificial_keep_all_seasonals_second.log | 759 +- ...s_issue_70_test_ozone_filter_seasonals.log | 272 +- ...issue_70_test_ozone_keep_all_seasonals.log | 272 +- ...2_test_artificial_32_exp_linear_0__100.log | 69 +- ..._72_test_artificial_32_sqrt_poly_7__20.log | 87 +- .../bugs_issue_73_issue_73_1_fast_mode.log | 35 +- .../bugs_issue_73_issue_73_1_fast_mode_2.log | 41 +- .../bugs_issue_75_issue_75_plot.log | 55 +- 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.../references/hierarchical_test_grouped.log | 86 +- ...chical_test_grouped_signals_AllMethods.log | 283 +- ...ical_test_grouped_signals_AllMethods_2.log | 452 +- ...signals_AllMethods_Exogenous_all_nodes.log | 834 +- ..._signals_AllMethods_Exogenous_per_node.log | 830 +- .../hierarchical_test_grouped_signals_BU.log | 86 +- .../hierarchical_test_grouped_signals_MO.log | 82 +- .../hierarchical_test_grouped_signals_OC.log | 86 +- .../hierarchical_test_grouped_signals_TD.log | 106 +- .../hierarchical_test_hierarchy_AU.log | 136 +- ...rarchical_test_hierarchy_AU_AllMethods.log | 739 +- ...rchy_AU_AllMethods_Exogenous_all_nodes.log | 438 +- ...archy_AU_AllMethods_Exogenous_per_node.log | 404 +- .../hierarchical_test_hierarchy_AU_BU.log | 136 +- .../hierarchical_test_hierarchy_AU_MO.log | 132 +- .../hierarchical_test_hierarchy_AU_OC.log | 152 +- .../hierarchical_test_hierarchy_AU_TD.log | 200 +- ..._control_test_ozone_all_models_enabled.log | 204 +- ...l_control_test_ozone_no_models_enabled.log | 80 +- ...uralnet_test_air_passengers_CPU_theano.log | 377 +- ...net_test_air_passengers_GPU_tensorflow.log | 473 +- ...uralnet_test_air_passengers_GPU_theano.log | 149 +- ...neuralnet_test_air_passengers_rnn_only.log | 147 +- ...uralnet_test_air_passengers_tensorflow.log | 163 +- .../neuralnet_test_ozone__CPU_theano.log | 786 +- .../neuralnet_test_ozone__GPU_tensorflow.log | 643 +- .../neuralnet_test_ozone__GPU_theano.log | 470 +- .../neuralnet_test_ozone_rnn_only.log | 236 +- .../neuralnet_test_ozone_rnn_only_LSTM.log | 242 +- .../neuralnet_test_ozone_rnn_only_MLP.log | 242 +- .../neuralnet_test_ozone_tensorflow.log | 56 +- .../references/perf_test_cycles_full_long.log | 800 +- .../perf_test_cycles_full_long_long.log | 782 +- .../perf_test_cycles_full_long_long_2.log | 2015 +-- tests/references/perf_test_ozone_ar_speed.log | 1612 ++- .../perf_test_ozone_ar_speed_many.log | 724 +- .../references/perf_test_ozone_debug_perf.log | 360 +- 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...emporal_hierarchy_test_temporal_demo_1.log | 281 +- ...rarchy_test_temporal_demo_daily_D_W_2W.log | 283 +- ...rchy_test_temporal_demo_daily_D_W_2W_Q.log | 368 +- ...erarchy_test_temporal_demo_daily_D_W_M.log | 279 +- ...archy_test_temporal_demo_daily_D_W_M_Q.log | 362 +- ...erarchy_test_temporal_demo_daily_D_W_Q.log | 283 +- ...y_test_temporal_demo_hourly_H_6H_12H_D.log | 362 +- ...test_temporal_demo_hourly_H_6H_12H_D_W.log | 467 +- ...ierarchy_test_temporal_demo_hourly_H_D.log | 216 +- ...est_temporal_demo_minutely_T_10T_30T_H.log | 360 +- ...rarchy_test_temporal_demo_minutely_T_H.log | 208 +- ..._test_temporal_demo_minutely_T_H_12H_D.log | 312 +- ...chy_test_temporal_demo_monthly_M_2M_6M.log | 313 +- ...test_temporal_demo_monthly_M_2M_6M_12M.log | 372 +- ...archy_test_temporal_demo_monthly_M_Q_A.log | 251 +- ...chy_test_temporal_demo_weekly_W_2W_M_Q.log | 338 +- ...rarchy_test_temporal_demo_weekly_W_Q_A.log | 237 +- .../references/time_res_test_ozone_Daily.log | 112 +- 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tests/references/xgb_test_ozone_xgb.log | 90 +- .../xgb_test_ozone_xgb_exogenous.log | 68 +- tests/references/xgb_test_ozone_xgb_only.log | 123 +- .../xgb_test_ozone_xgbx_exogenous.log | 68 +- 209 files changed, 37919 insertions(+), 31453 deletions(-) diff --git a/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series1.log b/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series1.log index 15d66947d..7a55ab00d 100644 --- a/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series1.log +++ b/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series1.log @@ -5,71 +5,135 @@ INFO:pyaf.std:START_TRAINING 'HeartRate' 2 85.6542 2 3 87.2093 3 4 87.1246 4 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_AR 72 0.0097 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_NoAR 8 0.0575 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.0068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.1124 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.1124 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.1248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.1248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0943 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0268 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.0399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0675 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0675 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.1263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0861 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.1263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.0861 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.4663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1968 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.4663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.1968 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 10579028.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 272.0813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 10579028.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 272.0813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 10579028.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 10579028.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 10579028.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 277.1075 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 10579028.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 277.1075 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 10579028.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 2235.1735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 10579028.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 2235.1735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0055 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0055 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0055 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0084 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.1068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1269 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.1269 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_None_AR 72 0.0096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.0576 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_NoCycle_AR 64 0.0096 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_NoCycle_NoAR 0 0.0576 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_AR 104 0.0096 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_NoAR 40 0.0105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_None_AR 104 0.0096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_None_NoAR 40 0.0105 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_NoCycle_AR 96 0.0096 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_NoCycle_NoAR 32 0.0105 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_AR 88 0.0098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_NoAR 24 0.0604 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_None_AR 88 0.0097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_None_NoAR 24 0.0605 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_NoCycle_AR 80 0.0097 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_NoCycle_NoAR 16 0.0605 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_AR 88 0.0098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_NoAR 24 0.0616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_None_AR 88 0.0098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_None_NoAR 24 0.0616 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_NoCycle_AR 80 0.0098 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_NoCycle_NoAR 16 0.0616 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_AR 104 0.0761 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_NoAR 40 0.0638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_AR 104 0.0886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_NoAR 40 0.0587 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_NoCycle_AR 96 0.0889 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_NoCycle_NoAR 32 0.0633 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_AR 136 0.0361 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_NoAR 72 0.011 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_None_AR 136 0.067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_None_NoAR 72 0.0105 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_NoCycle_AR 128 0.067 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_NoCycle_NoAR 64 0.0105 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_AR 120 0.0741 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_NoAR 56 0.0577 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_None_AR 120 0.0922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_None_NoAR 56 0.0575 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_NoCycle_AR 112 0.0922 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_NoCycle_NoAR 48 0.0575 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_AR 120 0.2158 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_NoAR 56 0.1498 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_None_AR 120 0.2382 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_None_NoAR 56 0.1484 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_NoCycle_AR 112 0.2382 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_NoCycle_NoAR 48 0.1484 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_AR 104 2464637.6532 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_NoAR 40 101.3328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_None_AR 104 2464657.5642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 149.2912 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_NoCycle_AR 96 2464657.5642 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_NoCycle_NoAR 32 149.2912 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_AR 136 2430937.6133 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_NoAR 72 4718.3743 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_None_AR 136 2433592.4112 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.0105 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_NoCycle_AR 128 2433592.4112 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.0105 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_AR 120 2464638.0153 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_NoAR 56 102.4253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_None_AR 120 2464658.466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_None_NoAR 56 150.9135 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_NoCycle_AR 112 2464658.466 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_NoCycle_NoAR 48 150.9135 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_AR 120 2464717.4721 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_NoAR 56 362.9838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_None_AR 120 2464943.4154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_None_NoAR 56 539.5064 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_NoCycle_AR 112 2464943.4154 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_NoCycle_NoAR 48 539.5064 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_AR 104 0.0238 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_NoAR 40 1.0517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_None_AR 104 0.0105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_None_NoAR 40 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_NoCycle_AR 96 0.0105 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_AR 136 0.0118 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_NoAR 72 0.011 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_AR 136 0.0105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_NoAR 72 0.0107 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_NoCycle_AR 128 0.0105 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_NoCycle_NoAR 64 0.0105 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_AR 120 0.0144 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_NoAR 56 0.0617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_AR 120 0.014 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_NoAR 56 0.0674 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_NoCycle_AR 112 0.0118 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_NoCycle_NoAR 48 0.0596 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_AR 120 0.014 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_NoAR 56 0.0704 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_None_AR 120 0.0119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_None_NoAR 56 0.0582 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_NoCycle_AR 112 0.0119 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_NoCycle_NoAR 48 0.0582 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'HeartRate' 6.3350982666015625 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['HeartRate']' 4.116448402404785 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=0 TimeMax=1430 TimeDelta=1 Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='HeartRate' Length=1799 Min=73.4366 Max=106.756 Mean=92.60074274596998 StdDev=5.485825559762327 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_HeartRate' Min=73.4366 Max=106.756 Mean=92.60074274596998 StdDev=5.485825559762327 @@ -84,35 +148,36 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9993 MASE_Forecast=0.9984 MASE_Test=0.9989 INFO:pyaf.std:MODEL_L1 L1_Fit=0.8835403214535288 L1_Forecast=0.958224301675978 L1_Test=0.6976299999999995 INFO:pyaf.std:MODEL_L2 L2_Fit=1.3418342432060646 L2_Forecast=1.6599375026853351 L2_Test=1.0107466354136452 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 84.2697 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _HeartRate_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 44.36135244369507 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.8727390766143799 - Split Transformation ... TestMAPE TestMASE -8 None _HeartRate ... 0.0069 0.9989 -9 None _HeartRate ... 0.0072 1.0459 -0 None _HeartRate ... 0.0068 0.9958 -12 None RelDiff_HeartRate ... 0.0069 0.9989 -11 None Diff_HeartRate ... 0.0069 0.9989 - -[5 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 29.566500425338745 +INFO:pyaf.std:START_FORECASTING '['HeartRate']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['HeartRate']' 1.2864162921905518 Split Transformation ... TestMAPE TestMASE 0 None _HeartRate ... 0.0068 0.9958 -1 None _HeartRate ... 0.0069 1.0144 -2 None _HeartRate ... 0.0065 0.9555 -3 None _HeartRate ... 0.0066 0.9784 +1 None _HeartRate ... 0.0068 0.9958 +2 None _HeartRate ... 0.0069 1.0144 +3 None _HeartRate ... 0.0069 1.0144 4 None _HeartRate ... 0.0069 1.0197 [5 rows x 20 columns] @@ -169,31 +234,33 @@ Forecasts { - "Dataset": { - "Signal": "HeartRate", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "0", - "1798" - ], - "TimeVariable": "Date" + "HeartRate": { + "Dataset": { + "Signal": "HeartRate", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "0", + "1798" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1799 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 1799 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "0.958224301675978", - "MAPE": "0.0105", - "MASE": "0.9984", - "RMSE": "1.6599375026853351" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "0.958224301675978", + "MAPE": "0.0105", + "MASE": "0.9984", + "RMSE": "1.6599375026853351" + } } } diff --git a/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series2.log b/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series2.log index 9325907b9..4ca5ef147 100644 --- a/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series2.log +++ b/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series2.log @@ -5,71 +5,135 @@ INFO:pyaf.std:START_TRAINING 'HeartRate' 2 91.8788 2 3 91.1772 3 4 89.7992 4 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_AR 72 0.0093 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_NoAR 8 0.0485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.0032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.0178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0039 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0039 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0085 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.0085 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.052 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.052 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.007 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.007 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.0377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0854 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.1138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.1107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.1073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.1356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 11420491.5945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 11420491.5945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 11420491.5945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 11420491.5945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 11420491.5945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 11420491.7789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 11420491.7814 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 11420491.7789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 11420491.7814 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 11420491.9658 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 11420491.5945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 11420491.5945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 11420491.9658 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.3267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 3.1383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0027 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0043 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0027 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0043 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.0221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0722 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.0722 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_None_AR 72 0.0092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.0486 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_NoCycle_AR 64 0.0092 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_NoCycle_NoAR 0 0.0486 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_AR 104 0.0093 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_NoAR 40 0.0096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_None_AR 104 0.0093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_None_NoAR 40 0.0095 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_NoCycle_AR 96 0.0093 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_NoCycle_NoAR 32 0.0095 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_AR 88 0.0094 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_NoAR 24 0.05 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_None_AR 88 0.0092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_None_NoAR 24 0.0502 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_NoCycle_AR 80 0.0092 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_NoCycle_NoAR 16 0.0502 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_AR 88 0.0094 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_NoAR 24 0.0478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_None_AR 88 0.0092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_None_NoAR 24 0.048 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_NoCycle_AR 80 0.0092 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_NoCycle_NoAR 16 0.048 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_AR 104 0.0318 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_NoAR 40 0.051 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_None_AR 104 0.0311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_None_NoAR 40 0.0507 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_NoCycle_AR 96 0.0311 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_NoCycle_NoAR 32 0.0507 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_AR 136 0.0279 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_NoAR 72 0.0126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_None_AR 136 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_None_NoAR 72 0.0095 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_NoCycle_AR 128 0.0258 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_NoCycle_NoAR 64 0.0095 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_AR 120 0.0596 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_NoAR 56 0.0633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_AR 120 0.0568 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_NoAR 56 0.1313 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_NoCycle_AR 112 0.056 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_NoCycle_NoAR 48 0.0622 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_AR 120 0.0672 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_NoAR 56 0.0684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_AR 120 0.0656 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_NoAR 56 0.1638 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_NoCycle_AR 112 0.0636 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_NoCycle_NoAR 48 0.0671 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_AR 104 11735869.5938 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_NoAR 40 11735869.5938 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_NoAR 40 0.2465 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_NoCycle_AR 96 11735869.5938 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_NoCycle_NoAR 32 11735869.5938 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_AR 136 11735869.7899 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_NoAR 72 11735869.7576 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_None_AR 136 11735869.5938 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.0095 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_NoCycle_AR 128 11735869.5938 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.0095 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_AR 120 11735869.7899 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_NoAR 56 11735869.724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_None_AR 120 11735869.757 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_None_NoAR 56 11735869.7569 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_NoCycle_AR 112 11735869.757 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_NoCycle_NoAR 48 11735869.7569 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_AR 120 11735869.5938 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_NoAR 56 11735869.9201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_AR 120 11735869.9201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_NoAR 56 11735869.5938 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_NoCycle_AR 112 11735869.5938 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_NoCycle_NoAR 48 11735869.9201 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_AR 104 0.0249 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_NoAR 40 1.0548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_AR 104 0.2876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_NoAR 40 1.0968 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_NoCycle_AR 96 0.0123 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_AR 136 0.0129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_AR 136 0.0125 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_NoAR 72 0.0097 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_NoCycle_AR 128 0.0123 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_NoCycle_NoAR 64 0.0095 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_AR 120 0.0139 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_NoAR 56 0.0488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_AR 120 0.0143 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_NoAR 56 0.0499 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_NoCycle_AR 112 0.0133 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_NoCycle_NoAR 48 0.0485 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_AR 120 0.014 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_NoAR 56 0.0534 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_None_AR 120 0.0135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_None_NoAR 56 0.0464 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_NoCycle_AR 112 0.0135 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_NoCycle_NoAR 48 0.0464 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'HeartRate' 6.048134088516235 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['HeartRate']' 4.126339912414551 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=0 TimeMax=1430 TimeDelta=1 Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='HeartRate' Length=1799 Min=80.2139 Max=104.895 Mean=96.64034991662034 StdDev=5.685869427849087 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_HeartRate' Min=80.2139 Max=104.895 Mean=96.64034991662034 StdDev=5.685869427849087 @@ -84,36 +148,37 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9993 MASE_Forecast=0.9972 MASE_Test=0.9857 INFO:pyaf.std:MODEL_L1 L1_Fit=0.5384650593990219 L1_Forecast=0.8758167597765364 L1_Test=0.2968999999999994 INFO:pyaf.std:MODEL_L2 L2_Fit=0.8803405025558304 L2_Forecast=1.4059289823287502 L2_Test=0.3958160027083285 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 91.4634 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _HeartRate_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 46.734652280807495 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.3438618183135986 - Split Transformation ... TestMAPE TestMASE -8 None _HeartRate ... 0.0030 0.9857 -12 None _HeartRate ... 0.0031 1.0022 -0 None _HeartRate ... 0.0032 1.0408 -11 None RelDiff_HeartRate ... 0.0030 0.9857 -9 None CumSum_HeartRate ... 0.0030 0.9857 - -[5 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 14.83701491355896 +INFO:pyaf.std:START_FORECASTING '['HeartRate']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['HeartRate']' 0.4978148937225342 Split Transformation ... TestMAPE TestMASE 0 None _HeartRate ... 0.0032 1.0408 -1 None _HeartRate ... 0.0030 0.9829 -2 None _HeartRate ... 0.0036 1.1792 -3 None _HeartRate ... 0.0030 0.9810 -4 None _HeartRate ... 0.0039 1.2645 +1 None _HeartRate ... 0.0032 1.0408 +2 None _HeartRate ... 0.0030 0.9829 +3 None _HeartRate ... 0.0036 1.1792 +4 None _HeartRate ... 0.0030 0.9829 [5 rows x 20 columns] Forecast Columns Index(['Date', 'HeartRate', 'row_number', 'Date_Normalized', '_HeartRate', @@ -169,31 +234,33 @@ Forecasts { - "Dataset": { - "Signal": "HeartRate", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "0", - "1798" - ], - "TimeVariable": "Date" + "HeartRate": { + "Dataset": { + "Signal": "HeartRate", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "0", + "1798" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1799 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 1799 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "0.8758167597765364", - "MAPE": "0.0095", - "MASE": "0.9972", - "RMSE": "1.4059289823287502" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "0.8758167597765364", + "MAPE": "0.0095", + "MASE": "0.9972", + "RMSE": "1.4059289823287502" + } } } diff --git a/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series3.log b/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series3.log index ce7959d91..dbe110ae9 100644 --- a/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series3.log +++ b/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series3.log @@ -5,44 +5,108 @@ INFO:pyaf.std:START_TRAINING 'HeartRate' 2 60.3391 2 3 60.0762 3 4 59.2526 4 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_AR 72 0.0131 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_NoAR 8 0.0451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.0104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.0131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.054 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.054 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.0841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.1809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.1809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.0876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.2964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.2964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.1405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.4015 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1921 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.4015 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.1921 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 15313545.8817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 15313545.8817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 15313545.7805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0109 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0109 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.0499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.1089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_None_AR 72 0.0128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.0449 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_NoCycle_AR 64 0.0128 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_NoCycle_NoAR 0 0.0449 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_AR 104 0.0129 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_NoAR 40 0.0136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_None_AR 104 0.013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_None_NoAR 40 0.0135 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_NoCycle_AR 96 0.013 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_NoCycle_NoAR 32 0.0135 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_AR 88 0.013 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_NoAR 24 0.0439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_None_AR 88 0.0128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_None_NoAR 24 0.0439 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_NoCycle_AR 80 0.0128 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_NoCycle_NoAR 16 0.0439 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_AR 88 0.0132 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_NoAR 24 0.0485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_None_AR 88 0.0129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_None_NoAR 24 0.0482 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_NoCycle_AR 80 0.0129 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_NoCycle_NoAR 16 0.0482 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_AR 104 0.0748 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_NoAR 40 0.0492 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_None_AR 104 0.0741 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_None_NoAR 40 0.0486 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_NoCycle_AR 96 0.0741 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_NoCycle_NoAR 32 0.0486 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_AR 136 0.0314 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_NoAR 72 0.015 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_None_AR 136 0.04 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_None_NoAR 72 0.0135 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_NoCycle_AR 128 0.04 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_NoCycle_NoAR 64 0.0135 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_AR 120 0.1108 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_NoAR 56 0.0649 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_None_AR 120 0.11 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_None_NoAR 56 0.0639 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_NoCycle_AR 112 0.11 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_NoCycle_NoAR 48 0.0639 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_AR 120 0.1414 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_NoAR 56 0.0809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_None_AR 120 0.1409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_None_NoAR 56 0.0796 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_NoCycle_AR 112 0.1409 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_NoCycle_NoAR 48 0.0796 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_AR 104 14294443.2653 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_NoAR 40 14294443.2653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_NoAR 40 0.1137 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_NoCycle_AR 96 14294443.2653 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_NoCycle_NoAR 32 14294443.2653 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_AR 136 14294443.3562 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_NoAR 72 14294443.3789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_None_AR 136 14294443.2653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.0135 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_NoCycle_AR 128 14294443.2653 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.0135 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_AR 120 14294443.2653 @@ -53,23 +117,23 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDi INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_NoAR 56 14294443.2653 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_NoCycle_AR 112 14294443.4928 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_NoCycle_NoAR 48 14294443.2653 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_AR 104 0.0278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_NoAR 40 1.0386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_None_AR 104 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_None_NoAR 40 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_NoCycle_AR 96 0.012 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_AR 136 0.0131 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_NoAR 72 0.0141 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_AR 136 0.0128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_NoAR 72 0.0137 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_NoCycle_AR 128 0.012 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_NoCycle_NoAR 64 0.0135 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_AR 120 0.0185 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_NoAR 56 0.0594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_None_AR 120 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_None_NoAR 56 0.0455 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_NoCycle_AR 112 0.017 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_NoCycle_NoAR 48 0.0455 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_AR 120 0.0173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_NoAR 56 0.1237 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_None_AR 120 0.0152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_None_NoAR 56 0.0659 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_NoCycle_AR 112 0.0152 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_NoCycle_NoAR 48 0.0659 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'HeartRate' 6.415632009506226 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['HeartRate']' 3.936952590942383 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=0 TimeMax=750 TimeDelta=1 Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='HeartRate' Length=949 Min=52.0833 Max=75.4733 Mean=58.671323287671235 StdDev=3.4097331733805167 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_HeartRate' Min=52.0833 Max=75.4733 Mean=58.671323287671235 StdDev=3.4097331733805167 @@ -84,36 +148,37 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9987 MASE_Forecast=1.0016 MASE_Test=0.9782 INFO:pyaf.std:MODEL_L1 L1_Fit=0.8621768308921437 L1_Forecast=0.8097771276595744 L1_Test=0.7280000000000009 INFO:pyaf.std:MODEL_L2 L2_Fit=1.1877862791793217 L2_Forecast=1.2159276200856834 L2_Test=0.8490812034193204 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 60.4839 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _HeartRate_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 51.371049880981445 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.49747896194458 - Split Transformation ... TestMAPE TestMASE -11 None _HeartRate ... 0.0131 0.9782 -15 None _HeartRate ... 0.0149 1.1087 -2 None _HeartRate ... 0.0104 0.7713 -14 None RelDiff_HeartRate ... 0.0131 0.9782 -13 None Diff_HeartRate ... 0.0131 0.9782 - -[5 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 14.990610837936401 +INFO:pyaf.std:START_FORECASTING '['HeartRate']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['HeartRate']' 0.31130051612854004 Split Transformation ... TestMAPE TestMASE 0 None CumSum_HeartRate ... 0.0109 0.8136 -1 None CumSum_HeartRate ... 0.0108 0.8078 -2 None _HeartRate ... 0.0104 0.7713 -3 None _HeartRate ... 0.0101 0.7464 -4 None _HeartRate ... 0.0108 0.8009 +1 None CumSum_HeartRate ... 0.0109 0.8136 +2 None CumSum_HeartRate ... 0.0108 0.8078 +3 None _HeartRate ... 0.0104 0.7713 +4 None _HeartRate ... 0.0104 0.7713 [5 rows x 20 columns] Forecast Columns Index(['Date', 'HeartRate', 'row_number', 'Date_Normalized', '_HeartRate', @@ -169,31 +234,33 @@ Forecasts { - "Dataset": { - "Signal": "HeartRate", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "0", - "948" - ], - "TimeVariable": "Date" + "HeartRate": { + "Dataset": { + "Signal": "HeartRate", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "0", + "948" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 949 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 949 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "0.8097771276595744", - "MAPE": "0.0135", - "MASE": "1.0016", - "RMSE": "1.2159276200856834" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "0.8097771276595744", + "MAPE": "0.0135", + "MASE": "1.0016", + "RMSE": "1.2159276200856834" + } } } diff --git a/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series4.log b/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series4.log index 72138fc7f..6868ba265 100644 --- a/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series4.log +++ b/tests/references/HeartRateTimeSeries_HeartRateTimeSeries_series4.log @@ -5,71 +5,135 @@ INFO:pyaf.std:START_TRAINING 'HeartRate' 2 58.8973 2 3 58.4359 3 4 58.7312 4 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_AR 72 0.0152 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_NoAR 8 0.0465 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.0183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0508 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.0508 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0196 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0196 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.0226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.0479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.0493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0905 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0512 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0905 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.0512 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0391 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0391 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0614 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0614 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.0495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.2756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.2756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.1133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 10600536.7676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.3412 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 10600536.7676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 10600536.7676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 10600536.7676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 10600536.7676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 10600537.0073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10600536.7676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 10600536.7676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 10600536.7676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 10600536.8845 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10600536.8845 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 10600536.8845 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 10600536.8845 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0237 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0149 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0591 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.0547 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.0557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_None_AR 72 0.015 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.0468 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_NoCycle_AR 64 0.015 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_ConstantTrend_NoCycle_NoAR 0 0.0468 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_AR 104 0.0156 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_NoAR 40 0.0159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_None_AR 104 0.0152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_Cycle_None_NoAR 40 0.0157 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_NoCycle_AR 96 0.0152 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_Lag1Trend_NoCycle_NoAR 32 0.0157 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_AR 88 0.0156 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_NoAR 24 0.0466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_None_AR 88 0.0155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_Cycle_None_NoAR 24 0.047 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_NoCycle_AR 80 0.0155 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_LinearTrend_NoCycle_NoAR 16 0.047 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_AR 88 0.0162 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_NoAR 24 0.0486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_None_AR 88 0.0161 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_Cycle_None_NoAR 24 0.049 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_NoCycle_AR 80 0.0161 INFO:pyaf.std:collectPerformanceIndices : MAPE None _HeartRate NoTransf_PolyTrend_NoCycle_NoAR 16 0.049 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_AR 104 0.0627 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_NoAR 40 0.0474 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_None_AR 104 0.0634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_Cycle_None_NoAR 40 0.047 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_NoCycle_AR 96 0.0634 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_ConstantTrend_NoCycle_NoAR 32 0.047 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_AR 136 0.0727 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_NoAR 72 0.0203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_None_AR 136 0.0523 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_Cycle_None_NoAR 72 0.0157 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_NoCycle_AR 128 0.0523 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_Lag1Trend_NoCycle_NoAR 64 0.0157 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_AR 120 0.0606 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_NoAR 56 0.0467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_None_AR 120 0.0622 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_Cycle_None_NoAR 56 0.0463 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_NoCycle_AR 112 0.0622 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_LinearTrend_NoCycle_NoAR 48 0.0463 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_AR 120 0.0873 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_NoAR 56 0.0607 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_None_AR 120 0.0847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_Cycle_None_NoAR 56 0.06 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_NoCycle_AR 112 0.0847 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_HeartRate Difference_PolyTrend_NoCycle_NoAR 48 0.06 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_AR 104 10764952.5545 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_NoAR 40 10764952.5545 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_Cycle_NoAR 40 0.2955 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_NoCycle_AR 96 10764952.5545 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_ConstantTrend_NoCycle_NoAR 32 10764952.5545 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_AR 136 10764952.6394 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_NoAR 72 10764952.6662 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_None_AR 136 10764952.5545 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.0157 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_NoCycle_AR 128 10764952.5545 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.0157 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_AR 120 10764952.7669 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_NoAR 56 10764952.7669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_Cycle_NoAR 56 10764952.5545 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_NoCycle_AR 112 10764952.5545 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_LinearTrend_NoCycle_NoAR 48 10764952.5545 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_AR 120 10764952.6499 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_NoAR 56 10764952.6703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_None_AR 120 10764952.6609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_Cycle_None_NoAR 56 10764952.6609 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_NoCycle_AR 112 10764952.6609 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_HeartRate RelativeDifference_PolyTrend_NoCycle_NoAR 48 10764952.6609 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_AR 104 0.0421 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_NoAR 40 1.0285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_AR 104 0.0355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_Cycle_NoAR 40 1.035 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_NoCycle_AR 96 0.0138 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_AR 136 0.0165 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_NoAR 72 0.0169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_AR 136 0.0142 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_Cycle_NoAR 72 0.0158 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_NoCycle_AR 128 0.0139 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_Lag1Trend_NoCycle_NoAR 64 0.0157 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_AR 120 0.0231 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_NoAR 56 0.0543 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_AR 120 0.0217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_Cycle_NoAR 56 0.0539 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_NoCycle_AR 112 0.0188 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_LinearTrend_NoCycle_NoAR 48 0.0481 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_AR 120 0.0241 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_NoAR 56 0.1256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_None_AR 120 0.018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_Cycle_None_NoAR 56 0.0742 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_NoCycle_AR 112 0.018 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_HeartRate Integration_PolyTrend_NoCycle_NoAR 48 0.0742 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'HeartRate' 6.316509008407593 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['HeartRate']' 4.118569612503052 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=0 TimeMax=750 TimeDelta=1 Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='HeartRate' Length=949 Min=52.9153 Max=77.4244 Mean=58.726979978925186 StdDev=3.5400112568120097 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_HeartRate' Min=52.9153 Max=77.4244 Mean=58.726979978925186 StdDev=3.5400112568120097 @@ -84,36 +148,37 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9987 MASE_Forecast=0.9978 MASE_Test=0.9692 INFO:pyaf.std:MODEL_L1 L1_Fit=0.7362363515312917 L1_Forecast=0.9636781914893616 L1_Test=1.37391 INFO:pyaf.std:MODEL_L2 L2_Fit=1.3136394202284594 L2_Forecast=1.779634744484551 L2_Test=1.6193514217117915 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 59.2885 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _HeartRate_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 41.70826506614685 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.879612922668457 - Split Transformation ... TestMAPE TestMASE -8 None _HeartRate ... 0.0226 0.9692 -12 None _HeartRate ... 0.0241 1.0290 -10 None Diff_HeartRate ... 0.0226 0.9692 -2 None _HeartRate ... 0.0183 0.7930 -9 None CumSum_HeartRate ... 0.0226 0.9692 - -[5 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 14.92343020439148 +INFO:pyaf.std:START_FORECASTING '['HeartRate']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['HeartRate']' 0.26796436309814453 Split Transformation ... TestMAPE TestMASE 0 None CumSum_HeartRate ... 0.0149 0.6384 1 None CumSum_HeartRate ... 0.0152 0.6496 -2 None _HeartRate ... 0.0183 0.7930 -3 None _HeartRate ... 0.0186 0.8044 -4 None _HeartRate ... 0.0196 0.8393 +2 None CumSum_HeartRate ... 0.0158 0.6751 +3 None _HeartRate ... 0.0183 0.7930 +4 None _HeartRate ... 0.0183 0.7930 [5 rows x 20 columns] Forecast Columns Index(['Date', 'HeartRate', 'row_number', 'Date_Normalized', '_HeartRate', @@ -169,31 +234,33 @@ Forecasts { - "Dataset": { - "Signal": "HeartRate", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "0", - "948" - ], - "TimeVariable": "Date" + "HeartRate": { + "Dataset": { + "Signal": "HeartRate", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "0", + "948" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 949 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 949 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_HeartRate_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "0.9636781914893616", - "MAPE": "0.0157", - "MASE": "0.9978", - "RMSE": "1.779634744484551" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "0.9636781914893616", + "MAPE": "0.0157", + "MASE": "0.9978", + "RMSE": "1.779634744484551" + } } } diff --git a/tests/references/HourOfWeek_test_Business_Hourly_LunchTime.log b/tests/references/HourOfWeek_test_Business_Hourly_LunchTime.log index fda84af6e..66c2984f9 100644 --- a/tests/references/HourOfWeek_test_Business_Hourly_LunchTime.log +++ b/tests/references/HourOfWeek_test_Business_Hourly_LunchTime.log @@ -17,6 +17,198 @@ Data columns (total 4 columns): dtypes: datetime64[ns](1), float64(1), int64(2) memory usage: 312.6 KB None +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 4 0.062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 49.9573 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 96.8834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 36 0.151 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 0.1502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.1498 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 0.15 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 0.1496 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 36 0.0531 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 36 0.1681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 0.15 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 0.146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 0.1502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 0.1479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 0.1507 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 36 0.1509 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.1505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 49.8398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.2819 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.3041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 0.3083 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 0.293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 0.2415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 20 49.517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 0.3034 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 0.26 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 0.3025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 0.2466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 49.7246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 49.5783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 49.6964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 96.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 50.8351 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 1.2586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 1.1931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 1.1906 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 1.1664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 1.119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 20 50.9086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 1.1896 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 1.1401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 1.1253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 51.0799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 50.7529 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 51.0524 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 97.903 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 36 261.4156 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 268.9567 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 267.2338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 293.1808 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 3928.3447 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR 36 13.3055 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 7165.6187 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 316.5359 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 5727.4072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 267.771 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 34.7062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 265.125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 256.6422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 30.3549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 30.3549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 10.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 10.4316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 22.8013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 14.9484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 3610.6969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 1.0519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 11.3674 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 10.8893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 49438.9792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 21.9093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 44.4032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 11.7374 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 23.1082 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 285.4444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 289.2118 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 289.8118 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 308.0495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 3953.2083 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 7.8212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 7193.5188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 335.7803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 5752.514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 295.5399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 56.8927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 286.0831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 289.0403 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 7.507 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 7.507 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 136.6842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 158.1355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 138.3238 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 122.5824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 3490.5936 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 433.63 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 6746.1602 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 103.2861 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 5301.4791 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 136.9257 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 386.9922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 159.1829 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 37.4664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 147.7624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 441.7705 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.85 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 360.7045 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 20274609665.3295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 2.4665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 20274609665.3295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 20274609667.0295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 20274609667.0295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 20274609667.0295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 20274609665.3295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 20274609667.0295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 20274609667.0295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 20274609665.3295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 20274609665.3295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 20274609665.3295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 14.8666 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.8941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 20274609666.1825 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 20274609666.0998 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 20274609666.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 20274609666.2591 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 20274609666.2592 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 20274609666.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 20274609666.1824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 0.0941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 96.9243 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 101.3121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 101.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 152.669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 136.9788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 0.1033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 38.5175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 149.2612 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 122.3773 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 102.428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 96.6377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 96.5641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 96.5641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 96.5641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 96.5641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 88.9677 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 90.92 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 91.0864 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 144.4072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 131.9147 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 10.1778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 96.3324 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 142.8042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 117.4379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 92.3756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 96.2833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 86.1972 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 86.1972 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 86.1972 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 86.1972 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_ThreeHourOfWeek_NoAR 4 0.0431 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 20.6693 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 39.8261 @@ -209,7 +401,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Po INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_NoAR 52 57.7024 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_None_NoAR 56 36.9669 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 36.9669 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 12.593913316726685 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 30.664918184280396 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_Hourly' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-11-28T11:00:00.000000 TimeDelta= Horizon=24 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10000 Min=2.0460889218351275 Max=2768.07062049063 Mean=1333.912571463825 StdDev=1188.8059015353733 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=2.0460889218351275 Max=2768.07062049063 Mean=1333.912571463825 StdDev=1188.8059015353733 @@ -235,26 +427,20 @@ INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_Three INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 1.2456092834472656 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 2.1738662719726562 INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 11.065069675445557 - Split Transformation ... TestMAPE TestMASE -2 None _Signal ... 0.0620 0.2509 -0 None _Signal ... 0.0531 0.0364 -1 None CumSum_Signal ... 0.1033 0.3840 - -[3 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.41565704345703 Split Transformation ... TestMAPE TestMASE 0 None _Signal ... 0.0531 0.0364 1 None CumSum_Signal ... 0.1033 0.3840 @@ -316,31 +502,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 24, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-02-20 15:00:00" - ], - "TimeVariable": "Time_Hourly" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 24, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-02-20 15:00:00" + ], + "TimeVariable": "Time_Hourly" + }, + "Training_Signal_Length": 10000 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR", + "Cycle": "Seasonal_ThreeHourOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 10000 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR", - "Cycle": "Seasonal_ThreeHourOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "28.540379810057328", - "MAPE": "0.0431", - "MASE": "0.3185", - "RMSE": "164.6084570896099" + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "28.540379810057328", + "MAPE": "0.0431", + "MASE": "0.3185", + "RMSE": "164.6084570896099" + } } } diff --git a/tests/references/HourOfWeek_test_Business_Hourly_MondayMorning.log b/tests/references/HourOfWeek_test_Business_Hourly_MondayMorning.log index 933660c81..53c0d0cf1 100644 --- a/tests/references/HourOfWeek_test_Business_Hourly_MondayMorning.log +++ b/tests/references/HourOfWeek_test_Business_Hourly_MondayMorning.log @@ -17,6 +17,198 @@ Data columns (total 4 columns): dtypes: datetime64[ns](1), float64(1), int64(2) memory usage: 312.6 KB None +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 4 1.551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 193.7841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 133.665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 36 0.1991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 0.1859 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.1846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 1.0257 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 1.0228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 36 0.0557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 36 21.9975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 1.0331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 1.0162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 1.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 0.5686 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 0.1993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 36 0.1983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 217.3017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 24.3176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 24.3379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 3.823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 1.8134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 0.375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 20 193.4678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 3.0027 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 1.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 5.9728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 217.3287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 217.2389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 193.4678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 133.2819 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 209.367 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 30.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 30.2307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 9.8804 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 7.9131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 8.0073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 20 187.4227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 9.0755 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 7.3846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 12.0386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 7.4325 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 209.2475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 210.1866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 187.4227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 125.8494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 36 533.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 588.1719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 578.1636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 27024.7611 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 27022.6706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR 36 232.4961 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 104.7107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 30448.888 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 30394.7669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 40288.0409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 12870.057 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 537.6946 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 537.8141 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 272.5902 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 272.5902 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 8.7262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 8.8283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 17.8279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 14.8425 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 27.6673 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 1.9869 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 50.3687 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 30.6662 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 17.145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 22.0104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 24.6476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 9.788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 36.3795 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 494.4306 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 544.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 525.5451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 26982.4741 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 26973.2461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 278.8834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 148.6272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 30386.1475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 30345.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 40242.1629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 12822.8894 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 502.0738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 472.859 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 226.1779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 226.1779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 1036.7436 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 1085.5713 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 1103.0307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 27453.9376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 27473.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 207.5327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 335.2989 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 30841.885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 30828.25 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 40691.6397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 13265.8088 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 1039.2983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 809.9686 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 685.79 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 685.79 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 2.3528 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 8.2735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 38460809.2087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 0.838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 0.838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 38460810.8847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 38460810.8847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 38460810.8847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 38460809.2087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 38460810.8847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 0.838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 38460809.2087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 38460809.8034 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 0.838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 38460810.1162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 38460809.9772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 38460810.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 38460810.1162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 38460809.9772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 38460810.1103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 38460810.0595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 0.0532 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 147.1938 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 283.9925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 285.6175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 273.342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 207.8824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 0.0532 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 168.8959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 245.7518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 177.2001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 234.787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 133.3034 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 133.79 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 133.79 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 133.79 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 133.79 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 127.6982 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 248.6344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 279.2991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 263.376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 201.3843 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 14.0217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 162.2879 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 232.6098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 172.4844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 221.4288 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 133.9754 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 119.3844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 119.3844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 117.16 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 119.3844 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_FourHourOfWeek_NoAR 4 1.4169 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 56.5535 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 62.6494 @@ -209,7 +401,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Po INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_NoAR 52 79.0316 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 58.3009 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 58.3839 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 12.896776914596558 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 32.72514533996582 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_Hourly' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-11-28T11:00:00.000000 TimeDelta= Horizon=24 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10000 Min=2.0460889218351275 Max=2768.9764229577795 Mean=1699.259571463825 StdDev=1243.5938707979117 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=2.0460889218351275 Max=2768.9764229577795 Mean=1699.259571463825 StdDev=1243.5938707979117 @@ -235,25 +427,20 @@ INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_Lag1Trend_residue_Seasonal_HourOfWee INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 1.1615498065948486 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 2.41277813911438 INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 11.38675570487976 - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0557 0.0061 -1 None CumSum_Signal ... 0.0532 0.0070 - -[2 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.035667896270752 Split Transformation ... TestMAPE TestMASE 0 None _Signal ... 0.0557 0.0061 1 None CumSum_Signal ... 0.0532 0.0070 @@ -315,31 +502,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 24, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-02-20 15:00:00" - ], - "TimeVariable": "Time_Hourly" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 24, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-02-20 15:00:00" + ], + "TimeVariable": "Time_Hourly" + }, + "Training_Signal_Length": 10000 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR", + "Cycle": "Seasonal_HourOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 10000 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR", - "Cycle": "Seasonal_HourOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "36", - "MAE": "1.145900390137953", - "MAPE": "0.0424", - "MASE": "0.0058", - "RMSE": "1.4294060088452705" + "Model_Performance": { + "COMPLEXITY": "36", + "MAE": "1.145900390137953", + "MAPE": "0.0424", + "MASE": "0.0058", + "RMSE": "1.4294060088452705" + } } } diff --git a/tests/references/HourOfWeek_test_Business_Hourly_WeekEnd.log b/tests/references/HourOfWeek_test_Business_Hourly_WeekEnd.log index 6be440b5f..e5e66eff7 100644 --- a/tests/references/HourOfWeek_test_Business_Hourly_WeekEnd.log +++ b/tests/references/HourOfWeek_test_Business_Hourly_WeekEnd.log @@ -17,6 +17,198 @@ Data columns (total 4 columns): dtypes: datetime64[ns](1), float64(1), int64(2) memory usage: 312.6 KB None +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 4 0.0495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 2.2444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 6.6709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 36 0.0896 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 0.0891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 0.0891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 0.0877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 36 0.0638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 36 0.0673 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 0.0892 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 0.0863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 0.0892 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 0.0855 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 0.0893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 36 0.0896 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.0893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 2.1643 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.1008 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.1064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 0.1063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 0.1068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 0.1074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 20 2.1391 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 0.1067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 0.1054 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 0.1053 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 0.1066 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 2.1377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 2.0792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 2.1379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 6.5668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 2.4569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.2451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.2295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 0.2287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 0.2316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 0.2306 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 20 2.4841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 0.23 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 0.2321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 0.2307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 0.2309 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 2.4875 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 2.3737 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 2.4839 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 6.8872 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 36 41.517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 36.0626 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 35.3938 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 47.22 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 58.9754 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR 36 0.7167 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 355.7548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 52.5248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 61.0577 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 33.8897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 949.4912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 37.9084 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 35.428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 4.4299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 4.4299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 9.8067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 3.0112 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 9.4024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 2.0252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 6.4872 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 1.6021 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 17.2304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 8.6391 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 6.7002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 12.9651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 16.147 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 0.3536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 18.7107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 49.4591 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 43.8502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 41.1523 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 51.8425 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 64.5835 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 7.0959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 361.4214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 54.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 68.2154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 39.0539 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 942.5163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 47.1306 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 44.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 2.2131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 2.2131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 12.7136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 4.529 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 7.1087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 14.9886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 32.5563 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 26.2471 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 329.6173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 25.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 34.5112 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 6.1911 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 974.9286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 8.7153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 22.5435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 31.7242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 31.7242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 136.9106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 2057673187.1082 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 0.6653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 2057673188.3993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 12.7746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 0.9604 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 2057673187.7519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 2057673187.7556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.0652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 7.6708 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 10.578 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 10.6216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 11.2144 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 9.778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 0.0885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 8.8903 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 10.776 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 8.6823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 10.186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 6.655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 6.6101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 6.6101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 6.6101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 6.6101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 7.4256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 9.7354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 9.8902 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 10.601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 9.4625 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 0.8337 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 5.5172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 10.2941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 8.497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 9.5274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 6.7121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 5.8092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 5.8092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 5.8092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 5.8092 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_NoAR 4 0.0336 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 1.1045 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 3.0354 @@ -209,7 +401,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Po INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_NoAR 52 3.7767 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_None_NoAR 56 2.78 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 2.78 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 12.050038576126099 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 35.8412389755249 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_Hourly' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-11-28T11:00:00.000000 TimeDelta= Horizon=24 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10000 Min=4.0460889218351275 Max=240.0706204906302 Mean=87.41957146382518 StdDev=95.87686313092453 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=4.0460889218351275 Max=240.0706204906302 Mean=87.41957146382518 StdDev=95.87686313092453 @@ -235,24 +427,20 @@ INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_DayOf INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 1.1639015674591064 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 2.065857410430908 INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 11.178277730941772 - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0495 0.4818 - -[1 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.9006769657135 Split Transformation ... TestMAPE TestMASE 0 None _Signal ... 0.0495 0.4818 1 None CumSum_Signal ... 0.0652 0.6505 @@ -314,31 +502,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 24, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-02-20 15:00:00" - ], - "TimeVariable": "Time_Hourly" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 24, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-02-20 15:00:00" + ], + "TimeVariable": "Time_Hourly" + }, + "Training_Signal_Length": 10000 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 10000 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "0.8010224899152841", - "MAPE": "0.0336", - "MASE": "0.2074", - "RMSE": "0.9993840459287185" + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.8010224899152841", + "MAPE": "0.0336", + "MASE": "0.2074", + "RMSE": "0.9993840459287185" + } } } diff --git a/tests/references/WeekOfMonth_test_Business_DayOfNthWeek.log b/tests/references/WeekOfMonth_test_Business_DayOfNthWeek.log index 53588e6a3..d5ffc84c6 100644 --- a/tests/references/WeekOfMonth_test_Business_DayOfNthWeek.log +++ b/tests/references/WeekOfMonth_test_Business_DayOfNthWeek.log @@ -18,6 +18,198 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(1), int64(3) memory usage: 390.8 KB None +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 4 0.0493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 2.3461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 3.9667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 36 0.0987 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 0.0981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.0977 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 0.0979 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 0.0974 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 36 0.0637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 36 0.099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 0.0981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 0.0948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 0.0981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 0.0966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 0.0983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 36 0.0987 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.0983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 9.867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.5512 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.5446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 0.534 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 0.5378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 0.5361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 20 2.8901 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 0.5349 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 0.5373 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 0.5405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 0.5378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 9.8677 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 10.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 2.8984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 4.5617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 11.4445 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 2.1652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 2.1552 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 2.1477 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 2.1456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 2.1451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 20 4.536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 2.1508 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 2.1451 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Diff_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 0.7553 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 8.4361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 4.2271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 3.3931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 3.6007 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 3.6007 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 14.2565 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 5.4732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 8.1208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 0.3088 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 3.186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 8.6531 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 7.536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 5.8804 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 5.1906 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 10.2925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 2.4347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 2.1519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 12.2972 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 5.4866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 10.0699 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Diff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 3.4711 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 2.7215 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 6.9483 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 10.7192 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.6202 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.6202 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 12.2935 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 17.6894 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Diff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 9.9775 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 4.1744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 16.5986 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 20.0711 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 9.1721 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 9.1721 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.8263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.7066 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RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.8263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 0.8263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 0.8263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 0.8263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 0.8263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 0.8263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.8263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0983 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: MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 41250571.7866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 41250571.7809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 41250571.7809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 41250571.7809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 41250571.7866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 41250571.7866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 41250571.7866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 41250571.7866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 41250571.7809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 41250571.7809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 0.0615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 4.8985 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 4.7103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 4.8935 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 6.7008 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 6.1066 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 0.4578 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 3.2042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 6.6199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 5.5269 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 4.9372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 4.4056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 4.0012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 4.0012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 4.0012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 4.0012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 4.4699 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 4.1311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 4.2857 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 6.0219 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 5.4789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 0.6663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 3.23 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 5.8941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 4.9453 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 4.1939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 4.0762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 3.3837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 3.3837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 3.3837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 3.3837 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 4 0.2079 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 2.4239 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 3.9948 @@ -210,7 +402,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Po INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_NoAR 52 8.3827 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_None_NoAR 56 3.7082 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 3.7082 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 12.781678199768066 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 24.31827974319458 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_Hourly' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-11-28T11:00:00.000000 TimeDelta= Horizon=24 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10000 Min=2.0460889218351275 Max=128.97642295777942 Mean=58.20357146382517 StdDev=50.73285407810681 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Signal' Min=6.904896811203461 Max=582035.7146382505 Mean=287070.56182537746 StdDev=170703.72771107237 @@ -236,24 +428,20 @@ INFO:pyaf.std:SEASONAL_MODEL_VALUES CumSum_Signal_Lag1Trend_residue_Seasonal_Day INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 1.3197686672210693 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 2.991826057434082 INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 11.301483631134033 - Split Transformation ... TestMAPE TestMASE -0 None CumSum_Signal ... 0.0615 0.3087 - -[1 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.117292881011963 Split Transformation ... TestMAPE TestMASE 0 None CumSum_Signal ... 0.0615 0.3087 1 None _Signal ... 0.0637 0.3365 @@ -317,31 +505,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 24, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-02-20 15:00:00" - ], - "TimeVariable": "Time_Hourly" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 24, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-02-20 15:00:00" + ], + "TimeVariable": "Time_Hourly" + }, + "Training_Signal_Length": 10000 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR", + "Cycle": "Seasonal_DayOfNthWeekOfMonth", + "Signal_Transoformation": "Integration", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 10000 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR", - "Cycle": "Seasonal_DayOfNthWeekOfMonth", - "Signal_Transoformation": "Integration", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "68", - "MAE": "1.361697843252755", - "MAPE": "0.1092", - "MASE": "0.6113", - "RMSE": "3.8434676879479586" + "Model_Performance": { + "COMPLEXITY": "68", + "MAE": "1.361697843252755", + "MAPE": "0.1092", + "MASE": "0.6113", + "RMSE": "3.8434676879479586" + } } } diff --git a/tests/references/WeekOfMonth_test_Business_WeekOfMonth.log b/tests/references/WeekOfMonth_test_Business_WeekOfMonth.log index 655249872..35b100d1b 100644 --- a/tests/references/WeekOfMonth_test_Business_WeekOfMonth.log +++ b/tests/references/WeekOfMonth_test_Business_WeekOfMonth.log @@ -18,6 +18,198 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(1), int64(3) memory usage: 390.8 KB None +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 4 0.1131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 4 0.1082 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 4 0.1193 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 4 0.1213 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 4 0.1214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR 4 0.1271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 4 0.119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 4 0.1196 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 4 0.123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 4 0.1206 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 4 0.1272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 4 0.1049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 4 0.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.4281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.4281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 36 0.1475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 0.1458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.1458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 0.1456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 0.1469 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 36 0.1406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 36 0.1489 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 0.146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 0.142 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 0.1459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 0.1448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 0.1462 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 36 0.1473 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.1464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 0.1322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.1046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 0.1073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 0.108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 0.1159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 20 0.1067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 0.107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 0.1064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 0.106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 0.112 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 0.1198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 0.1259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3704 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3704 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 0.2747 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.2589 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.1836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 0.1817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 0.1878 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 20 0.1897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 0.1804 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 0.1818 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 0.1804 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 0.1832 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 0.2602 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 0.2721 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.1997 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.1997 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 36 22.3316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 33.4158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 24.6883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 13.773 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 26.4074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR 36 33.9697 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 34.9902 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 16.5197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 34.8746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 17.2057 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 19.9876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 19.2518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 34.0358 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.1939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 41.6213 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 25.2984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 8.6298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 9.42 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 5.5732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 0.4974 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 32.6599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 1.4214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 12.6657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 11.098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 4.3434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 16.0703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 3.1189 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 22.9451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 34.0293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 25.5094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 14.1549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 27.1088 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 34.4735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 35.6299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 16.9065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 35.5175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 17.4068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 20.6258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 19.5546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 34.7911 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.7459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.7459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 25.6558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 36.619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 28.5791 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 16.8666 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 29.8403 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 37.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 38.7723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 19.5726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 37.9383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 20.031 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 23.2156 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 21.7085 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 37.2911 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 3.429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 3.429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 115130263.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 115130264.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 115130264.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 115130264.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 115130264.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 115130264.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 115130264.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 115130263.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 115130263.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 115130264.8424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 115130264.8424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 115130263.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 115130264.8424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 115130264.8424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 115130264.8424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 115130263.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 115130264.8424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 115130263.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 115130263.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 115130264.042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 115130263.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 115130264.8424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 2.601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.8737 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 0.1478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 0.8072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.4219 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.4219 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 0.7058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 0.7409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 0.5903 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 0.7506 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 0.4725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 0.4517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 0.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 0.6767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 0.6449 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 0.6457 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 0.3094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 0.3094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.7738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.3094 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfMonth_NoAR 4 0.1438 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 4 0.2781 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_NoAR 4 0.3087 @@ -210,7 +402,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Po INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_NoAR 52 0.78 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.58 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 0.4664 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 12.659698486328125 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 25.972113132476807 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_Hourly' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-11-28T11:00:00.000000 TimeDelta= Horizon=24 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10000 Min=1.0087114820063787 Max=18.08971940186743 Mean=7.355571463825168 StdDev=4.338269632537709 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0087114820063787 Max=18.08971940186743 Mean=7.355571463825168 StdDev=4.338269632537709 @@ -236,25 +428,20 @@ INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_DayOf INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 1.1389026641845703 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 2.882389545440674 INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 11.344051837921143 - Split Transformation ... TestMAPE TestMASE -1 None _Signal ... 0.1131 0.8156 -0 None _Signal ... 0.1322 0.9880 - -[2 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.884492874145508 Split Transformation ... TestMAPE TestMASE 0 None _Signal ... 0.1322 0.9880 1 None _Signal ... 0.1131 0.8156 @@ -316,31 +503,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 24, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-02-20 15:00:00" - ], - "TimeVariable": "Time_Hourly" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 24, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-02-20 15:00:00" + ], + "TimeVariable": "Time_Hourly" + }, + "Training_Signal_Length": 10000 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR", + "Cycle": "Seasonal_DayOfMonth", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 10000 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR", - "Cycle": "Seasonal_DayOfMonth", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "0.8013268287451984", - "MAPE": "0.1438", - "MASE": "0.6916", - "RMSE": "1.0003899486432173" + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.8013268287451984", + "MAPE": "0.1438", + "MASE": "0.6916", + "RMSE": "1.0003899486432173" + } } } diff --git a/tests/references/basic_checks_test_pearson.log b/tests/references/basic_checks_test_pearson.log index e5fba5683..b4b205a5b 100644 --- a/tests/references/basic_checks_test_pearson.log +++ b/tests/references/basic_checks_test_pearson.log @@ -1,2 +1,2 @@ -1.4.1 1.15.4 sys.version_info(major=3, minor=7, micro=1, releaselevel='final', serial=0) -(0.8660254037844386, 0.011724811003954649) (0.8660254037844388, 0.011724811003954602) +1.4.1 1.19.0 sys.version_info(major=3, minor=8, micro=4, releaselevel='final', serial=0) +(0.8660254037844386, 0.011724811003954649) (0.8660254037844386, 0.011724811003954649) diff --git a/tests/references/bugs_issue_106_insurance_exog.log b/tests/references/bugs_issue_106_insurance_exog.log index 7414f7553..9735beef5 100644 --- a/tests/references/bugs_issue_106_insurance_exog.log +++ b/tests/references/bugs_issue_106_insurance_exog.log @@ -27,7 +27,7 @@ Data columns (total 2 columns): 1 TV.advert 40 non-null float64 dtypes: float64(2) memory usage: 768.0 bytes -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Quotes' 3.241619110107422 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Quotes']' 2.293325901031494 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=2002.0 TimeMax=2004.25 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Quotes' Length=40 Min=8.394680000000001 Max=18.438979999999997 Mean=13.604347 StdDev=2.369165266733412 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Quotes' Min=8.394680000000001 Max=18.438979999999997 Mean=13.604347 StdDev=2.369165266733412 @@ -40,32 +40,41 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Quotes_ConstantTrend_residue_zeroCycle_residue_AR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0773 MAPE_Forecast=0.0849 MAPE_Test=0.11 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.075 SMAPE_Forecast=0.0927 SMAPE_Test=0.1191 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6987 MASE_Forecast=0.8417 MASE_Test=0.7994 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.9626864830078556 L1_Forecast=1.1534761793879782 L1_Test=1.8425531526283625 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.1670717218220485 L2_Forecast=1.6085552731482886 L2_Test=2.1866032428022977 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.9626864830078559 L1_Forecast=1.153476179387978 L1_Test=1.842553152628363 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.1670717218220485 L2_Forecast=1.6085552731482895 L2_Test=2.1866032428022977 INFO:pyaf.std:MODEL_COMPLEXITY 7 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 13.298839285714282 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Quotes_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 TV.advert_Lag1 -2.1005818786858557 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag1 1.7363372330908362 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.2717817063855942 -INFO:pyaf.std:AR_MODEL_COEFF 4 TV.advert_Lag9 -0.19440202823219743 -INFO:pyaf.std:AR_MODEL_COEFF 5 TV.advert_Lag10 -0.12693071971380282 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag9 0.12521608597557266 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.020224752260123477 +INFO:pyaf.std:AR_MODEL_COEFF 1 TV.advert_Lag1 -2.100581878685855 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag1 1.7363372330908358 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.2717817063855931 +INFO:pyaf.std:AR_MODEL_COEFF 4 TV.advert_Lag9 -0.19440202823219688 +INFO:pyaf.std:AR_MODEL_COEFF 5 TV.advert_Lag10 -0.12693071971380188 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag9 0.12521608597557277 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.020224752260124212 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.38603138923645 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.2240135669708252 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 15.633532524108887 +INFO:pyaf.std:START_FORECASTING '['Quotes']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Quotes']' 0.19390463829040527 Forecast Columns Index(['Index', 'Quotes', 'row_number', 'Index_Normalized', '_Quotes', '_Quotes_ConstantTrend', '_Quotes_ConstantTrend_residue', '_Quotes_ConstantTrend_residue_zeroCycle', @@ -98,31 +107,33 @@ Forecasts { - "Dataset": { - "Signal": "Quotes", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2002.0", - "2005.25" - ], - "TimeVariable": "Index" + "Quotes": { + "Dataset": { + "Signal": "Quotes", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2002.0", + "2005.25" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 40 + }, + "Model": { + "AR_Model": "ARX", + "Best_Decomposition": "_Quotes_ConstantTrend_residue_zeroCycle_residue_ARX(10)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 40 - }, - "Model": { - "AR_Model": "ARX", - "Best_Decomposition": "_Quotes_ConstantTrend_residue_zeroCycle_residue_ARX(10)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "7", - "MAE": "1.1534761793879782", - "MAPE": "0.0849", - "MASE": "0.8417", - "RMSE": "1.6085552731482886" + "Model_Performance": { + "COMPLEXITY": "7", + "MAE": "1.153476179387978", + "MAPE": "0.0849", + "MASE": "0.8417", + "RMSE": "1.6085552731482895" + } } } diff --git a/tests/references/bugs_issue_106_insurance_exog_svrx.log b/tests/references/bugs_issue_106_insurance_exog_svrx.log index 4f89c41ab..b9634b0ed 100644 --- a/tests/references/bugs_issue_106_insurance_exog_svrx.log +++ b/tests/references/bugs_issue_106_insurance_exog_svrx.log @@ -27,7 +27,7 @@ Data columns (total 2 columns): 1 TV.advert 40 non-null float64 dtypes: float64(2) memory usage: 768.0 bytes -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Quotes' 3.1963391304016113 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Quotes']' 2.4258835315704346 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=2002.0 TimeMax=2004.25 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Quotes' Length=40 Min=8.394680000000001 Max=18.438979999999997 Mean=13.604347 StdDev=2.369165266733412 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Quotes' Min=8.394680000000001 Max=18.438979999999997 Mean=13.604347 StdDev=2.369165266733412 @@ -43,22 +43,31 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9139 MASE_Forecast=0.7398 MASE_Test=1.0453 INFO:pyaf.std:MODEL_L1 L1_Fit=1.2592287805681903 L1_Forecast=1.0137629010453009 L1_Test=2.40949481081744 INFO:pyaf.std:MODEL_L2 L2_Fit=1.540112999787147 L2_Forecast=1.3045166407249262 L2_Test=2.849910160294584 INFO:pyaf.std:MODEL_COMPLEXITY 7 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 13.298839285714282 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Quotes_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.375471591949463 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.22046709060668945 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 16.34457230567932 +INFO:pyaf.std:START_FORECASTING '['Quotes']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Quotes']' 0.21106410026550293 Forecast Columns Index(['Index', 'Quotes', 'row_number', 'Index_Normalized', '_Quotes', '_Quotes_ConstantTrend', '_Quotes_ConstantTrend_residue', '_Quotes_ConstantTrend_residue_zeroCycle', @@ -91,31 +100,33 @@ Forecasts { - "Dataset": { - "Signal": "Quotes", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2002.0", - "2005.25" - ], - "TimeVariable": "Index" + "Quotes": { + "Dataset": { + "Signal": "Quotes", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2002.0", + "2005.25" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 40 + }, + "Model": { + "AR_Model": "SVRX", + "Best_Decomposition": "_Quotes_ConstantTrend_residue_zeroCycle_residue_SVRX(10)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 40 - }, - "Model": { - "AR_Model": "SVRX", - "Best_Decomposition": "_Quotes_ConstantTrend_residue_zeroCycle_residue_SVRX(10)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "7", - "MAE": "1.0137629010453009", - "MAPE": "0.0771", - "MASE": "0.7398", - "RMSE": "1.3045166407249262" + "Model_Performance": { + "COMPLEXITY": "7", + "MAE": "1.0137629010453009", + "MAPE": "0.0771", + "MASE": "0.7398", + "RMSE": "1.3045166407249262" + } } } diff --git a/tests/references/bugs_issue_106_insurance_exog_xgbx.log b/tests/references/bugs_issue_106_insurance_exog_xgbx.log index 073287e42..648f96a49 100644 --- a/tests/references/bugs_issue_106_insurance_exog_xgbx.log +++ b/tests/references/bugs_issue_106_insurance_exog_xgbx.log @@ -27,7 +27,7 @@ Data columns (total 2 columns): 1 TV.advert 40 non-null float64 dtypes: float64(2) memory usage: 768.0 bytes -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Quotes' 4.178548336029053 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Quotes']' 2.7035975456237793 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=2002.0 TimeMax=2004.25 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Quotes' Length=40 Min=8.394680000000001 Max=18.438979999999997 Mean=13.604347 StdDev=2.369165266733412 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='RelDiff_Quotes' Min=-0.9999998617059067 Max=38888522.8393915 Mean=972213.1063974078 StdDev=6071468.676646217 @@ -43,22 +43,31 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=2.4446 MASE_Forecast=0.7626 MASE_Test=2.1211 INFO:pyaf.std:MODEL_L1 L1_Fit=3.368315168993535 L1_Forecast=1.0451022900788 L1_Test=4.889243758295139 INFO:pyaf.std:MODEL_L2 L2_Fit=4.77675507548661 L2_Forecast=1.282249697252878 L2_Test=5.03116626898096 INFO:pyaf.std:MODEL_COMPLEXITY 39 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:REALTIVE_DIFFERENCING_TRANSFORMATION RelativeDifference 0.4555787859781169 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.006617895414000985 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES RelDiff_Quotes_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 26.588268518447876 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.16761350631713867 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 15.72731614112854 +INFO:pyaf.std:START_FORECASTING '['Quotes']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Quotes']' 0.23804903030395508 Forecast Columns Index(['Index', 'Quotes', 'row_number', 'Index_Normalized', 'RelDiff_Quotes', 'RelDiff_Quotes_ConstantTrend', 'RelDiff_Quotes_ConstantTrend_residue', 'RelDiff_Quotes_ConstantTrend_residue_zeroCycle', @@ -92,31 +101,33 @@ Forecasts { - "Dataset": { - "Signal": "Quotes", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2002.0", - "2005.25" - ], - "TimeVariable": "Index" + "Quotes": { + "Dataset": { + "Signal": "Quotes", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2002.0", + "2005.25" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 40 + }, + "Model": { + "AR_Model": "XGBX", + "Best_Decomposition": "RelDiff_Quotes_ConstantTrend_residue_zeroCycle_residue_XGBX(10)", + "Cycle": "NoCycle", + "Signal_Transoformation": "RelativeDifference", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 40 - }, - "Model": { - "AR_Model": "XGBX", - "Best_Decomposition": "RelDiff_Quotes_ConstantTrend_residue_zeroCycle_residue_XGBX(10)", - "Cycle": "NoCycle", - "Signal_Transoformation": "RelativeDifference", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "39", - "MAE": "1.0451022900788", - "MAPE": "0.0773", - "MASE": "0.7626", - "RMSE": "1.282249697252878" + "Model_Performance": { + "COMPLEXITY": "39", + "MAE": "1.0451022900788", + "MAPE": "0.0773", + "MASE": "0.7626", + "RMSE": "1.282249697252878" + } } } diff --git a/tests/references/bugs_issue_106_submitted_script.log b/tests/references/bugs_issue_106_submitted_script.log index 64ba4e84c..af1578b65 100644 --- a/tests/references/bugs_issue_106_submitted_script.log +++ b/tests/references/bugs_issue_106_submitted_script.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'Quotes' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Quotes' 3.9653656482696533 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Quotes']' 2.292341470718384 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=2002.0 TimeMax=2004.25 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Quotes' Length=40 Min=8.394680000000001 Max=18.438979999999997 Mean=13.604347 StdDev=2.369165266733412 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Quotes' Min=8.394680000000001 Max=18.438979999999997 Mean=13.604347 StdDev=2.369165266733412 @@ -12,15 +12,24 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Quotes_ConstantTrend_residue_zeroCycle_residue_AR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0773 MAPE_Forecast=0.0849 MAPE_Test=0.11 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.075 SMAPE_Forecast=0.0927 SMAPE_Test=0.1191 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6987 MASE_Forecast=0.8417 MASE_Test=0.7994 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.9626864830078556 L1_Forecast=1.1534761793879782 L1_Test=1.8425531526283625 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.1670717218220485 L2_Forecast=1.6085552731482886 L2_Test=2.1866032428022977 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.9626864830078559 L1_Forecast=1.153476179387978 L1_Test=1.842553152628363 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.1670717218220485 L2_Forecast=1.6085552731482895 L2_Test=2.1866032428022977 INFO:pyaf.std:MODEL_COMPLEXITY 7 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 13.298839285714282 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Quotes_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 TV.advert_Lag1 -2.1005818786858557 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag1 1.7363372330908362 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.2717817063855942 -INFO:pyaf.std:AR_MODEL_COEFF 4 TV.advert_Lag9 -0.19440202823219743 -INFO:pyaf.std:AR_MODEL_COEFF 5 TV.advert_Lag10 -0.12693071971380282 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag9 0.12521608597557266 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.020224752260123477 +INFO:pyaf.std:AR_MODEL_COEFF 1 TV.advert_Lag1 -2.100581878685855 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag1 1.7363372330908358 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.2717817063855931 +INFO:pyaf.std:AR_MODEL_COEFF 4 TV.advert_Lag9 -0.19440202823219688 +INFO:pyaf.std:AR_MODEL_COEFF 5 TV.advert_Lag10 -0.12693071971380188 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag9 0.12521608597557277 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Quotes_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.020224752260124212 INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/bugs_issue_19_issue_19_no_interpolation.log b/tests/references/bugs_issue_19_issue_19_no_interpolation.log index a63dccd8a..4bb153666 100644 --- a/tests/references/bugs_issue_19_issue_19_no_interpolation.log +++ b/tests/references/bugs_issue_19_issue_19_no_interpolation.log @@ -14,7 +14,7 @@ INFO:pyaf.std:START_TRAINING 'number' 11 1979-01-01 554711.0 12 2010-01-01 1089170.0 13 2012-01-01 1113000.0 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'number' 3.1107118129730225 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['number']' 2.124722957611084 INFO:pyaf.std:TIME_DETAIL TimeVariable='date' TimeMin=1890-01-01T00:00:00.000000 TimeMax=2012-01-01T00:00:00.000000 TimeDelta= Horizon=7 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='number' Length=14 Min=328.0 Max=1113000.0 Mean=336235.5714285714 StdDev=366246.1043413908 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_number' Min=328.0 Max=1113000.0 Mean=336235.5714285714 StdDev=366246.1043413908 @@ -29,10 +29,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9286 MASE_Forecast=0.9286 MASE_Test=0.9286 INFO:pyaf.std:MODEL_L1 L1_Fit=80286.14285714286 L1_Forecast=80286.14285714286 L1_Test=80286.14285714286 INFO:pyaf.std:MODEL_L2 L2_Fit=161993.1535475673 L2_Forecast=161993.1535475673 L2_Test=161993.1535475673 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 328.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _number_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.30592966079711914 +INFO:pyaf.std:START_FORECASTING '['number']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['number']' 0.2197122573852539 None Index(['date', 'number', 'row_number', 'date_Normalized', '_number', '_number_Lag1Trend', '_number_Lag1Trend_residue', diff --git a/tests/references/bugs_issue_21_test_artificial_1024__poly_7__20.log b/tests/references/bugs_issue_21_test_artificial_1024__poly_7__20.log index d2de25dc9..da13a3b43 100644 --- a/tests/references/bugs_issue_21_test_artificial_1024__poly_7__20.log +++ b/tests/references/bugs_issue_21_test_artificial_1024__poly_7__20.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_poly_7_None_0.0_20 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 29.79198718070984 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 28.49068808555603 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=0.9748625189130324 Max=8.174474529963426 Mean=4.467083113887294 StdDev=2.2720101238883745 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=0.9748625189130324 Max=8.174474529963426 Mean=4.467083113887294 StdDev=2.2720101238883745 @@ -10,16 +10,25 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_Seasonal_D INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0027 MAPE_Forecast=0.0025 MAPE_Test=0.0024 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0027 SMAPE_Forecast=0.0025 SMAPE_Test=0.0024 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0024 MASE_Forecast=0.0024 MASE_Test=0.0025 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.0077427509380126325 L1_Forecast=0.007752178330234778 L1_Test=0.008092973573743204 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.009562756644705524 L2_Forecast=0.00972677380342146 L2_Test=0.009494035022035836 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0027 MAPE_Forecast=0.0026 MAPE_Test=0.0023 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0027 SMAPE_Forecast=0.0026 SMAPE_Test=0.0023 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0024 MASE_Forecast=0.0024 MASE_Test=0.0024 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.007733941651715486 L1_Forecast=0.00781048053426572 L1_Test=0.007931562352892718 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.009571772574003903 L2_Forecast=0.00978873961733261 L2_Test=0.009289379331030634 INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.464686321798596 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek -0.6008510412605059 {5: -0.6054546233981428, 6: -3.464640601873258, 0: -0.6079803747701233, 1: 2.249456137708915, 2: -2.0355254096829376, 3: 0.821461245799457, 4: 3.6799519609362124} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5261518955230713 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.32052111625671387 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', '_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek', @@ -46,59 +55,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 2.4287561598270684 - 2.4096916831723623 2.4478206364817745] - [Timestamp('2002-10-10 00:00:00') nan 5.285869394863768 - 5.266804918209062 5.304933871518474] - [Timestamp('2002-10-11 00:00:00') nan 8.144200495319833 - 8.125136018665128 8.163264971974538] - [Timestamp('2002-10-12 00:00:00') nan 3.8584181828121755 - 3.8393537061574694 3.8774826594668816] - [Timestamp('2002-10-13 00:00:00') nan 0.9999812295832231 - 0.980916752928517 1.0190457062379292] - [Timestamp('2002-10-14 00:00:00') nan 3.8569928710065025 - 3.8379283943517963 3.8760573476612086] - [Timestamp('2002-10-15 00:00:00') nan 6.7142962106793735 - 6.695231734024667 6.73336068733408] - [Timestamp('2002-10-16 00:00:00') nan 2.4287561598270684 - 2.4096916831723623 2.4478206364817745] - [Timestamp('2002-10-17 00:00:00') nan 5.285869394863768 - 5.266804918209062 5.304933871518474] - [Timestamp('2002-10-18 00:00:00') nan 8.144200495319833 - 8.125136018665128 8.163264971974538] - [Timestamp('2002-10-19 00:00:00') nan 3.8584181828121755 - 3.8393537061574694 3.8774826594668816] - [Timestamp('2002-10-20 00:00:00') nan 0.9999812295832231 - 0.980916752928517 1.0190457062379292]] + [[Timestamp('2002-10-09 00:00:00') nan 2.4291609121156585 + 2.4099749824656866 2.4483468417656304] + [Timestamp('2002-10-10 00:00:00') nan 5.286147567598054 + 5.266961637948081 5.305333497248026] + [Timestamp('2002-10-11 00:00:00') nan 8.144638282734808 + 8.125452353084837 8.16382421238478] + [Timestamp('2002-10-12 00:00:00') nan 3.8592316984004533 + 3.8400457687504814 3.8784176280504252] + [Timestamp('2002-10-13 00:00:00') nan 1.000045719925338 + 0.9808597902753662 1.01923164957531] + [Timestamp('2002-10-14 00:00:00') nan 3.856705947028473 + 3.837520017378501 3.8758918766784447] + [Timestamp('2002-10-15 00:00:00') nan 6.714142459507511 + 6.694956529857539 6.733328389157483] + [Timestamp('2002-10-16 00:00:00') nan 2.4291609121156585 + 2.4099749824656866 2.4483468417656304] + [Timestamp('2002-10-17 00:00:00') nan 5.286147567598054 + 5.266961637948081 5.305333497248026] + [Timestamp('2002-10-18 00:00:00') nan 8.144638282734808 + 8.125452353084837 8.16382421238478] + [Timestamp('2002-10-19 00:00:00') nan 3.8592316984004533 + 3.8400457687504814 3.8784176280504252] + [Timestamp('2002-10-20 00:00:00') nan 1.000045719925338 + 0.9808597902753662 1.01923164957531]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "0.007752178330234778", - "MAPE": "0.0025", - "MASE": "0.0024", - "RMSE": "0.00972677380342146" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.00781048053426572", + "MAPE": "0.0026", + "MASE": "0.0024", + "RMSE": "0.00978873961733261" + } } } @@ -107,7 +118,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":8.1439221291,"1001":3.8708356429,"1002":1.003193187,"1003":3.8672664353,"1004":6.7010063142,"1005":2.4261025469,"1006":5.3010607342,"1007":8.1551230437,"1008":3.8604243275,"1009":1.0071801442,"1010":3.8433870139,"1011":6.7082301895,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":8.1442004953,"1001":3.8584181828,"1002":0.9999812296,"1003":3.856992871,"1004":6.7142962107,"1005":2.4287561598,"1006":5.2858693949,"1007":8.1442004953,"1008":3.8584181828,"1009":0.9999812296,"1010":3.856992871,"1011":6.7142962107,"1012":2.4287561598,"1013":5.2858693949,"1014":8.1442004953,"1015":3.8584181828,"1016":0.9999812296,"1017":3.856992871,"1018":6.7142962107,"1019":2.4287561598,"1020":5.2858693949,"1021":8.1442004953,"1022":3.8584181828,"1023":0.9999812296},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.4096916832,"1013":5.2668049182,"1014":8.1251360187,"1015":3.8393537062,"1016":0.9809167529,"1017":3.8379283944,"1018":6.695231734,"1019":2.4096916832,"1020":5.2668049182,"1021":8.1251360187,"1022":3.8393537062,"1023":0.9809167529},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.4478206365,"1013":5.3049338715,"1014":8.163264972,"1015":3.8774826595,"1016":1.0190457062,"1017":3.8760573477,"1018":6.7333606873,"1019":2.4478206365,"1020":5.3049338715,"1021":8.163264972,"1022":3.8774826595,"1023":1.0190457062}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":8.1439221291,"1001":3.8708356429,"1002":1.003193187,"1003":3.8672664353,"1004":6.7010063142,"1005":2.4261025469,"1006":5.3010607342,"1007":8.1551230437,"1008":3.8604243275,"1009":1.0071801442,"1010":3.8433870139,"1011":6.7082301895,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":8.1446382827,"1001":3.8592316984,"1002":1.0000457199,"1003":3.856705947,"1004":6.7141424595,"1005":2.4291609121,"1006":5.2861475676,"1007":8.1446382827,"1008":3.8592316984,"1009":1.0000457199,"1010":3.856705947,"1011":6.7141424595,"1012":2.4291609121,"1013":5.2861475676,"1014":8.1446382827,"1015":3.8592316984,"1016":1.0000457199,"1017":3.856705947,"1018":6.7141424595,"1019":2.4291609121,"1020":5.2861475676,"1021":8.1446382827,"1022":3.8592316984,"1023":1.0000457199},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.4099749825,"1013":5.2669616379,"1014":8.1254523531,"1015":3.8400457688,"1016":0.9808597903,"1017":3.8375200174,"1018":6.6949565299,"1019":2.4099749825,"1020":5.2669616379,"1021":8.1254523531,"1022":3.8400457688,"1023":0.9808597903},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.4483468418,"1013":5.3053334972,"1014":8.1638242124,"1015":3.8784176281,"1016":1.0192316496,"1017":3.8758918767,"1018":6.7333283892,"1019":2.4483468418,"1020":5.3053334972,"1021":8.1638242124,"1022":3.8784176281,"1023":1.0192316496}} diff --git a/tests/references/bugs_issue_21_test_ozone_max_autoreg_5.log b/tests/references/bugs_issue_21_test_ozone_max_autoreg_5.log index 222831938..16eacd300 100644 --- a/tests/references/bugs_issue_21_test_ozone_max_autoreg_5.log +++ b/tests/references/bugs_issue_21_test_ozone_max_autoreg_5.log @@ -5,56 +5,65 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 6.931502103805542 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.531840801239014 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51)' [ConstantTrend + Seasonal_MonthOfYear + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1717 MAPE_Forecast=0.1763 MAPE_Test=0.2277 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1638 SMAPE_Forecast=0.1654 SMAPE_Test=0.2154 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7158 MASE_Forecast=0.6915 MASE_Test=1.1909 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6291266190910706 L1_Forecast=0.5368454190888379 L1_Test=0.562969274778477 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.817735753801375 L2_Forecast=0.688166006344623 L2_Test=0.6631881335251025 -INFO:pyaf.std:MODEL_COMPLEXITY 9 +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51)' [LinearTrend + Seasonal_MonthOfYear + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1612 MAPE_Forecast=0.1584 MAPE_Test=0.202 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1558 SMAPE_Forecast=0.1693 SMAPE_Test=0.2154 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7002 MASE_Forecast=0.6602 MASE_Test=0.9674 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6154384466218491 L1_Forecast=0.51251140887633 L1_Test=0.45730962575482526 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8109824782709544 L2_Forecast=0.6268238366862833 L2_Test=0.5022648309220156 +INFO:pyaf.std:MODEL_COMPLEXITY 25 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.012969125577120266 {1: -1.6355903804183052, 2: -1.4774474242490654, 3: -0.8776655161662057, 4: -0.2654214593143367, 5: -0.1765218216970199, 6: 0.7586716848135344, 7: 1.2705207720895366, 8: 1.359815379282721, 9: 0.9720594361345896, 10: 0.8345785406856634, 11: -0.442570179696129, 12: -1.4421958172494698} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.3717558399819233 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 0.16693633808961456 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.14798130478826757 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.12315118564851021 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 0.033367932508881544 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.28569414347803307 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_Lag51 -0.10507614744584112 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.1037960813715369 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_Lag50 -0.09114127409386556 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.09088603353876978 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 26.62859034538269 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.4513118267059326 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 15.639476537704468 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4351489543914795 Split Transformation ... ForecastMAPE TestMAPE -0 None _Ozone ... 0.1677 0.1969 -1 None _Ozone ... 0.1677 0.1969 -2 None _Ozone ... 0.1763 0.2277 -3 None _Ozone ... 0.1763 0.2277 -4 None Diff_Ozone ... 0.1782 0.2139 +0 None _Ozone ... 0.1584 0.2020 +1 None _Ozone ... 0.1753 0.3587 +2 None _Ozone ... 0.1765 0.2209 +3 None Diff_Ozone ... 0.1782 0.2139 +4 None _Ozone ... 0.1824 0.2155 [5 rows x 8 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', - '_Ozone_ConstantTrend', '_Ozone_ConstantTrend_residue', - '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear', - '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue', - '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51)', - '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51)_residue', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51)', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -74,47 +83,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.660600 -205 1972-02-01 NaN 0.933432 -206 1972-03-01 NaN 1.615045 -207 1972-04-01 NaN 2.282369 -208 1972-05-01 NaN 2.480953 -209 1972-06-01 NaN 3.510304 -210 1972-07-01 NaN 4.060964 -211 1972-08-01 NaN 4.037702 -212 1972-09-01 NaN 4.213275 -213 1972-10-01 NaN 3.842288 -214 1972-11-01 NaN 2.757299 -215 1972-12-01 NaN 1.892737 +204 1972-01-01 NaN 0.614000 +205 1972-02-01 NaN 0.820925 +206 1972-03-01 NaN 1.499271 +207 1972-04-01 NaN 2.055680 +208 1972-05-01 NaN 2.090226 +209 1972-06-01 NaN 3.074955 +210 1972-07-01 NaN 3.596957 +211 1972-08-01 NaN 3.732612 +212 1972-09-01 NaN 3.366857 +213 1972-10-01 NaN 3.211462 +214 1972-11-01 NaN 1.928041 +215 1972-12-01 NaN 0.910116 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51)", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51)", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "9", - "MAE": "0.5368454190888379", - "MAPE": "0.1763", - "MASE": "0.6915", - "RMSE": "0.688166006344623" + "Model_Performance": { + "COMPLEXITY": "25", + "MAE": "0.51251140887633", + "MAPE": "0.1584", + "MASE": "0.6602", + "RMSE": "0.6268238366862833" + } } } @@ -123,7 +134,7 @@ Forecasts 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diff --git a/tests/references/bugs_issue_22_test_artificial_1024_cumsum_constant_5__20.log b/tests/references/bugs_issue_22_test_artificial_1024_cumsum_constant_5__20.log index 455bdb58f..7a99e44e4 100644 --- a/tests/references/bugs_issue_22_test_artificial_1024_cumsum_constant_5__20.log +++ b/tests/references/bugs_issue_22_test_artificial_1024_cumsum_constant_5__20.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_constant_5_cumsum_0.0_20 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 23.375853061676025 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 40.24472188949585 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=4.992654846402984 Max=4248.006283907735 Mean=2126.100763761686 StdDev=1226.9854133615368 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=4.992654846402984 Max=4248.006283907735 Mean=2126.100763761686 StdDev=1226.9854133615368 @@ -13,13 +13,22 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_LinearTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0446 MAPE_Forecast=0.0082 MAPE_Test=0.0089 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.031 SMAPE_Forecast=0.0082 SMAPE_Test=0.0089 INFO:pyaf.std:MODEL_MASE MASE_Fit=2.9584 MASE_Forecast=7.3792 MASE_Test=9.6155 -INFO:pyaf.std:MODEL_L1 L1_Fit=12.422340276749978 L1_Forecast=30.96309806305713 L1_Test=37.59245563126092 -INFO:pyaf.std:MODEL_L2 L2_Fit=14.365878855067926 L2_Forecast=31.198732156205125 L2_Test=37.619925078758605 +INFO:pyaf.std:MODEL_L1 L1_Fit=12.42234027674986 L1_Forecast=30.963098063056886 L1_Test=37.59245563126107 +INFO:pyaf.std:MODEL_L2 L2_Fit=14.365878855067795 L2_Forecast=31.198732156204887 L2_Test=37.61992507875875 INFO:pyaf.std:MODEL_COMPLEXITY 16 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (27.737986966420294, array([3306.32337065])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_0.01_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.3774449825286865 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.46735239028930664 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', '_Signal_0.01_LinearTrend', '_Signal_0.01_LinearTrend_residue', '_Signal_0.01_LinearTrend_residue_zeroCycle', @@ -46,8 +55,8 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 4215.471717998313 - 4154.322202972151 4276.621233024474] + [[Timestamp('2002-10-09 00:00:00') nan 4215.471717998314 + 4154.322202972152 4276.621233024475] [Timestamp('2002-10-10 00:00:00') nan 4219.609794807633 4158.460279781471 4280.759309833795] [Timestamp('2002-10-11 00:00:00') nan 4223.7478716169535 @@ -56,49 +65,51 @@ Forecasts 4166.736433400112 4289.035463452436] [Timestamp('2002-10-13 00:00:00') nan 4232.024025235594 4170.874510209433 4293.173540261756] - [Timestamp('2002-10-14 00:00:00') nan 4236.162102044913 - 4175.012587018751 4297.311617071075] + [Timestamp('2002-10-14 00:00:00') nan 4236.162102044914 + 4175.012587018752 4297.3116170710755] [Timestamp('2002-10-15 00:00:00') nan 4240.300178854233 4179.150663828072 4301.449693880395] [Timestamp('2002-10-16 00:00:00') nan 4244.438255663554 4183.288740637392 4305.587770689715] [Timestamp('2002-10-17 00:00:00') nan 4248.576332472874 4187.426817446712 4309.725847499036] - [Timestamp('2002-10-18 00:00:00') nan 4252.714409282193 - 4191.564894256031 4313.863924308354] - [Timestamp('2002-10-19 00:00:00') nan 4256.852486091513 - 4195.702971065351 4318.002001117675] - [Timestamp('2002-10-20 00:00:00') nan 4260.9905629008335 - 4199.841047874672 4322.140077926995]] + [Timestamp('2002-10-18 00:00:00') nan 4252.714409282194 + 4191.564894256032 4313.863924308355] + [Timestamp('2002-10-19 00:00:00') nan 4256.852486091514 + 4195.702971065352 4318.002001117676] + [Timestamp('2002-10-20 00:00:00') nan 4260.990562900834 + 4199.841047874673 4322.140077926996]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_0.01_LinearTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "16", - "MAE": "30.96309806305713", - "MAPE": "0.0082", - "MASE": "7.3792", - "RMSE": "31.198732156205125" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_0.01_LinearTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "16", + "MAE": "30.963098063056886", + "MAPE": "0.0082", + "MASE": "7.3792", + "RMSE": "31.198732156204887" + } } } diff --git a/tests/references/bugs_issue_29_test_mem_1.log b/tests/references/bugs_issue_29_test_mem_1.log index 7a3cb409d..5e1f52d8b 100644 --- a/tests/references/bugs_issue_29_test_mem_1.log +++ b/tests/references/bugs_issue_29_test_mem_1.log @@ -1,46 +1,46 @@ DISPLAY_USED_MEM_START -function 11871 -dict 5379 -tuple 5337 +function 11870 +dict 5386 +tuple 5335 cell 2811 wrapper_descriptor 2705 -method_descriptor 2034 +method_descriptor 2036 getset_descriptor 2000 -builtin_function_or_method 1950 -weakref 1916 +builtin_function_or_method 1960 +weakref 1918 type 981 property 832 -list 600 -module 505 -ModuleSpec 502 +list 603 +module 506 +ModuleSpec 503 member_descriptor 472 fused_cython_function 412 -SourceFileLoader 389 -classmethod 353 +SourceFileLoader 390 +classmethod 354 _GenericAlias 344 set 301 DISPLAY_USED_MEM_END DISPLAY_USED_MEM_START -function 21339 -dict 10053 -tuple 9334 -cell 5471 -weakref 3313 -wrapper_descriptor 3249 -getset_descriptor 3211 -method_descriptor 2758 -builtin_function_or_method 2658 -type 1994 -list 1361 -module 1059 -ModuleSpec 1057 -property 993 -set 912 +function 21693 +dict 10351 +tuple 9581 +cell 6383 +weakref 3333 +getset_descriptor 3259 +wrapper_descriptor 3255 +method_descriptor 2761 +builtin_function_or_method 2665 +type 2002 +list 1824 +Parameter 1776 +module 1069 +ModuleSpec 1066 +property 1019 +set 919 method 890 -SourceFileLoader 848 -fused_cython_function 575 -member_descriptor 516 -classmethod 512 +SourceFileLoader 857 +fused_cython_function 578 +member_descriptor 518 DISPLAY_USED_MEM_END Month Ozone Time 0 1955-01 2.7 1955-01-01 @@ -49,26 +49,26 @@ DISPLAY_USED_MEM_END 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 DISPLAY_USED_MEM_START -function 21869 -dict 10306 -tuple 9535 -cell 5488 -weakref 3644 -getset_descriptor 3263 -wrapper_descriptor 3250 -builtin_function_or_method 2852 -method_descriptor 2771 -type 2097 -list 1421 -module 1084 -ModuleSpec 1082 -property 997 -set 956 +function 22225 +dict 10604 +tuple 9813 +cell 6400 +weakref 3664 +getset_descriptor 3311 +wrapper_descriptor 3256 +builtin_function_or_method 2859 +method_descriptor 2774 +type 2105 +list 1884 +Parameter 1776 +module 1094 +ModuleSpec 1091 +property 1023 +set 963 method 892 -SourceFileLoader 873 -fused_cython_function 575 -member_descriptor 517 -classmethod 512 +SourceFileLoader 882 +fused_cython_function 578 +member_descriptor 519 DISPLAY_USED_MEM_END RangeIndex: 204 entries, 0 to 203 @@ -81,24 +81,24 @@ Data columns (total 3 columns): dtypes: datetime64[ns](1), float64(1), object(1) memory usage: 4.9+ KB DISPLAY_USED_MEM_START -function 21869 -dict 10311 -tuple 9538 -cell 5488 -weakref 3651 -getset_descriptor 3263 -wrapper_descriptor 3250 -builtin_function_or_method 2858 -method_descriptor 2771 -type 2097 -list 1425 -module 1084 -ModuleSpec 1082 -property 997 -set 956 +function 22225 +dict 10609 +tuple 9816 +cell 6400 +weakref 3671 +getset_descriptor 3311 +wrapper_descriptor 3256 +builtin_function_or_method 2865 +method_descriptor 2774 +type 2105 +list 1888 +Parameter 1776 +module 1094 +ModuleSpec 1091 +property 1023 +set 963 method 892 -SourceFileLoader 873 -fused_cython_function 575 -member_descriptor 517 -classmethod 512 +SourceFileLoader 882 +fused_cython_function 578 +member_descriptor 519 DISPLAY_USED_MEM_END diff --git a/tests/references/bugs_issue_32_bug_test_ozone_heroku_1.log b/tests/references/bugs_issue_32_bug_test_ozone_heroku_1.log index e533f919b..5664ca40f 100644 --- a/tests/references/bugs_issue_32_bug_test_ozone_heroku_1.log +++ b/tests/references/bugs_issue_32_bug_test_ozone_heroku_1.log @@ -1,28 +1,28 @@ INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.608172178268433 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0106346607208252 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.9605710506439209 REQUEST_DETAILS [('CSVFile', 'https://raw.githubusercontent.com/antoinecarme/TimeSeriesData/master/ozone-la.csv'), ('DateFormat', '%Y-%m'), ('Horizon', 12), ('Name', 'model1'), ('Present', '1968-08'), ('SignalVar', 'Ozone'), ('TimeVar', 'Month')] None DATASET_DETECTED_COLUMNS Index(['Month', 'Ozone'], dtype='object') DATASET_FINAL_COLUMNS Index(['Month', 'Ozone'], dtype='object') TRAIN_PARAMS (164, 2) Index(['Month', 'Ozone'], dtype='object') Month Ozone 12 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.379494905471802 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.327519416809082 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.9676716327667236 Split Transformation ... ForecastMAPE TestMAPE -0 None _Ozone ... 0.2113 0.2264 -1 None _Ozone ... 0.2113 0.2264 -2 None _Ozone ... 0.2235 0.2255 -3 None _Ozone ... 0.2235 0.2255 -4 None _Ozone ... 0.2289 0.2524 +0 None _Ozone ... 0.1982 0.2201 +1 None _Ozone ... 0.1983 0.2233 +2 None _Ozone ... 0.2121 0.2201 +3 None _Ozone ... 0.2148 0.2552 +4 None _Ozone ... 0.2148 0.2552 [5 rows x 8 columns] Forecast Columns Index(['Month', 'Ozone', 'row_number', 'Month_Normalized', '_Ozone', - '_Ozone_ConstantTrend', '_Ozone_ConstantTrend_residue', - '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear', - '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue', - '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(7)', - '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(7)_residue', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_zeroCycle', + '_Ozone_LinearTrend_residue_zeroCycle_residue', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(41)', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(41)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -42,47 +42,49 @@ memory usage: 4.2 KB None Forecasts Month Ozone Ozone_Forecast -164 1968-09-01 NaN 4.791309 -165 1968-10-01 NaN 3.985846 -166 1968-11-01 NaN 3.442976 -167 1968-12-01 NaN 2.278233 -168 1969-01-01 NaN 1.944084 -169 1969-02-01 NaN 2.043278 -170 1969-03-01 NaN 2.873911 -171 1969-04-01 NaN 3.405701 -172 1969-05-01 NaN 3.613842 -173 1969-06-01 NaN 4.501946 -174 1969-07-01 NaN 5.062317 -175 1969-08-01 NaN 4.862143 +164 1968-09-01 NaN 3.156650 +165 1968-10-01 NaN 2.565327 +166 1968-11-01 NaN 1.170904 +167 1968-12-01 NaN 0.769093 +168 1969-01-01 NaN 1.059295 +169 1969-02-01 NaN 0.295400 +170 1969-03-01 NaN 0.982530 +171 1969-04-01 NaN 1.971115 +172 1969-05-01 NaN 2.641341 +173 1969-06-01 NaN 2.647952 +174 1969-07-01 NaN 3.194151 +175 1969-08-01 NaN 3.647742 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1968-08-01 00:00:00" - ], - "TimeVariable": "Month" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1968-08-01 00:00:00" + ], + "TimeVariable": "Month" + }, + "Training_Signal_Length": 164 }, - "Training_Signal_Length": 164 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(7)", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "11", - "MAE": "0.653482682684735", - "MAPE": "0.2113", - "MASE": "0.7315", - "RMSE": "0.8224065159486033" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(41)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "46", + "MAE": "0.7190207683728788", + "MAPE": "0.1983", + "MASE": "0.8049", + "RMSE": "0.9679782641701922" + } } } @@ -91,7 +93,7 @@ Forecasts -{"Month":{"152":"1967-09-01T00:00:00.000Z","153":"1967-10-01T00:00:00.000Z","154":"1967-11-01T00:00:00.000Z","155":"1967-12-01T00:00:00.000Z","156":"1968-01-01T00:00:00.000Z","157":"1968-02-01T00:00:00.000Z","158":"1968-03-01T00:00:00.000Z","159":"1968-04-01T00:00:00.000Z","160":"1968-05-01T00:00:00.000Z","161":"1968-06-01T00:00:00.000Z","162":"1968-07-01T00:00:00.000Z","163":"1968-08-01T00:00:00.000Z","164":"1968-09-01T00:00:00.000Z","165":"1968-10-01T00:00:00.000Z","166":"1968-11-01T00:00:00.000Z","167":"1968-12-01T00:00:00.000Z","168":"1969-01-01T00:00:00.000Z","169":"1969-02-01T00:00:00.000Z","170":"1969-03-01T00:00:00.000Z","171":"1969-04-01T00:00:00.000Z","172":"1969-05-01T00:00:00.000Z","173":"1969-06-01T00:00:00.000Z","174":"1969-07-01T00:00:00.000Z","175":"1969-08-01T00:00:00.000Z"},"Ozone":{"152":3.7,"153":5.4,"154":3.0,"155":1.8,"156":2.1,"157":2.6,"158":2.8,"159":3.2,"160":3.5,"161":3.5,"162":4.9,"163":4.2,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null,"175":null},"Ozone_Forecast":{"152":5.398071081,"153":3.1719249802,"154":4.4232682463,"155":2.1464840204,"156":1.53564224,"157":2.7447200451,"158":2.4223917047,"159":3.8000898748,"160":3.4070593648,"161":4.4918929507,"162":4.5921381633,"163":4.8674475092,"164":4.7913086462,"165":3.9858456424,"166":3.4429760378,"167":2.2782326389,"168":1.9440837124,"169":2.0432775434,"170":2.8739114546,"171":3.4057011265,"172":3.6138419421,"173":4.5019461887,"174":5.0623168858,"175":4.8621434982}} +{"Month":{"152":"1967-09-01T00:00:00.000Z","153":"1967-10-01T00:00:00.000Z","154":"1967-11-01T00:00:00.000Z","155":"1967-12-01T00:00:00.000Z","156":"1968-01-01T00:00:00.000Z","157":"1968-02-01T00:00:00.000Z","158":"1968-03-01T00:00:00.000Z","159":"1968-04-01T00:00:00.000Z","160":"1968-05-01T00:00:00.000Z","161":"1968-06-01T00:00:00.000Z","162":"1968-07-01T00:00:00.000Z","163":"1968-08-01T00:00:00.000Z","164":"1968-09-01T00:00:00.000Z","165":"1968-10-01T00:00:00.000Z","166":"1968-11-01T00:00:00.000Z","167":"1968-12-01T00:00:00.000Z","168":"1969-01-01T00:00:00.000Z","169":"1969-02-01T00:00:00.000Z","170":"1969-03-01T00:00:00.000Z","171":"1969-04-01T00:00:00.000Z","172":"1969-05-01T00:00:00.000Z","173":"1969-06-01T00:00:00.000Z","174":"1969-07-01T00:00:00.000Z","175":"1969-08-01T00:00:00.000Z"},"Ozone":{"152":3.7,"153":5.4,"154":3.0,"155":1.8,"156":2.1,"157":2.6,"158":2.8,"159":3.2,"160":3.5,"161":3.5,"162":4.9,"163":4.2,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null,"175":null},"Ozone_Forecast":{"152":4.0989364648,"153":2.6444905443,"154":2.7643856351,"155":1.5979145552,"156":1.3735911163,"157":1.4649727686,"158":1.8083451837,"159":3.0312414901,"160":3.4213492373,"161":2.863817935,"162":3.4719029133,"163":4.9803706576,"164":3.1566497236,"165":2.5653272716,"166":1.1709040353,"167":0.7690926662,"168":1.0592948004,"169":0.295399654,"170":0.982530367,"171":1.9711151052,"172":2.6413414669,"173":2.6479524674,"174":3.1941505832,"175":3.6477418957}} diff --git a/tests/references/bugs_issue_32_bug_test_ozone_heroku_1_ws.log b/tests/references/bugs_issue_32_bug_test_ozone_heroku_1_ws.log index a0f582c81..71dc2b068 100644 --- a/tests/references/bugs_issue_32_bug_test_ozone_heroku_1_ws.log +++ b/tests/references/bugs_issue_32_bug_test_ozone_heroku_1_ws.log @@ -1,202 +1,2 @@ -{ - "model": { - "CSVFileInfo": [ - { - "Exogenous_Variables": null, - "Signal_Stats": { - "Length": "216", - "Max": "8.7", - "Mean": "3.77268518519", - "Min": "1.2", - "StdDev": "1.49163378914" - }, - "Time_Stats": { - "Max": "1972-12-01 00:00:00", - "Min": "1955-01-01 00:00:00" - } - } - ], - "ForecastData": [ - { - "Forecast": [ - 4.911522498669456, - 3.9753513790446946, - 3.682547570045993, - 2.4682794088306435, - 2.011011316366974, - 2.0396018116599217, - 2.906748543748365, - 3.4129178610354987, - 3.6501279370788264, - 4.517747052885375, - 5.078427940981653, - 4.918087119157342, - 5.479357333854169, - 4.543336231812355, - 3.9469631968780723, - 3.0554254993492314, - 2.319241040192546, - 2.4406136386992614, - 3.203874604346335, - 3.773141027439477, - 3.8589636967365117 - ], - "Forecast_Lower_Bound": [ - 3.0915431405631004, - 2.102818244747455, - 1.8864016479435624, - 0.901598450147125, - 0.3478905065963398, - 0.31646596646361047, - 1.2998543193564047, - 1.6561568920677074, - 2.011030825353033, - 2.758747829286188, - 3.403158757827995, - 3.2142025591883154, - 3.7456493772220885, - 2.849159811254789, - 2.2339918695354046, - 1.3384958386673695, - 0.5304641002666923, - 0.7623326841487212, - 1.3519709010183978, - 2.0008079922588022, - 2.0997210419729946 - ], - "Forecast_Upper_Bound": [ - 6.731501856775811, - 5.847884513341935, - 5.478693492148424, - 4.034960367514162, - 3.674132126137608, - 3.762737656856233, - 4.5136427681403255, - 5.16967883000329, - 5.28922504880462, - 6.2767462764845625, - 6.753697124135311, - 6.621971679126368, - 7.213065290486249, - 6.237512652369921, - 5.6599345242207395, - 4.772355160031093, - 4.1080179801184, - 4.118894593249801, - 5.055778307674272, - 5.545474062620152, - 5.618206351500029 - ], - "Time": [ - "1968-08-31 00:00:00", - "1968-09-30 00:00:00", - "1968-10-30 00:00:00", - "1968-11-29 00:00:00", - "1968-12-29 00:00:00", - "1969-01-28 00:00:00", - "1969-02-27 00:00:00", - "1969-03-29 00:00:00", - "1969-04-28 00:00:00", - "1969-05-28 00:00:00", - "1969-06-27 00:00:00", - 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","DateFormat":"The format of the date column , if it is a physcial date/time/datetime column (iso : yyyy-mm-dd by default), empty otherwise","ExogenousData":"A CSV file (URIs are also welcome!!!) containing a date column (same name and format as in CSVFile), and the columns containing the values of exogenous variables.","ExogenousVars":"(optional) a string giving the names of exogenous variables , separated by spaces.","Horizon":"number of future time periods to be predicted. The length of a period is inferred from data (most frequent difference between two consecutive dates)","Name":"Name used to identify the model in the API","Present":"date/time of the last known signal value. Predictions start after this date/time","SignalVar":"Name of the signal column to be predicted","TimeVar":"Name of the date/time column"}]}} diff --git a/tests/references/bugs_issue_34_issue_34.log b/tests/references/bugs_issue_34_issue_34.log index d45163be6..fd27cd7d8 100644 --- a/tests/references/bugs_issue_34_issue_34.log +++ b/tests/references/bugs_issue_34_issue_34.log @@ -1,41 +1,50 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_constant_12_log_0.0_100 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 38.85717058181763 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 47.192978382110596 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=4.571992533788244e-05 Max=2.192953994056831 Mean=1.4807174180258371 StdDev=0.5163795312772639 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=4.571992533788244e-05 Max=2.192953994056831 Mean=1.4807174180258371 StdDev=0.5163795312772639 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [ConstantTrend + Cycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)' [ConstantTrend + Cycle + AR] INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=4.9273 MAPE_Forecast=0.0124 MAPE_Test=0.0123 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.02 SMAPE_Forecast=0.0126 SMAPE_Test=0.0124 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0217 MASE_Forecast=0.0292 MASE_Test=0.0322 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.013063771343637669 L1_Forecast=0.014746551806329031 L1_Test=0.016246892069689895 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.020494169082185845 L2_Forecast=0.019147041242420594 L2_Test=0.019232151835886564 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=5.1206 MAPE_Forecast=0.0127 MAPE_Test=0.0126 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0205 SMAPE_Forecast=0.0129 SMAPE_Test=0.0127 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0221 MASE_Forecast=0.0298 MASE_Test=0.0331 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.013277604506547816 L1_Forecast=0.01503147446509271 L1_Test=0.01669745616636692 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.021418561803327685 L2_Forecast=0.0194261121107048 L2_Test=0.019997087300471054 INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.447554308305932 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE 12 0.19865233512924096 {0: -0.6043637204323232, 1: 0.2749056346329821, 2: 0.5414206778181057, 3: -0.6178674161456083, 4: -0.06712010876997465, 5: -1.0589126705417033, 6: -0.2984853692196847, 7: 0.28288904195056874, 8: 0.12163832101563687, 9: 0.41746367171833654, 10: 0.42315054426266463, 11: 0.6507142287160173} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag12 0.27540121681974394 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag24 0.1956740780994321 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag36 0.1514351070037859 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag48 0.1191060625998416 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag60 0.10204905002569499 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 0.05152418251385259 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag7 0.04159709653163844 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag2 0.03601526915585023 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag5 0.029898249004025528 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag9 0.02863519991951824 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag12 0.28263872643490795 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag24 0.1957138780858344 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag36 0.14744006485992045 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag48 0.11419967941577615 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag60 0.0956043058188272 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag1 0.062040252992983075 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag7 0.049871879068367604 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag2 0.04270585169676575 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag5 0.03648313752182348 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag9 0.03291660838736886 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.5817959308624268 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.8805086612701416 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', - 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)_residue', + 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)_residue', '_Signal_0.01_Trend', '_Signal_0.01_Trend_residue', '_Signal_0.01_Cycle', '_Signal_0.01_Cycle_residue', '_Signal_0.01_AR', '_Signal_0.01_AR_residue', '_Signal_0.01_TransformedForecast', @@ -56,59 +65,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 1.5460391116394103 - 1.508510910804266 1.5835673124745546] - [Timestamp('2002-10-10 00:00:00') nan 0.744806131179014 - 0.6838993597282131 0.8057129026298148] - [Timestamp('2002-10-11 00:00:00') nan 1.3420168366404424 - 1.2607334339915715 1.4233002392893133] - [Timestamp('2002-10-12 00:00:00') nan 1.8599988940873142 - 1.7614381231977678 1.9585596649768606] - [Timestamp('2002-10-13 00:00:00') nan 1.7122952031824343 - 1.5989335049694373 1.8256569013954314] - [Timestamp('2002-10-14 00:00:00') nan 1.9687497647716237 - 1.8428140011688388 2.0946855283744084] - [Timestamp('2002-10-15 00:00:00') nan 1.9848159663242742 - 1.848257181523624 2.1213747511249244] - [Timestamp('2002-10-16 00:00:00') nan 2.1800371822891096 - 2.034399087555347 2.325675277022872] - [Timestamp('2002-10-17 00:00:00') nan 1.0872318374862069 - 0.9337000782908313 1.2407635966815824] - [Timestamp('2002-10-18 00:00:00') nan 1.8416295031111813 - 1.6811902390800366 2.002068767142326] - [Timestamp('2002-10-19 00:00:00') nan 2.1022470831593454 - 1.9357937841012725 2.2687003822174185] - [Timestamp('2002-10-20 00:00:00') nan 1.0995973880257996 - 0.9279504288022772 1.2712443472493222]] + [[Timestamp('2002-10-09 00:00:00') nan 1.5444273884272732 + 1.5063522086902918 1.5825025681642546] + [Timestamp('2002-10-10 00:00:00') nan 0.7437243778959323 + 0.6816994443924407 0.8057493113994239] + [Timestamp('2002-10-11 00:00:00') nan 1.341469034456995 + 1.258820325359372 1.4241177435546182] + [Timestamp('2002-10-12 00:00:00') nan 1.8580366952555394 + 1.7580544897034385 1.9580189008076403] + [Timestamp('2002-10-13 00:00:00') nan 1.710330134756511 + 1.595605866315586 1.8250544031974358] + [Timestamp('2002-10-14 00:00:00') nan 1.9693084051720622 + 1.8421054236040415 2.096511386740083] + [Timestamp('2002-10-15 00:00:00') nan 1.984050446189983 + 1.8463489758825327 2.121751916497433] + [Timestamp('2002-10-16 00:00:00') nan 2.180074670894771 + 2.0334695613121405 2.3266797804774018] + [Timestamp('2002-10-17 00:00:00') nan 1.0844315432829514 + 0.93015604764679 1.2387070389191128] + [Timestamp('2002-10-18 00:00:00') nan 1.8397985635388783 + 1.6788611831138343 2.0007359439639223] + [Timestamp('2002-10-19 00:00:00') nan 2.101039917006269 + 1.934337994544259 2.267741839468279] + [Timestamp('2002-10-20 00:00:00') nan 1.0938678407063134 + 0.9222220033337838 1.265513678078843]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.014746551806329031", - "MAPE": "0.0124", - "MASE": "0.0292", - "RMSE": "0.019147041242420594" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.01503147446509271", + "MAPE": "0.0127", + "MASE": "0.0298", + "RMSE": "0.0194261121107048" + } } } @@ -117,7 +128,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":1.5378295318,"1001":0.7716046325,"1002":1.3643030253,"1003":1.8433026728,"1004":1.7120553603,"1005":1.9880944539,"1006":1.9777617872,"1007":2.1929539941,"1008":1.0983312152,"1009":1.8497246416,"1010":2.0999812704,"1011":1.1250626809,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":1.5439377784,"1001":0.7450269012,"1002":1.3332482792,"1003":1.8631688717,"1004":1.7063451737,"1005":1.967466783,"1006":1.9862661151,"1007":2.1767131014,"1008":1.0836545518,"1009":1.840047352,"1010":2.1008025533,"1011":1.0899652128,"1012":1.5460391116,"1013":0.7448061312,"1014":1.3420168366,"1015":1.8599988941,"1016":1.7122952032,"1017":1.9687497648,"1018":1.9848159663,"1019":2.1800371823,"1020":1.0872318375,"1021":1.8416295031,"1022":2.1022470832,"1023":1.099597388},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.5085109108,"1013":0.6838993597,"1014":1.260733434,"1015":1.7614381232,"1016":1.598933505,"1017":1.8428140012,"1018":1.8482571815,"1019":2.0343990876,"1020":0.9337000783,"1021":1.6811902391,"1022":1.9357937841,"1023":0.9279504288},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.5835673125,"1013":0.8057129026,"1014":1.4233002393,"1015":1.958559665,"1016":1.8256569014,"1017":2.0946855284,"1018":2.1213747511,"1019":2.325675277,"1020":1.2407635967,"1021":2.0020687671,"1022":2.2687003822,"1023":1.2712443472}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":1.5378295318,"1001":0.7716046325,"1002":1.3643030253,"1003":1.8433026728,"1004":1.7120553603,"1005":1.9880944539,"1006":1.9777617872,"1007":2.1929539941,"1008":1.0983312152,"1009":1.8497246416,"1010":2.0999812704,"1011":1.1250626809,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":1.5443827954,"1001":0.7437276184,"1002":1.3318846633,"1003":1.8632487145,"1004":1.7065898343,"1005":1.9644632885,"1006":1.98352269,"1007":2.17555645,"1008":1.0859083606,"1009":1.8370211928,"1010":2.1002610535,"1011":1.089149113,"1012":1.5444273884,"1013":0.7437243779,"1014":1.3414690345,"1015":1.8580366953,"1016":1.7103301348,"1017":1.9693084052,"1018":1.9840504462,"1019":2.1800746709,"1020":1.0844315433,"1021":1.8397985635,"1022":2.101039917,"1023":1.0938678407},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.5063522087,"1013":0.6816994444,"1014":1.2588203254,"1015":1.7580544897,"1016":1.5956058663,"1017":1.8421054236,"1018":1.8463489759,"1019":2.0334695613,"1020":0.9301560476,"1021":1.6788611831,"1022":1.9343379945,"1023":0.9222220033},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.5825025682,"1013":0.8057493114,"1014":1.4241177436,"1015":1.9580189008,"1016":1.8250544032,"1017":2.0965113867,"1018":2.1217519165,"1019":2.3266797805,"1020":1.2387070389,"1021":2.000735944,"1022":2.2677418395,"1023":1.2655136781}} diff --git a/tests/references/bugs_issue_34_issue_34_1.log b/tests/references/bugs_issue_34_issue_34_1.log index 15e5c5f82..5439bad1e 100644 --- a/tests/references/bugs_issue_34_issue_34_1.log +++ b/tests/references/bugs_issue_34_issue_34_1.log @@ -1,7 +1,5 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.5' -GENERATING_RANDOM_DATASET Signal_1024_D_0_constant_12_log_0.0_100 -TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.5' 13.520290851593018 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.5']' 7.579643726348877 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.5' Length=1012 Min=-0.9945154367282514 Max=3.2015395828134037 Mean=1.4876487862946526 StdDev=0.7211370240536645 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.5' Min=-0.9945154367282514 Max=3.2015395828134037 Mean=1.4876487862946526 StdDev=0.7211370240536645 @@ -11,28 +9,39 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.5_LinearTrend_residue_Seasonal_DayO INFO:pyaf.std:TREND_DETAIL '_Signal_0.5_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.5_LinearTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.5_LinearTrend_residue_Seasonal_DayOfWeek_residue_ARX(64)' [ARX] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.9013 MAPE_Forecast=0.5352 MAPE_Test=24.8776 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.9477 SMAPE_Forecast=0.769 SMAPE_Test=0.8091 -INFO:pyaf.std:MODEL_MASE MASE_Fit=1.1204 MASE_Forecast=1.1474 MASE_Test=0.8775 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.9136016222593273 L1_Forecast=0.8158577105448637 L1_Test=0.9261289779598654 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.0473380636125325 L2_Forecast=0.954441413654372 L2_Test=1.089352054586877 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=3.1544 MAPE_Forecast=1.0531 MAPE_Test=118.521 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.5842 SMAPE_Forecast=0.5382 SMAPE_Test=0.5369 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.163 MASE_Forecast=1.3775 MASE_Test=0.8496 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.9483305550038108 L1_Forecast=0.9794502435038672 L1_Test=0.8966781304335029 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.0890012968204301 L2_Forecast=1.1132661105465658 L2_Test=1.1331425654622298 INFO:pyaf.std:MODEL_COMPLEXITY 220 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.3032770973336083, array([0.32825275])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_0.5_LinearTrend_residue_Seasonal_DayOfWeek 0.040753651527020485 {5: 0.0023205649016722063, 6: 0.03621387331093007, 0: 0.09264540414889932, 1: 0.1119561876502918, 2: 0.06736110555713437, 3: 0.049579034213817263, 4: 0.02212370559494581} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 exog_73=0_Lag6 -0.8227200639816089 -INFO:pyaf.std:AR_MODEL_COEFF 2 exog_17=0_Lag9 0.7260203825399316 -INFO:pyaf.std:AR_MODEL_COEFF 3 exog_26=0_Lag8 -0.7131448043010956 -INFO:pyaf.std:AR_MODEL_COEFF 4 exog_1=0_Lag9 -0.7100025905451414 -INFO:pyaf.std:AR_MODEL_COEFF 5 exog_85=0_Lag1 0.6389386899901207 -INFO:pyaf.std:AR_MODEL_COEFF 6 exog_22=0_Lag2 -0.6314676766304455 -INFO:pyaf.std:AR_MODEL_COEFF 7 exog_68=0_Lag7 0.5962963481171157 -INFO:pyaf.std:AR_MODEL_COEFF 8 exog_26=0_Lag6 0.56539983955293 -INFO:pyaf.std:AR_MODEL_COEFF 9 exog_2=0_Lag9 -0.5613013438469814 -INFO:pyaf.std:AR_MODEL_COEFF 10 exog_11=0_Lag7 -0.5603749613416178 +INFO:pyaf.std:AR_MODEL_COEFF 1 exog_73=0_Lag6 -0.8151371321231737 +INFO:pyaf.std:AR_MODEL_COEFF 2 exog_26=0_Lag8 -0.7366217379691167 +INFO:pyaf.std:AR_MODEL_COEFF 3 exog_17=0_Lag9 0.7313156189348435 +INFO:pyaf.std:AR_MODEL_COEFF 4 exog_1=0_Lag9 -0.7255264700959593 +INFO:pyaf.std:AR_MODEL_COEFF 5 exog_85=0_Lag1 0.6325977250061062 +INFO:pyaf.std:AR_MODEL_COEFF 6 exog_22=0_Lag2 -0.6107970917825476 +INFO:pyaf.std:AR_MODEL_COEFF 7 exog_68=0_Lag7 0.5847160231908073 +INFO:pyaf.std:AR_MODEL_COEFF 8 exog_26=0_Lag6 0.5845507587353176 +INFO:pyaf.std:AR_MODEL_COEFF 9 exog_11=0_Lag7 -0.569226315609416 +INFO:pyaf.std:AR_MODEL_COEFF 10 exog_41=0_Lag1 0.5610294001036429 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 6.5806732177734375 +INFO:pyaf.std:START_FORECASTING '['Signal_0.5']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.5']' 3.4001011848449707 +GENERATING_RANDOM_DATASET Signal_1024_D_0_constant_12_log_0.0_100 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 Split Transformation ... ForecastMAPE TestMAPE -0 None _Signal_0.5 ... 0.5352 24.8776 +0 None _Signal_0.5 ... 1.0531 118.521 [1 rows x 8 columns] Forecast Columns Index(['Date', 'Signal_0.5', 'row_number', 'Date_Normalized', '_Signal_0.5', @@ -59,47 +68,49 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 24.1 KB None Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 1.0148154336255042] - [Timestamp('2002-10-10 00:00:00') nan 0.30157604940037186] - [Timestamp('2002-10-11 00:00:00') nan 0.7251520195379553] - [Timestamp('2002-10-12 00:00:00') nan 1.0776843519303931] - [Timestamp('2002-10-13 00:00:00') nan 0.9775883785004778] - [Timestamp('2002-10-14 00:00:00') nan 1.2567556397719808] - [Timestamp('2002-10-15 00:00:00') nan 0.8570892414059517] - [Timestamp('2002-10-16 00:00:00') nan 0.9581132533015386] - [Timestamp('2002-10-17 00:00:00') nan 0.6639234299728007] - [Timestamp('2002-10-18 00:00:00') nan 0.5332141062892182] - [Timestamp('2002-10-19 00:00:00') nan 1.4703425148290032] - [Timestamp('2002-10-20 00:00:00') nan 0.6205873816760921]] + [[Timestamp('2002-10-09 00:00:00') nan 2.6658868659779626] + [Timestamp('2002-10-10 00:00:00') nan 1.8999760011088316] + [Timestamp('2002-10-11 00:00:00') nan 2.500262811885732] + [Timestamp('2002-10-12 00:00:00') nan 2.8197781619834204] + [Timestamp('2002-10-13 00:00:00') nan 2.8536667807316456] + [Timestamp('2002-10-14 00:00:00') nan 3.0138775735963685] + [Timestamp('2002-10-15 00:00:00') nan 2.688422497087159] + [Timestamp('2002-10-16 00:00:00') nan 2.120396676725378] + [Timestamp('2002-10-17 00:00:00') nan 2.1272624333528363] + [Timestamp('2002-10-18 00:00:00') nan 2.1362888099002557] + [Timestamp('2002-10-19 00:00:00') nan 2.7265619235240846] + [Timestamp('2002-10-20 00:00:00') nan 2.0386214245597776]] { - "Dataset": { - "Signal": "Signal_0.5", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.5": { + "Dataset": { + "Signal": "Signal_0.5", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 + }, + "Model": { + "AR_Model": "ARX", + "Best_Decomposition": "_Signal_0.5_LinearTrend_residue_Seasonal_DayOfWeek_residue_ARX(64)", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "ARX", - "Best_Decomposition": "_Signal_0.5_LinearTrend_residue_Seasonal_DayOfWeek_residue_ARX(64)", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "220", - "MAE": "0.8158577105448637", - "MAPE": "0.5352", - "MASE": "1.1474", - "RMSE": "0.954441413654372" + "Model_Performance": { + "COMPLEXITY": "220", + "MAE": "0.9794502435038672", + "MAPE": "1.0531", + "MASE": "1.3775", + "RMSE": "1.1132661105465658" + } } } @@ -108,7 +119,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.5":{"1000":1.0995345169,"1001":0.0015476707,"1002":1.9643576455,"1003":1.4631446834,"1004":1.7642396869,"1005":2.6590409575,"1006":2.134227951,"1007":2.6890093241,"1008":0.4476406123,"1009":1.7287494394,"1010":2.8519572473,"1011":1.7260918237,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.5_Forecast":{"1000":0.9430684489,"1001":0.4555097688,"1002":0.103840956,"1003":1.2025296192,"1004":0.7919939504,"1005":1.4089225903,"1006":0.9057685812,"1007":1.8589703956,"1008":0.4115856161,"1009":0.72302695,"1010":1.1203777436,"1011":0.3983233993,"1012":1.0148154336,"1013":0.3015760494,"1014":0.7251520195,"1015":1.0776843519,"1016":0.9775883785,"1017":1.2567556398,"1018":0.8570892414,"1019":0.9581132533,"1020":0.66392343,"1021":0.5332141063,"1022":1.4703425148,"1023":0.6205873817}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.5":{"1000":1.0995345169,"1001":0.0015476707,"1002":1.9643576455,"1003":1.4631446834,"1004":1.7642396869,"1005":2.6590409575,"1006":2.134227951,"1007":2.6890093241,"1008":0.4476406123,"1009":1.7287494394,"1010":2.8519572473,"1011":1.7260918237,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.5_Forecast":{"1000":2.7341373712,"1001":2.1899584766,"1002":1.9033308796,"1003":3.0371612212,"1004":2.5791782629,"1005":3.1730170359,"1006":3.0125020577,"1007":2.8040143058,"1008":2.155231292,"1009":2.518660455,"1010":2.8731915364,"1011":2.1872426975,"1012":2.665886866,"1013":1.8999760011,"1014":2.5002628119,"1015":2.819778162,"1016":2.8536667807,"1017":3.0138775736,"1018":2.6884224971,"1019":2.1203966767,"1020":2.1272624334,"1021":2.1362888099,"1022":2.7265619235,"1023":2.0386214246}} diff --git a/tests/references/bugs_issue_34_test_artificial_1024__poly_7_12_100.log b/tests/references/bugs_issue_34_test_artificial_1024__poly_7_12_100.log index 21753af5b..428473245 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024__poly_7_12_100.log +++ b/tests/references/bugs_issue_34_test_artificial_1024__poly_7_12_100.log @@ -1,31 +1,41 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_poly_7_None_0.0_100 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 33.30438780784607 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 31.483452558517456 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=0.978510719611242 Max=8.242199134283469 Mean=4.511808556982061 StdDev=2.2724474826072507 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=0.978510719611242 Max=8.242199134283469 Mean=4.511808556982061 StdDev=2.2724474826072507 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [LinearTrend + Seasonal_DayOfWeek + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_LinearTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0027 MAPE_Forecast=0.0026 MAPE_Test=0.0024 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0027 SMAPE_Forecast=0.0026 SMAPE_Test=0.0024 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0025 MASE_Forecast=0.0027 MASE_Test=0.0025 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.008121753197262518 L1_Forecast=0.008838348271793792 L1_Test=0.008180843728764523 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.010134885775656619 L2_Forecast=0.010953824075188297 L2_Test=0.01013622337352375 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [PolyTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_PolyTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0035 MAPE_Forecast=0.0124 MAPE_Test=0.0228 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0035 SMAPE_Forecast=0.0126 SMAPE_Test=0.0233 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0032 MASE_Forecast=0.0115 MASE_Test=0.0204 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.010373898119329938 L1_Forecast=0.037459807854013795 L1_Test=0.0661692756000873 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.013018878970340696 L2_Forecast=0.040681117573338206 L2_Test=0.06684282541265579 INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (4.442309216374491, array([ 0.16286316, -0.03756318, -0.04794307])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_0.01_PolyTrend_residue_Seasonal_DayOfWeek -0.5992039869049448 {5: -0.6091311073685302, 6: -3.4666818962327834, 0: -0.6083082407720131, 1: 2.2484950458546944, 2: -2.0320498344437365, 3: 0.8213195255705847, 4: 3.6806144321421943} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5345625877380371 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.34458494186401367 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', - '_Signal_0.01_LinearTrend', '_Signal_0.01_LinearTrend_residue', - '_Signal_0.01_LinearTrend_residue_Seasonal_DayOfWeek', - '_Signal_0.01_LinearTrend_residue_Seasonal_DayOfWeek_residue', - '_Signal_0.01_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR', - '_Signal_0.01_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + 'Date_Normalized_^2', 'Date_Normalized_^3', '_Signal_0.01_PolyTrend', + '_Signal_0.01_PolyTrend_residue', + '_Signal_0.01_PolyTrend_residue_Seasonal_DayOfWeek', + '_Signal_0.01_PolyTrend_residue_Seasonal_DayOfWeek_residue', + '_Signal_0.01_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_Signal_0.01_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', '_Signal_0.01_Trend', '_Signal_0.01_Trend_residue', '_Signal_0.01_Cycle', '_Signal_0.01_Cycle_residue', '_Signal_0.01_AR', '_Signal_0.01_AR_residue', '_Signal_0.01_TransformedForecast', @@ -46,59 +56,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 2.5297677081885572 - 2.508298213001188 2.5512372033759263] - [Timestamp('2002-10-10 00:00:00') nan 5.384917748172628 - 5.3634482529852585 5.406387243359997] - [Timestamp('2002-10-11 00:00:00') nan 8.242815886620761 - 8.221346391433393 8.26428538180813] - [Timestamp('2002-10-12 00:00:00') nan 3.956009243946955 - 3.934539748759586 3.977478739134324] - [Timestamp('2002-10-13 00:00:00') nan 1.0969113945001734 - 1.0754418993128043 1.1183808896875425] - [Timestamp('2002-10-14 00:00:00') nan 3.955664594152352 - 3.9341950989649828 3.977134089339721] - [Timestamp('2002-10-15 00:00:00') nan 6.813437992325758 - 6.791968497138389 6.834907487513127] - [Timestamp('2002-10-16 00:00:00') nan 2.5304977000037994 - 2.5090282048164303 2.5519671951911684] - [Timestamp('2002-10-17 00:00:00') nan 5.38564773998787 5.364178244800501 - 5.407117235175239] - [Timestamp('2002-10-18 00:00:00') nan 8.243545878436004 - 8.222076383248636 8.265015373623372] - [Timestamp('2002-10-19 00:00:00') nan 3.956739235762197 - 3.935269740574828 3.978208730949566] - [Timestamp('2002-10-20 00:00:00') nan 1.0976413863154155 - 1.0761718911280465 1.1191108815027846]] + [[Timestamp('2002-10-09 00:00:00') nan 2.458863766381396 + 2.3791287759376534 2.538598756825139] + [Timestamp('2002-10-10 00:00:00') nan 5.312028744309163 5.23229375386542 + 5.391763734752907] + [Timestamp('2002-10-11 00:00:00') nan 8.17111857983899 8.091383589395246 + 8.250853570282732] + [Timestamp('2002-10-12 00:00:00') nan 3.881167279767309 + 3.801432289323566 3.9609022702110517] + [Timestamp('2002-10-13 00:00:00') nan 1.0234100402589785 + 0.9436750498152356 1.1031450307027213] + [Timestamp('2002-10-14 00:00:00') nan 3.881576554428608 + 3.801841563984865 3.9613115448723506] + [Timestamp('2002-10-15 00:00:00') nan 6.738172008553165 + 6.658437018109422 6.817906998996908] + [Timestamp('2002-10-16 00:00:00') nan 2.457418603977629 + 2.377683613533886 2.5371535944213717] + [Timestamp('2002-10-17 00:00:00') nan 5.310578747375946 + 5.230843756932202 5.390313737819689] + [Timestamp('2002-10-18 00:00:00') nan 8.169663744428707 + 8.089928753984964 8.24939873487245] + [Timestamp('2002-10-19 00:00:00') nan 3.879707601932345 + 3.7999726114886023 3.959442592376088] + [Timestamp('2002-10-20 00:00:00') nan 1.0219455160517201 + 0.9422105256079772 1.101680506495463]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_0.01_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.008838348271793792", - "MAPE": "0.0026", - "MASE": "0.0027", - "RMSE": "0.010953824075188297" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_0.01_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.037459807854013795", + "MAPE": "0.0124", + "MASE": "0.0115", + "RMSE": "0.040681117573338206" + } } } @@ -107,7 +119,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":8.2389495685,"1001":3.9364510294,"1002":1.0922903743,"1003":3.9481017692,"1004":6.79043888,"1005":2.5305029821,"1006":5.3804299309,"1007":8.2345059792,"1008":3.9672462632,"1009":1.1058235131,"1010":3.9480024827,"1011":6.8071896957,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":8.241355903,"1001":3.9545492603,"1002":1.0954514109,"1003":3.9542046105,"1004":6.8119780087,"1005":2.5290377164,"1006":5.3841877564,"1007":8.2420858948,"1008":3.9552792521,"1009":1.0961814027,"1010":3.9549346023,"1011":6.8127080005,"1012":2.5297677082,"1013":5.3849177482,"1014":8.2428158866,"1015":3.9560092439,"1016":1.0969113945,"1017":3.9556645942,"1018":6.8134379923,"1019":2.5304977,"1020":5.38564774,"1021":8.2435458784,"1022":3.9567392358,"1023":1.0976413863},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.508298213,"1013":5.363448253,"1014":8.2213463914,"1015":3.9345397488,"1016":1.0754418993,"1017":3.934195099,"1018":6.7919684971,"1019":2.5090282048,"1020":5.3641782448,"1021":8.2220763832,"1022":3.9352697406,"1023":1.0761718911},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.5512372034,"1013":5.4063872434,"1014":8.2642853818,"1015":3.9774787391,"1016":1.1183808897,"1017":3.9771340893,"1018":6.8349074875,"1019":2.5519671952,"1020":5.4071172352,"1021":8.2650153736,"1022":3.9782087309,"1023":1.1191108815}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":8.2389495685,"1001":3.9364510294,"1002":1.0922903743,"1003":3.9481017692,"1004":6.79043888,"1005":2.5305029821,"1006":5.3804299309,"1007":8.2345059792,"1008":3.9672462632,"1009":1.1058235131,"1010":3.9480024827,"1011":6.8071896957,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":8.1739270848,"1001":3.8839853867,"1002":1.026237757,"1003":3.8844138889,"1004":6.7410189687,"1005":2.4602751976,"1006":5.3134449824,"1007":8.1725396288,"1008":3.8825931435,"1009":1.0248407228,"1010":3.8830120596,"1011":6.7396123404,"1012":2.4588637664,"1013":5.3120287443,"1014":8.1711185798,"1015":3.8811672798,"1016":1.0234100403,"1017":3.8815765544,"1018":6.7381720086,"1019":2.457418604,"1020":5.3105787474,"1021":8.1696637444,"1022":3.8797076019,"1023":1.0219455161},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.3791287759,"1013":5.2322937539,"1014":8.0913835894,"1015":3.8014322893,"1016":0.9436750498,"1017":3.801841564,"1018":6.6584370181,"1019":2.3776836135,"1020":5.2308437569,"1021":8.089928754,"1022":3.7999726115,"1023":0.9422105256},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.5385987568,"1013":5.3917637348,"1014":8.2508535703,"1015":3.9609022702,"1016":1.1031450307,"1017":3.9613115449,"1018":6.817906999,"1019":2.5371535944,"1020":5.3903137378,"1021":8.2493987349,"1022":3.9594425924,"1023":1.1016805065}} diff --git a/tests/references/bugs_issue_34_test_artificial_1024_diff_poly_0__0.log b/tests/references/bugs_issue_34_test_artificial_1024_diff_poly_0__0.log index 5f27686b0..9395d94ee 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024_diff_poly_0__0.log +++ b/tests/references/bugs_issue_34_test_artificial_1024_diff_poly_0__0.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_poly_0_diff_0.0_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 14.095259666442871 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 19.004042148590088 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=-0.034405743277656255 Max=0.9876924379551518 Mean=0.0013069546095828674 StdDev=0.032562037513681336 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Signal_0.01' Min=0.9450404718182226 Max=1.3366298534475332 Mean=1.1297035909984083 StdDev=0.09581631424474732 @@ -16,10 +16,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.649 MASE_Forecast=0.7191 MASE_Test=0.7388 INFO:pyaf.std:MODEL_L1 L1_Fit=0.008100564685171027 L1_Forecast=0.0073183486792331 L1_Test=0.0066300421283013835 INFO:pyaf.std:MODEL_L2 L2_Fit=0.01053434835932809 L2_Forecast=0.009318472926502308 L2_Test=0.00960919904266602 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.077633278814644, array([0.05083609])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_Signal_0.01_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.1326522827148438 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.5017437934875488 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', 'CumSum_Signal_0.01', 'CumSum_Signal_0.01_LinearTrend', 'CumSum_Signal_0.01_LinearTrend_residue', @@ -75,31 +84,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Signal_0.01_LinearTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "Integration", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "0.0073183486792331", - "MAPE": "0.9891", - "MASE": "0.7191", - "RMSE": "0.009318472926502308" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Signal_0.01_LinearTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "Integration", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "0.0073183486792331", + "MAPE": "0.9891", + "MASE": "0.7191", + "RMSE": "0.009318472926502308" + } } } diff --git a/tests/references/bugs_issue_34_test_artificial_1024_diff_poly_0__100.log b/tests/references/bugs_issue_34_test_artificial_1024_diff_poly_0__100.log index 337e84f02..9b792affa 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024_diff_poly_0__100.log +++ b/tests/references/bugs_issue_34_test_artificial_1024_diff_poly_0__100.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_poly_0_diff_0.0_100 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 24.1153244972229 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 36.17159128189087 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=-0.034405743277656255 Max=0.9876924379551518 Mean=0.0013069546095828674 StdDev=0.032562037513681336 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Signal_0.01' Min=0.9450404718182226 Max=1.3366298534475332 Mean=1.1297035909984083 StdDev=0.09581631424474732 @@ -16,10 +16,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.649 MASE_Forecast=0.7191 MASE_Test=0.7388 INFO:pyaf.std:MODEL_L1 L1_Fit=0.008100564685171027 L1_Forecast=0.0073183486792331 L1_Test=0.0066300421283013835 INFO:pyaf.std:MODEL_L2 L2_Fit=0.01053434835932809 L2_Forecast=0.009318472926502308 L2_Test=0.00960919904266602 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.077633278814644, array([0.05083609])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_Signal_0.01_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.853203296661377 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.51350998878479 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', 'CumSum_Signal_0.01', 'CumSum_Signal_0.01_LinearTrend', 'CumSum_Signal_0.01_LinearTrend_residue', @@ -75,31 +84,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Signal_0.01_LinearTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "Integration", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "0.0073183486792331", - "MAPE": "0.9891", - "MASE": "0.7191", - "RMSE": "0.009318472926502308" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Signal_0.01_LinearTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "Integration", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "0.0073183486792331", + "MAPE": "0.9891", + "MASE": "0.7191", + "RMSE": "0.009318472926502308" + } } } diff --git a/tests/references/bugs_issue_34_test_artificial_1024_inv_constant_30__20.log b/tests/references/bugs_issue_34_test_artificial_1024_inv_constant_30__20.log index fb33b561b..566689205 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024_inv_constant_30__20.log +++ b/tests/references/bugs_issue_34_test_artificial_1024_inv_constant_30__20.log @@ -1,31 +1,40 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_constant_30_inv_0.0_20 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 40.69943165779114 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 59.552812814712524 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=0.08870370393155147 Max=1.0188957625478472 Mean=0.26027323341860925 StdDev=0.1905621630558238 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=0.08870370393155147 Max=1.0188957625478472 Mean=0.26027323341860925 StdDev=0.1905621630558238 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0417 MAPE_Forecast=0.0417 MAPE_Test=0.0458 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0414 SMAPE_Forecast=0.0413 SMAPE_Test=0.0465 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0426 MASE_Forecast=0.0441 MASE_Test=0.0316 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.007617977657276502 L1_Forecast=0.007843811019232651 L1_Test=0.00774813360404577 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.009601588626770843 L2_Forecast=0.00977001825024159 L2_Test=0.010062411763432958 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.04 MAPE_Forecast=0.0438 MAPE_Test=0.0476 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0398 SMAPE_Forecast=0.0435 SMAPE_Test=0.0485 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0409 MASE_Forecast=0.0459 MASE_Test=0.0312 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.007320908476567094 L1_Forecast=0.008167703299077808 L1_Test=0.007667517753186747 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.009541524370585365 L2_Forecast=0.010109529761904306 L2_Test=0.010676720026913465 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.2601633977364918 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE 60 -0.08269549309623431 {0: 0.3378243097402606, 1: 0.06906461940926245, 2: 0.015138133305166807, 3: -0.11663001784125165, 4: -0.1339045719837755, 5: -0.11018183948076116, 6: 0.24086725837639092, 7: -0.1398427541851442, 8: 0.11293915480847075, 9: -0.1382691453960283, 10: -0.14164396612663852, 11: -0.04674202061617572, 12: -0.14726629220576076, 13: 0.7406184521746384, 14: 0.11450368223667223, 15: 0.07095874205165012, 16: -0.14128615912781353, 17: -0.07075815704786775, 18: -0.14700990996134317, 19: -0.1074103915546647, 20: 0.16722693684364642, 21: -0.1460572837827579, 22: -0.06604305260439414, 23: 0.0416822954437967, 24: 0.007262630496355338, 25: -0.1263241311876809, 26: -0.11584722972255762, 27: -0.10170762392871224, 28: -0.11884282242169669, 29: 0.16924339562637664, 30: 0.3329866676923846, 31: 0.07798821490502927, 32: 0.010415566144349742, 33: -0.11546676237972212, 34: -0.13415187761947067, 35: -0.11178065563106404, 36: 0.23912926890671504, 37: -0.13925111512422766, 38: 0.11270769103784373, 39: -0.1463023735036203, 40: -0.13926328642918767, 41: -0.04413545375983852, 42: -0.15280236110549622, 43: 0.7411245525034479, 44: 0.11362706985741705, 45: 0.07365221905767633, 46: -0.14738719739991604, 47: -0.0713111879994355, 48: -0.14419666610351603, 49: -0.09943217639190266, 50: 0.1639856893116282, 51: -0.14776813250354565, 52: -0.06077057291198398, 53: 0.03507255377027013, 54: 0.02020134346881164, 55: -0.12190832649982508, 56: -0.1179896358280551, 57: -0.10109980903776172, 58: -0.1112864203903666, 59: 0.1684735573210957} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5151546001434326 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.49061131477355957 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', - 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_0.01_Trend', '_Signal_0.01_Trend_residue', '_Signal_0.01_Cycle', '_Signal_0.01_Cycle_residue', '_Signal_0.01_AR', '_Signal_0.01_AR_residue', '_Signal_0.01_TransformedForecast', @@ -46,59 +55,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 0.19801499101751258 - 0.17886575524703907 0.21716422678798608] - [Timestamp('2002-10-10 00:00:00') nan 0.30054279521687044 - 0.28139355944639693 0.31969203098734394] - [Timestamp('2002-10-11 00:00:00') nan 0.27443658745040456 - 0.25528735167993105 0.29358582322087806] - [Timestamp('2002-10-12 00:00:00') nan 0.13604496429619012 - 0.1168957285257166 0.15519420006666362] - [Timestamp('2002-10-13 00:00:00') nan 0.14321865357687755 - 0.12406941780640403 0.16236788934735105] - [Timestamp('2002-10-14 00:00:00') nan 0.1603739199584705 - 0.141224684187997 0.179523155728944] - [Timestamp('2002-10-15 00:00:00') nan 0.14439969638339545 - 0.12525046061292194 0.16354893215386895] - [Timestamp('2002-10-16 00:00:00') nan 0.4296133468852403 - 0.41046411111476677 0.4487625826557138] - [Timestamp('2002-10-17 00:00:00') nan 0.5971012040516628 - 0.5779519682811892 0.6162504398221363] - [Timestamp('2002-10-18 00:00:00') nan 0.3334814609698736 - 0.3143322251994001 0.3526306967403471] - [Timestamp('2002-10-19 00:00:00') nan 0.2731290011608949 - 0.2539797653904214 0.2922782369313684] - [Timestamp('2002-10-20 00:00:00') nan 0.14650306305972177 - 0.12735382728924827 0.16565229883019528]] + [[Timestamp('2002-10-09 00:00:00') nan 0.1993928248245078 + 0.17957814649117534 0.21920750315784024] + [Timestamp('2002-10-10 00:00:00') nan 0.2952359515067619 + 0.27542127317342946 0.31505062984009435] + [Timestamp('2002-10-11 00:00:00') nan 0.2803647412053034 + 0.26055006287197097 0.30017941953863586] + [Timestamp('2002-10-12 00:00:00') nan 0.1382550712366667 + 0.11844039290333425 0.15806974956999914] + [Timestamp('2002-10-13 00:00:00') nan 0.14217376190843667 + 0.12235908357510422 0.16198844024176912] + [Timestamp('2002-10-14 00:00:00') nan 0.15906358869873005 + 0.1392489103653976 0.1788782670320625] + [Timestamp('2002-10-15 00:00:00') nan 0.14887697734612518 + 0.12906229901279273 0.16869165567945762] + [Timestamp('2002-10-16 00:00:00') nan 0.4286369550575875 + 0.40882227672425503 0.4484516333909199] + [Timestamp('2002-10-17 00:00:00') nan 0.5979877074767523 + 0.57817302914342 0.6178023858100847] + [Timestamp('2002-10-18 00:00:00') nan 0.3292280171457542 + 0.3094133388124218 0.3490426954790867] + [Timestamp('2002-10-19 00:00:00') nan 0.27530153104165855 + 0.2554868527083261 0.295116209374991] + [Timestamp('2002-10-20 00:00:00') nan 0.14353337989524012 + 0.12371870156190767 0.16334805822857257]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.007843811019232651", - "MAPE": "0.0417", - "MASE": "0.0441", - "RMSE": "0.00977001825024159" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.008167703299077808", + "MAPE": "0.0438", + "MASE": "0.0459", + "RMSE": "0.010109529761904306" + } } } @@ -107,7 +118,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":0.1265020446,"1001":0.1954270832,"1002":0.1087897692,"1003":1.0012578741,"1004":0.3767752145,"1005":0.3281434476,"1006":0.1367923094,"1007":0.2028725088,"1008":0.1083501207,"1009":0.1632129098,"1010":0.4215827912,"1011":0.1074045744,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":0.1197797162,"1001":0.2152193271,"1002":0.1086968061,"1003":0.9999524613,"1004":0.3742865821,"1005":0.3326541176,"1006":0.1170162958,"1007":0.1884303574,"1008":0.1153422155,"1009":0.1560421242,"1010":0.4276459816,"1011":0.1110256915,"1012":0.198014991,"1013":0.3005427952,"1014":0.2744365875,"1015":0.1360449643,"1016":0.1432186536,"1017":0.16037392,"1018":0.1443996964,"1019":0.4296133469,"1020":0.5971012041,"1021":0.333481461,"1022":0.2731290012,"1023":0.1465030631},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":0.1788657552,"1013":0.2813935594,"1014":0.2552873517,"1015":0.1168957285,"1016":0.1240694178,"1017":0.1412246842,"1018":0.1252504606,"1019":0.4104641111,"1020":0.5779519683,"1021":0.3143322252,"1022":0.2539797654,"1023":0.1273538273},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":0.2171642268,"1013":0.319692031,"1014":0.2935858232,"1015":0.1551942001,"1016":0.1623678893,"1017":0.1795231557,"1018":0.1635489322,"1019":0.4487625827,"1020":0.6162504398,"1021":0.3526306967,"1022":0.2922782369,"1023":0.1656522988}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":0.1265020446,"1001":0.1954270832,"1002":0.1087897692,"1003":1.0012578741,"1004":0.3767752145,"1005":0.3281434476,"1006":0.1367923094,"1007":0.2028725088,"1008":0.1083501207,"1009":0.1632129098,"1010":0.4215827912,"1011":0.1074045744,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":0.1209001113,"1001":0.216027944,"1002":0.1073610366,"1003":1.0012879502,"1004":0.3737904676,"1005":0.3338156168,"1006":0.1127762003,"1007":0.1888522097,"1008":0.1159667316,"1009":0.1607312213,"1010":0.424149087,"1011":0.1123952652,"1012":0.1993928248,"1013":0.2952359515,"1014":0.2803647412,"1015":0.1382550712,"1016":0.1421737619,"1017":0.1590635887,"1018":0.1488769773,"1019":0.4286369551,"1020":0.5979877075,"1021":0.3292280171,"1022":0.275301531,"1023":0.1435333799},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":0.1795781465,"1013":0.2754212732,"1014":0.2605500629,"1015":0.1184403929,"1016":0.1223590836,"1017":0.1392489104,"1018":0.129062299,"1019":0.4088222767,"1020":0.5781730291,"1021":0.3094133388,"1022":0.2554868527,"1023":0.1237187016},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":0.2192075032,"1013":0.3150506298,"1014":0.3001794195,"1015":0.1580697496,"1016":0.1619884402,"1017":0.178878267,"1018":0.1686916557,"1019":0.4484516334,"1020":0.6178023858,"1021":0.3490426955,"1022":0.2951162094,"1023":0.1633480582}} diff --git a/tests/references/bugs_issue_34_test_artificial_1024_inv_linear_7_12_100.log b/tests/references/bugs_issue_34_test_artificial_1024_inv_linear_7_12_100.log index 16c441157..882070c67 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024_inv_linear_7_12_100.log +++ b/tests/references/bugs_issue_34_test_artificial_1024_inv_linear_7_12_100.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_linear_7_inv_0.0_100 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 34.59473729133606 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 40.83157396316528 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=0.09825838271568212 Max=1.008573235906052 Mean=0.33345418708293995 StdDev=0.2692131253141072 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=0.09825838271568212 Max=1.008573235906052 Mean=0.33345418708293995 StdDev=0.2692131253141072 @@ -10,16 +10,25 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_Seasonal_D INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0365 MAPE_Forecast=0.0418 MAPE_Test=0.0328 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0363 SMAPE_Forecast=0.0414 SMAPE_Test=0.0324 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0293 MASE_Forecast=0.0396 MASE_Test=0.0323 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.009243079664910241 L1_Forecast=0.011889388635319622 L1_Test=0.010963796425181874 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.011935332659166444 L2_Forecast=0.016358033107965502 L2_Test=0.01668711666045733 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0364 MAPE_Forecast=0.0415 MAPE_Test=0.0319 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0362 SMAPE_Forecast=0.0411 SMAPE_Test=0.0316 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0293 MASE_Forecast=0.0382 MASE_Test=0.0308 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.009218758867513574 L1_Forecast=0.011477153539941524 L1_Test=0.010477265241786942 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.012003899313357979 L2_Forecast=0.015497043204011752 L2_Test=0.015574784516890735 INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.3350851588169763 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek -0.08616718582402916 {5: -0.07834337430345356, 6: 0.6252105901599434, 0: -0.07906210383354334, 1: -0.18809006884480742, 2: 0.07280155838646526, 3: -0.14829621486475822, 4: -0.2116018076658721} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.717724084854126 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.4839956760406494 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', '_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek', @@ -46,59 +55,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 0.4077784834832371 - 0.3757167385916247 0.4398402283748495] - [Timestamp('2002-10-10 00:00:00') nan 0.18775213915614017 - 0.1556903942645278 0.21981388404775254] - [Timestamp('2002-10-11 00:00:00') nan 0.12276207460107869 - 0.0907003297094663 0.15482381949269108] - [Timestamp('2002-10-12 00:00:00') nan 0.2561631121374214 - 0.22410136724580898 0.2882248570290338] - [Timestamp('2002-10-13 00:00:00') nan 0.9633472942085168 - 0.9312855493169044 0.9954090391001292] - [Timestamp('2002-10-14 00:00:00') nan 0.2555969010440387 - 0.2235351561524263 0.2876586459356511] - [Timestamp('2002-10-15 00:00:00') nan 0.14737733438040151 - 0.11531558948878913 0.1794390792720139] - [Timestamp('2002-10-16 00:00:00') nan 0.4077784834832371 - 0.3757167385916247 0.4398402283748495] - [Timestamp('2002-10-17 00:00:00') nan 0.18775213915614017 - 0.1556903942645278 0.21981388404775254] - [Timestamp('2002-10-18 00:00:00') nan 0.12276207460107869 - 0.0907003297094663 0.15482381949269108] - [Timestamp('2002-10-19 00:00:00') nan 0.2561631121374214 - 0.22410136724580898 0.2882248570290338] - [Timestamp('2002-10-20 00:00:00') nan 0.9633472942085168 - 0.9312855493169044 0.9954090391001292]] + [[Timestamp('2002-10-09 00:00:00') nan 0.4078867172034416 + 0.3775125125235786 0.4382609218833046] + [Timestamp('2002-10-10 00:00:00') nan 0.1867889439522181 + 0.15641473927235505 0.21716314863208114] + [Timestamp('2002-10-11 00:00:00') nan 0.1234833511511042 + 0.09310914647124117 0.15385755583096725] + [Timestamp('2002-10-12 00:00:00') nan 0.25674178451352275 + 0.2263675798336597 0.28711598919338577] + [Timestamp('2002-10-13 00:00:00') nan 0.9602957489769197 + 0.9299215442970566 0.9906699536567828] + [Timestamp('2002-10-14 00:00:00') nan 0.256023054983433 + 0.22564885030356993 0.286397259663296] + [Timestamp('2002-10-15 00:00:00') nan 0.1469950899721689 + 0.11662088529230587 0.17736929465203194] + [Timestamp('2002-10-16 00:00:00') nan 0.4078867172034416 + 0.3775125125235786 0.4382609218833046] + [Timestamp('2002-10-17 00:00:00') nan 0.1867889439522181 + 0.15641473927235505 0.21716314863208114] + [Timestamp('2002-10-18 00:00:00') nan 0.1234833511511042 + 0.09310914647124117 0.15385755583096725] + [Timestamp('2002-10-19 00:00:00') nan 0.25674178451352275 + 0.2263675798336597 0.28711598919338577] + [Timestamp('2002-10-20 00:00:00') nan 0.9602957489769197 + 0.9299215442970566 0.9906699536567828]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "0.011889388635319622", - "MAPE": "0.0418", - "MASE": "0.0396", - "RMSE": "0.016358033107965502" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.011477153539941524", + "MAPE": "0.0415", + "MASE": "0.0382", + "RMSE": "0.015497043204011752" + } } } @@ -107,7 +118,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":0.128672394,"1001":0.243731006,"1002":0.9214186023,"1003":0.2551921782,"1004":0.1338783882,"1005":0.4097390617,"1006":0.1913218306,"1007":0.123596153,"1008":0.2738627576,"1009":0.9338029553,"1010":0.2544294131,"1011":0.1499922845,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":0.1227620746,"1001":0.2561631121,"1002":0.9633472942,"1003":0.255596901,"1004":0.1473773344,"1005":0.4077784835,"1006":0.1877521392,"1007":0.1227620746,"1008":0.2561631121,"1009":0.9633472942,"1010":0.255596901,"1011":0.1473773344,"1012":0.4077784835,"1013":0.1877521392,"1014":0.1227620746,"1015":0.2561631121,"1016":0.9633472942,"1017":0.255596901,"1018":0.1473773344,"1019":0.4077784835,"1020":0.1877521392,"1021":0.1227620746,"1022":0.2561631121,"1023":0.9633472942},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":0.3757167386,"1013":0.1556903943,"1014":0.0907003297,"1015":0.2241013672,"1016":0.9312855493,"1017":0.2235351562,"1018":0.1153155895,"1019":0.3757167386,"1020":0.1556903943,"1021":0.0907003297,"1022":0.2241013672,"1023":0.9312855493},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":0.4398402284,"1013":0.219813884,"1014":0.1548238195,"1015":0.288224857,"1016":0.9954090391,"1017":0.2876586459,"1018":0.1794390793,"1019":0.4398402284,"1020":0.219813884,"1021":0.1548238195,"1022":0.288224857,"1023":0.9954090391}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":0.128672394,"1001":0.243731006,"1002":0.9214186023,"1003":0.2551921782,"1004":0.1338783882,"1005":0.4097390617,"1006":0.1913218306,"1007":0.123596153,"1008":0.2738627576,"1009":0.9338029553,"1010":0.2544294131,"1011":0.1499922845,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":0.1234833512,"1001":0.2567417845,"1002":0.960295749,"1003":0.256023055,"1004":0.14699509,"1005":0.4078867172,"1006":0.186788944,"1007":0.1234833512,"1008":0.2567417845,"1009":0.960295749,"1010":0.256023055,"1011":0.14699509,"1012":0.4078867172,"1013":0.186788944,"1014":0.1234833512,"1015":0.2567417845,"1016":0.960295749,"1017":0.256023055,"1018":0.14699509,"1019":0.4078867172,"1020":0.186788944,"1021":0.1234833512,"1022":0.2567417845,"1023":0.960295749},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":0.3775125125,"1013":0.1564147393,"1014":0.0931091465,"1015":0.2263675798,"1016":0.9299215443,"1017":0.2256488503,"1018":0.1166208853,"1019":0.3775125125,"1020":0.1564147393,"1021":0.0931091465,"1022":0.2263675798,"1023":0.9299215443},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":0.4382609219,"1013":0.2171631486,"1014":0.1538575558,"1015":0.2871159892,"1016":0.9906699537,"1017":0.2863972597,"1018":0.1773692947,"1019":0.4382609219,"1020":0.2171631486,"1021":0.1538575558,"1022":0.2871159892,"1023":0.9906699537}} diff --git a/tests/references/bugs_issue_34_test_artificial_1024_log_linear_30_12_20.log b/tests/references/bugs_issue_34_test_artificial_1024_log_linear_30_12_20.log index 163793eaf..ee1a23e06 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024_log_linear_30_12_20.log +++ b/tests/references/bugs_issue_34_test_artificial_1024_log_linear_30_12_20.log @@ -1,41 +1,51 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_linear_30_log_0.0_20 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 42.464802503585815 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 63.22091102600098 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=-0.020492319132446663 Max=2.5120315971507767 Mean=1.8384744686334862 StdDev=0.46521979729623253 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=-0.020492319132446663 Max=2.5120315971507767 Mean=1.8384744686334862 StdDev=0.46521979729623253 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [ConstantTrend + Cycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0422 MAPE_Forecast=0.0145 MAPE_Test=0.0154 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0172 SMAPE_Forecast=0.0147 SMAPE_Test=0.0157 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.041 MASE_Forecast=0.0681 MASE_Test=0.0596 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02096447301613301 L1_Forecast=0.026391367917222155 L1_Test=0.028749086535437247 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.03594264864775236 L2_Forecast=0.03414170229453914 L2_Test=0.040855882564566036 -INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)' [PolyTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_PolyTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1037 MAPE_Forecast=0.0092 MAPE_Test=0.0107 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0281 SMAPE_Forecast=0.0092 SMAPE_Test=0.0108 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0743 MASE_Forecast=0.0465 MASE_Test=0.0462 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.037969768184030854 L1_Forecast=0.018015927482562935 L1_Test=0.022292193704344487 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09956119134557317 L2_Forecast=0.022189447564016054 L2_Test=0.027578186896401652 +INFO:pyaf.std:MODEL_COMPLEXITY 80 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (1.5512770968011926, array([ 0.46229959, 0.06800685, -0.08151274])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_0.01_PolyTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag30 0.4272137732042773 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag60 0.22636560160465818 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag7 0.17534818628258342 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 0.1615131951895506 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag2 0.13980389320548559 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag26 -0.09750054231301733 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag5 0.07512186729675371 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag31 -0.07048031925772599 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag17 0.0630970886480771 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag6 0.06173415037390534 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag30 0.7915076838334318 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag8 -0.26243073947552253 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag37 -0.25075716386216185 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag7 0.24963023631945802 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag4 -0.23411690979925964 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag38 0.22252381503362156 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag2 0.18378077533695203 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag32 -0.17661630092811198 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag31 -0.17515384663709943 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag34 0.17054329192684015 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.3875443935394287 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.8352773189544678 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', - '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', - 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)_residue', + 'Date_Normalized_^2', 'Date_Normalized_^3', '_Signal_0.01_PolyTrend', + '_Signal_0.01_PolyTrend_residue', + '_Signal_0.01_PolyTrend_residue_zeroCycle', + '_Signal_0.01_PolyTrend_residue_zeroCycle_residue', + '_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)', + '_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)_residue', '_Signal_0.01_Trend', '_Signal_0.01_Trend_residue', '_Signal_0.01_Cycle', '_Signal_0.01_Cycle_residue', '_Signal_0.01_AR', '_Signal_0.01_AR_residue', '_Signal_0.01_TransformedForecast', @@ -56,59 +66,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 2.0853440142247677 - 2.018426277727471 2.1522617507220643] - [Timestamp('2002-10-10 00:00:00') nan 1.806393146609379 - 1.700020193505001 1.912766099713757] - [Timestamp('2002-10-11 00:00:00') nan 1.8545423091998026 - 1.7162575108665974 1.9928271075330077] - [Timestamp('2002-10-12 00:00:00') nan 2.3295082114466426 - 2.164372781392983 2.494643641500302] - [Timestamp('2002-10-13 00:00:00') nan 2.2869199087444807 - 2.098768271240035 2.4750715462489263] - [Timestamp('2002-10-14 00:00:00') nan 2.1894992180614943 - 1.9814268336840388 2.39757160243895] - [Timestamp('2002-10-15 00:00:00') nan 2.269513989552091 - 2.0439665916000624 2.495061387504119] - [Timestamp('2002-10-16 00:00:00') nan 1.6448137433786627 - 1.4037006046784701 1.8859268820788553] - [Timestamp('2002-10-17 00:00:00') nan 1.454655288747019 - 1.199654800367159 1.709655777126879] - [Timestamp('2002-10-18 00:00:00') nan 1.7483820046940537 - 1.4810880341364765 2.015675975251631] - [Timestamp('2002-10-19 00:00:00') nan 1.8303948889779118 - 1.5523467077885715 2.108443070167252] - [Timestamp('2002-10-20 00:00:00') nan 2.274955454983396 - 1.9874603472775623 2.56245056268923]] + [[Timestamp('2002-10-09 00:00:00') nan 2.08565947082169 2.042168153596218 + 2.1291507880471614] + [Timestamp('2002-10-10 00:00:00') nan 1.8528943882981461 + 1.7863501244393165 1.9194386521569757] + [Timestamp('2002-10-11 00:00:00') nan 1.9213969305401524 + 1.827481008821915 2.0153128522583894] + [Timestamp('2002-10-12 00:00:00') nan 2.354640496745186 + 2.2132774282186363 2.496003565271736] + [Timestamp('2002-10-13 00:00:00') nan 2.3298362320416683 + 2.0911546612950045 2.568517802788332] + [Timestamp('2002-10-14 00:00:00') nan 2.226407021154331 + 1.737798130404899 2.7150159119037633] + [Timestamp('2002-10-15 00:00:00') nan 2.335124263362758 + 1.155517581696196 3.51473094502932] + [Timestamp('2002-10-16 00:00:00') nan 1.7016274675108345 + -1.0692925998392562 4.472547534860925] + [Timestamp('2002-10-17 00:00:00') nan 1.5931926584596257 + -4.825440068887965 8.011825385807217] + [Timestamp('2002-10-18 00:00:00') nan 1.8173008012407623 + -13.394061809285649 17.028663411767173] + [Timestamp('2002-10-19 00:00:00') nan 1.9315998618110795 + -35.527005274069786 39.390204997691946] + [Timestamp('2002-10-20 00:00:00') nan 2.331897040360673 + -90.7015547012198 95.36534878194115]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.026391367917222155", - "MAPE": "0.0145", - "MASE": "0.0681", - "RMSE": "0.03414170229453914" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "80", + "MAE": "0.018015927482562935", + "MAPE": "0.0092", + "MASE": "0.0465", + "RMSE": "0.022189447564016054" + } } } @@ -117,7 +129,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":2.4844169434,"1001":2.0388120016,"1002":2.4913388596,"1003":1.3879103463,"1004":1.7449972623,"1005":1.8058753335,"1006":2.435103794,"1007":2.1218726601,"1008":2.4666197377,"1009":2.2493226203,"1010":1.6763252888,"1011":2.487441118,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":2.4261479675,"1001":2.0332962963,"1002":2.5169522304,"1003":1.2930608089,"1004":1.6731927577,"1005":1.8047902007,"1006":2.4641205106,"1007":2.1293420171,"1008":2.4758384655,"1009":2.245275028,"1010":1.6632037045,"1011":2.5124189517,"1012":2.0853440142,"1013":1.8063931466,"1014":1.8545423092,"1015":2.3295082114,"1016":2.2869199087,"1017":2.1894992181,"1018":2.2695139896,"1019":1.6448137434,"1020":1.4546552887,"1021":1.7483820047,"1022":1.830394889,"1023":2.274955455},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.0184262777,"1013":1.7000201935,"1014":1.7162575109,"1015":2.1643727814,"1016":2.0987682712,"1017":1.9814268337,"1018":2.0439665916,"1019":1.4037006047,"1020":1.1996548004,"1021":1.4810880341,"1022":1.5523467078,"1023":1.9874603473},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.1522617507,"1013":1.9127660997,"1014":1.9928271075,"1015":2.4946436415,"1016":2.4750715462,"1017":2.3975716024,"1018":2.4950613875,"1019":1.8859268821,"1020":1.7096557771,"1021":2.0156759753,"1022":2.1084430702,"1023":2.5624505627}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":2.4844169434,"1001":2.0388120016,"1002":2.4913388596,"1003":1.3879103463,"1004":1.7449972623,"1005":1.8058753335,"1006":2.435103794,"1007":2.1218726601,"1008":2.4666197377,"1009":2.2493226203,"1010":1.6763252888,"1011":2.487441118,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":2.4404873905,"1001":2.0554332245,"1002":2.4971019122,"1003":1.3791754348,"1004":1.6986799922,"1005":1.7680132113,"1006":2.4213414278,"1007":2.1135349588,"1008":2.4207962676,"1009":2.2211229573,"1010":1.6809092208,"1011":2.4950121775,"1012":2.0856594708,"1013":1.8528943883,"1014":1.9213969305,"1015":2.3546404967,"1016":2.329836232,"1017":2.2264070212,"1018":2.3351242634,"1019":1.7016274675,"1020":1.5931926585,"1021":1.8173008012,"1022":1.9315998618,"1023":2.3318970404},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.0421681536,"1013":1.7863501244,"1014":1.8274810088,"1015":2.2132774282,"1016":2.0911546613,"1017":1.7377981304,"1018":1.1555175817,"1019":-1.0692925998,"1020":-4.8254400689,"1021":-13.3940618093,"1022":-35.5270052741,"1023":-90.7015547012},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.129150788,"1013":1.9194386522,"1014":2.0153128523,"1015":2.4960035653,"1016":2.5685178028,"1017":2.7150159119,"1018":3.514730945,"1019":4.4725475349,"1020":8.0118253858,"1021":17.0286634118,"1022":39.3902049977,"1023":95.3653487819}} diff --git a/tests/references/bugs_issue_34_test_artificial_1024_log_linear_5_12_100.log b/tests/references/bugs_issue_34_test_artificial_1024_log_linear_5_12_100.log index 2245f530a..152e86d82 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024_log_linear_5_12_100.log +++ b/tests/references/bugs_issue_34_test_artificial_1024_log_linear_5_12_100.log @@ -1,41 +1,50 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_linear_5_log_0.0_100 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 35.99029469490051 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 48.87701106071472 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=-0.012896925465742856 Max=2.0045413019174334 Mean=1.308400198941626 StdDev=0.6398671026728568 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=-0.012896925465742856 Max=2.0045413019174334 Mean=1.308400198941626 StdDev=0.6398671026728568 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [ConstantTrend + Cycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)' [ConstantTrend + Cycle + AR] INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3035 MAPE_Forecast=0.0194 MAPE_Test=0.0213 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0464 SMAPE_Forecast=0.0202 SMAPE_Test=0.0221 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0098 MASE_Forecast=0.0114 MASE_Test=0.0113 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.00916384954276328 L1_Forecast=0.010146044480158802 L1_Test=0.01045717135450403 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.012656179006503275 L2_Forecast=0.013442076637214934 L2_Test=0.013426800682519799 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3081 MAPE_Forecast=0.0192 MAPE_Test=0.0212 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0464 SMAPE_Forecast=0.0199 SMAPE_Test=0.0219 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0098 MASE_Forecast=0.0114 MASE_Test=0.0115 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.009179196364323046 L1_Forecast=0.010150783754375406 L1_Test=0.010652105887816019 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.012702785688931604 L2_Forecast=0.013410198870405777 L2_Test=0.013374503696437851 INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.2979535126583446 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE 5 0.32252196732395133 {0: 0.33411689700317193, 1: -1.1812541197232085, 2: 0.3346956682788542, 3: -0.16257247361768223, 4: 0.6681144859297448} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag5 0.09684124495568594 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag10 0.08966276267009138 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag15 0.08628963007916446 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag20 0.07550020462801434 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag25 0.07266728495333152 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag30 0.07095468900499159 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag35 0.06823326543578928 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag40 0.06186143429242318 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag50 0.05933004849921957 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag45 0.05858304117343067 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag5 0.09767218208288354 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag10 0.09028206003083158 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag15 0.08671187891310768 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag20 0.07576043845254243 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag25 0.07274246165508644 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag30 0.07088224847150593 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag35 0.06808946615717137 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag40 0.06169062749335781 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag50 0.059006919124037785 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag45 0.0583408135591918 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.8132612705230713 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 1.311781406402588 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', - 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)_residue', + 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)_residue', '_Signal_0.01_Trend', '_Signal_0.01_Trend_residue', '_Signal_0.01_Cycle', '_Signal_0.01_Cycle_residue', '_Signal_0.01_AR', '_Signal_0.01_AR_residue', '_Signal_0.01_TransformedForecast', @@ -56,59 +65,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 1.6759262452585069 - 1.6495797750495655 1.7022727154674482] - [Timestamp('2002-10-10 00:00:00') nan 1.1978309823047661 - 1.1593828113306537 1.2362791532788786] - [Timestamp('2002-10-11 00:00:00') nan 1.996847941687526 - 1.947969827087749 2.045726056287303] - [Timestamp('2002-10-12 00:00:00') nan 1.678767578418268 - 1.621737008316633 1.7357981485199032] - [Timestamp('2002-10-13 00:00:00') nan 0.2545802269018934 - 0.1912511232698464 0.31790933053394044] - [Timestamp('2002-10-14 00:00:00') nan 1.676601606547448 - 1.6083846355289446 1.7448185775659515] - [Timestamp('2002-10-15 00:00:00') nan 1.1986155856625866 - 1.126587700494948 1.2706434708302252] - [Timestamp('2002-10-16 00:00:00') nan 1.9976716356273612 - 1.9226551625001582 2.072688108754564] - [Timestamp('2002-10-17 00:00:00') nan 1.6798289078253203 - 1.602439990735115 1.7572178249155257] - [Timestamp('2002-10-18 00:00:00') nan 0.2524642585524501 - 0.17315114179590962 0.33177737530899054] - [Timestamp('2002-10-19 00:00:00') nan 1.6771125165190937 - 1.5961966626499575 1.7580283703882298] - [Timestamp('2002-10-20 00:00:00') nan 1.19930352000067 1.117018877256898 - 1.281588162744442]] + [[Timestamp('2002-10-09 00:00:00') nan 1.675792799822534 + 1.6495088100365387 1.7020767896085292] + [Timestamp('2002-10-10 00:00:00') nan 1.196698230840187 + 1.158389522483584 1.23500693919679] + [Timestamp('2002-10-11 00:00:00') nan 1.9979538859587707 + 1.9492515314114331 2.0466562405061084] + [Timestamp('2002-10-12 00:00:00') nan 1.6783886460816353 + 1.6215532205643244 1.7352240715989462] + [Timestamp('2002-10-13 00:00:00') nan 0.25497146476412086 + 0.19184810207559444 0.3180948274526473] + [Timestamp('2002-10-14 00:00:00') nan 1.6764761765450826 + 1.6084672358094319 1.7444851172807334] + [Timestamp('2002-10-15 00:00:00') nan 1.197357922371823 + 1.1255347238210889 1.269181120922557] + [Timestamp('2002-10-16 00:00:00') nan 1.9989037017558677 + 1.9240870257876124 2.073720377724123] + [Timestamp('2002-10-17 00:00:00') nan 1.6794045128397188 + 1.6022108241956698 1.7565982014837678] + [Timestamp('2002-10-18 00:00:00') nan 0.2528992237057803 + 0.17377589408969168 0.33202255332186886] + [Timestamp('2002-10-19 00:00:00') nan 1.6769866071923052 + 1.5962514456698882 1.7577217687147222] + [Timestamp('2002-10-20 00:00:00') nan 1.1979548734335086 + 1.1158356158146074 1.2800741310524097]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.010146044480158802", - "MAPE": "0.0194", - "MASE": "0.0114", - "RMSE": "0.013442076637214934" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.010150783754375406", + "MAPE": "0.0192", + "MASE": "0.0114", + "RMSE": "0.013410198870405777" + } } } @@ -117,7 +128,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":1.6782881616,"1001":0.2822495426,"1002":1.6889618309,"1003":1.1827283634,"1004":1.9873211807,"1005":1.6782148981,"1006":0.2615849171,"1007":1.6647266842,"1008":1.2012421795,"1009":1.9897695182,"1010":1.6787149649,"1011":0.2744806954,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":1.6762934022,"1001":0.2520044794,"1002":1.6732282601,"1003":1.1966435581,"1004":1.996672772,"1005":1.677028487,"1006":0.2542334935,"1007":1.6761614991,"1008":1.1955724698,"1009":1.9961353602,"1010":1.6778392224,"1011":0.2531187623,"1012":1.6759262453,"1013":1.1978309823,"1014":1.9968479417,"1015":1.6787675784,"1016":0.2545802269,"1017":1.6766016065,"1018":1.1986155857,"1019":1.9976716356,"1020":1.6798289078,"1021":0.2524642586,"1022":1.6771125165,"1023":1.19930352},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.649579775,"1013":1.1593828113,"1014":1.9479698271,"1015":1.6217370083,"1016":0.1912511233,"1017":1.6083846355,"1018":1.1265877005,"1019":1.9226551625,"1020":1.6024399907,"1021":0.1731511418,"1022":1.5961966626,"1023":1.1170188773},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.7022727155,"1013":1.2362791533,"1014":2.0457260563,"1015":1.7357981485,"1016":0.3179093305,"1017":1.7448185776,"1018":1.2706434708,"1019":2.0726881088,"1020":1.7572178249,"1021":0.3317773753,"1022":1.7580283704,"1023":1.2815881627}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":1.6782881616,"1001":0.2822495426,"1002":1.6889618309,"1003":1.1827283634,"1004":1.9873211807,"1005":1.6782148981,"1006":0.2615849171,"1007":1.6647266842,"1008":1.2012421795,"1009":1.9897695182,"1010":1.6787149649,"1011":0.2744806954,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":1.6758857519,"1001":0.2523877643,"1002":1.6730960653,"1003":1.195512054,"1004":1.9978016377,"1005":1.6766356308,"1006":0.2546019439,"1007":1.6760452222,"1008":1.1944125515,"1009":1.9972475397,"1010":1.6774613668,"1011":0.2534915521,"1012":1.6757927998,"1013":1.1966982308,"1014":1.997953886,"1015":1.6783886461,"1016":0.2549714648,"1017":1.6764761765,"1018":1.1973579224,"1019":1.9989037018,"1020":1.6794045128,"1021":0.2528992237,"1022":1.6769866072,"1023":1.1979548734},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.64950881,"1013":1.1583895225,"1014":1.9492515314,"1015":1.6215532206,"1016":0.1918481021,"1017":1.6084672358,"1018":1.1255347238,"1019":1.9240870258,"1020":1.6022108242,"1021":0.1737758941,"1022":1.5962514457,"1023":1.1158356158},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.7020767896,"1013":1.2350069392,"1014":2.0466562405,"1015":1.7352240716,"1016":0.3180948275,"1017":1.7444851173,"1018":1.2691811209,"1019":2.0737203777,"1020":1.7565982015,"1021":0.3320225533,"1022":1.7577217687,"1023":1.2800741311}} diff --git a/tests/references/bugs_issue_34_test_artificial_1024_sqr_poly_5_12_20.log b/tests/references/bugs_issue_34_test_artificial_1024_sqr_poly_5_12_20.log index a028b39e3..f796a61fb 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024_sqr_poly_5_12_20.log +++ b/tests/references/bugs_issue_34_test_artificial_1024_sqr_poly_5_12_20.log @@ -1,41 +1,50 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_poly_5_sqr_0.0_20 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 38.618816614151 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 50.74535083770752 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=0.9917376976448105 Max=53.56216825026867 Mean=23.158907818377326 StdDev=17.12895813027858 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=0.9917376976448105 Max=53.56216825026867 Mean=23.158907818377326 StdDev=17.12895813027858 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [ConstantTrend + Cycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)' [ConstantTrend + Cycle + AR] INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [AR] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)' [AR] INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0053 MAPE_Forecast=0.0018 MAPE_Test=0.0018 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0063 SMAPE_Forecast=0.0018 SMAPE_Test=0.0018 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0007 MASE_Forecast=0.0006 MASE_Test=0.0007 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.01719588937538593 L1_Forecast=0.01671289276897048 L1_Test=0.019035412513731104 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.04385473526732899 L2_Forecast=0.019896622064385 L2_Test=0.02244710407998201 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.01722161885457115 L1_Forecast=0.016847708137328766 L1_Test=0.019193995375855144 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.044104147917855295 L2_Forecast=0.020050025728119675 L2_Test=0.022587304949091284 INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 22.882400338104517 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE 5 2.7488523940873133 {0: 3.395869001105355, 1: -21.61642072013197, 2: 3.3961866578814206, 3: -13.098472466998542, 4: 27.927586028659583} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag5 0.6382742484606586 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag10 0.19745849693604856 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag3 -0.17239448991767473 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 -0.13359657143650383 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag4 -0.11924710612432145 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag7 -0.06371432929171986 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag15 0.057887441647163335 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag61 0.040284719496251134 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag13 0.036975390777178124 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag64 0.03540865406124535 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag5 0.6404212117112742 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag10 0.19591210408943477 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag3 -0.1783112328302316 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag1 -0.13179518084341682 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag4 -0.11864296529674667 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag7 -0.06345485686297819 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag15 0.056853459209451486 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag61 0.04052471964352042 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag13 0.03884531474412987 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag64 0.035486972358624594 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.634850263595581 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 1.260263442993164 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', - 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)_residue', + 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)', + '_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)_residue', '_Signal_0.01_Trend', '_Signal_0.01_Trend_residue', '_Signal_0.01_Cycle', '_Signal_0.01_Cycle_residue', '_Signal_0.01_AR', '_Signal_0.01_AR_residue', '_Signal_0.01_TransformedForecast', @@ -56,59 +65,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 28.29202879165514 - 28.253031412408948 28.331026170901335] - [Timestamp('2002-10-10 00:00:00') nan 11.014732522718074 - 10.94855597033099 11.080909075105158] - [Timestamp('2002-10-11 00:00:00') nan 53.561251177619035 - 53.46738287785636 53.65511947738171] - [Timestamp('2002-10-12 00:00:00') nan 28.301144133984973 - 28.180762408874738 28.42152585909521] - [Timestamp('2002-10-13 00:00:00') nan 1.7700039358040978 - 1.62441246007758 1.9155954115306155] - [Timestamp('2002-10-14 00:00:00') nan 28.30068171069347 - 28.131100947499824 28.470262473887118] - [Timestamp('2002-10-15 00:00:00') nan 11.024090449961117 - 10.831843653872793 11.21633724604944] - [Timestamp('2002-10-16 00:00:00') nan 53.567024630812696 - 53.35361174866997 53.78043751295542] - [Timestamp('2002-10-17 00:00:00') nan 28.308002977492293 - 28.074740320779398 28.54126563420519] - [Timestamp('2002-10-18 00:00:00') nan 1.7816145730426727 - 1.5297882888393515 2.033440857245994] - [Timestamp('2002-10-19 00:00:00') nan 28.310261438690294 - 28.041061047726654 28.579461829653933] - [Timestamp('2002-10-20 00:00:00') nan 11.035433235192166 - 10.749861774905288 11.321004695479044]] + [[Timestamp('2002-10-09 00:00:00') nan 28.29233511075769 + 28.253037060330577 28.331633161184804] + [Timestamp('2002-10-10 00:00:00') nan 11.01417559723766 + 10.947335827713873 11.081015366761447] + [Timestamp('2002-10-11 00:00:00') nan 53.560887584262005 + 53.466023613115716 53.655751555408294] + [Timestamp('2002-10-12 00:00:00') nan 28.30095038783748 + 28.179269636084616 28.422631139590347] + [Timestamp('2002-10-13 00:00:00') nan 1.7707420330992174 + 1.6235712816987207 1.9179127844997141] + [Timestamp('2002-10-14 00:00:00') nan 28.30139409410593 + 28.129974915335843 28.472813272876017] + [Timestamp('2002-10-15 00:00:00') nan 11.023378972822563 + 10.829044364525535 11.217713581119591] + [Timestamp('2002-10-16 00:00:00') nan 53.56628515880801 + 53.35054180299011 53.78202851462591] + [Timestamp('2002-10-17 00:00:00') nan 28.307582314765995 + 28.071731384602337 28.543433244929652] + [Timestamp('2002-10-18 00:00:00') nan 1.7828820635222085 + 1.5282317212010939 2.037532405843323] + [Timestamp('2002-10-19 00:00:00') nan 28.31137800887673 + 28.039145645399355 28.583610372354105] + [Timestamp('2002-10-20 00:00:00') nan 11.034568353130666 + 10.745758987871438 11.323377718389894]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.01671289276897048", - "MAPE": "0.0018", - "MASE": "0.0006", - "RMSE": "0.019896622064385" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.016847708137328766", + "MAPE": "0.0018", + "MASE": "0.0006", + "RMSE": "0.020050025728119675" + } } } @@ -117,7 +128,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":28.2622666655,"1001":1.7444532744,"1002":28.2749415602,"1003":11.0108448398,"1004":53.5487709023,"1005":28.2773935387,"1006":1.7586416843,"1007":28.290416892,"1008":11.0139485153,"1009":53.5621682503,"1010":28.3068751651,"1011":1.7568830354,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":28.2268440142,"1001":1.7357026164,"1002":28.246467207,"1003":11.0068554996,"1004":53.5236136404,"1005":28.248670557,"1006":1.7471677405,"1007":28.2687535285,"1008":11.0105500691,"1009":53.5447369567,"1010":28.2683400004,"1011":1.7622885275,"1012":28.2920287917,"1013":11.0147325227,"1014":53.5612511776,"1015":28.301144134,"1016":1.7700039358,"1017":28.3006817107,"1018":11.02409045,"1019":53.5670246308,"1020":28.3080029775,"1021":1.781614573,"1022":28.3102614387,"1023":11.0354332352},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":28.2530314124,"1013":10.9485559703,"1014":53.4673828779,"1015":28.1807624089,"1016":1.6244124601,"1017":28.1311009475,"1018":10.8318436539,"1019":53.3536117487,"1020":28.0747403208,"1021":1.5297882888,"1022":28.0410610477,"1023":10.7498617749},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":28.3310261709,"1013":11.0809090751,"1014":53.6551194774,"1015":28.4215258591,"1016":1.9155954115,"1017":28.4702624739,"1018":11.216337246,"1019":53.780437513,"1020":28.5412656342,"1021":2.0334408572,"1022":28.5794618297,"1023":11.3210046955}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":28.2622666655,"1001":1.7444532744,"1002":28.2749415602,"1003":11.0108448398,"1004":53.5487709023,"1005":28.2773935387,"1006":1.7586416843,"1007":28.290416892,"1008":11.0139485153,"1009":53.5621682503,"1010":28.3068751651,"1011":1.7568830354,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":28.2265035832,"1001":1.7361210174,"1002":28.2467223515,"1003":11.0065187288,"1004":53.5230927779,"1005":28.2483526617,"1006":1.7477621074,"1007":28.269084082,"1008":11.0101071338,"1009":53.5441652735,"1010":28.2680610115,"1011":1.762980421,"1012":28.2923351108,"1013":11.0141755972,"1014":53.5608875843,"1015":28.3009503878,"1016":1.7707420331,"1017":28.3013940941,"1018":11.0233789728,"1019":53.5662851588,"1020":28.3075823148,"1021":1.7828820635,"1022":28.3113780089,"1023":11.0345683531},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":28.2530370603,"1013":10.9473358277,"1014":53.4660236131,"1015":28.1792696361,"1016":1.6235712817,"1017":28.1299749153,"1018":10.8290443645,"1019":53.350541803,"1020":28.0717313846,"1021":1.5282317212,"1022":28.0391456454,"1023":10.7457589879},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":28.3316331612,"1013":11.0810153668,"1014":53.6557515554,"1015":28.4226311396,"1016":1.9179127845,"1017":28.4728132729,"1018":11.2177135811,"1019":53.7820285146,"1020":28.5434332449,"1021":2.0375324058,"1022":28.5836103724,"1023":11.3233777184}} diff --git a/tests/references/bugs_issue_34_test_artificial_1024_sqrt_constant_7_12_100.log b/tests/references/bugs_issue_34_test_artificial_1024_sqrt_constant_7_12_100.log index 429feb93a..5a0c700b9 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024_sqrt_constant_7_12_100.log +++ b/tests/references/bugs_issue_34_test_artificial_1024_sqrt_constant_7_12_100.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_constant_7_sqrt_0.0_100 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 32.24102759361267 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 32.50078535079956 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=0.976817907433116 Max=2.8907356910762547 Mean=2.044450416498691 StdDev=0.5765740953146404 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=0.976817907433116 Max=2.8907356910762547 Mean=2.044450416498691 StdDev=0.5765740953146404 @@ -10,16 +10,25 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_Seasonal_D INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0053 MAPE_Forecast=0.0091 MAPE_Test=0.0125 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0053 SMAPE_Forecast=0.0091 SMAPE_Test=0.0127 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0113 MASE_Forecast=0.0188 MASE_Test=0.0242 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.009282477206609484 L1_Forecast=0.015305085191048775 L1_Test=0.019834962371485614 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.011553995313226068 L2_Forecast=0.0178248431450344 L2_Test=0.023297412455808356 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0053 MAPE_Forecast=0.009 MAPE_Test=0.0124 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0053 SMAPE_Forecast=0.0091 SMAPE_Test=0.0126 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0113 MASE_Forecast=0.0187 MASE_Test=0.024 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.009273097629175785 L1_Forecast=0.015237553203738768 L1_Test=0.01963169017538045 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.011578127040229239 L2_Forecast=0.017770566263132545 L2_Test=0.023181389130290372 INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 2.040658726819666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek -0.06071359083967953 {5: -0.06665388730808885, 6: -1.0261488479541572, 0: -0.06920492384066534, 1: 0.5564143367575656, 2: -0.4693002626075212, 3: 0.26529415156107916, 4: 0.819831330448135} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0279171466827393 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.7884180545806885 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', '_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek', @@ -46,59 +55,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 1.5716006376177303 - 1.536663945053463 1.6065373301819976] - [Timestamp('2002-10-10 00:00:00') nan 2.3065954333086185 - 2.271658740744351 2.341532125872886] - [Timestamp('2002-10-11 00:00:00') nan 2.8602859757057013 - 2.825349283141434 2.8952226682699687] - [Timestamp('2002-10-12 00:00:00') nan 1.9721920615715374 - 1.93725536900727 2.0071287541358047] - [Timestamp('2002-10-13 00:00:00') nan 1.0147354685118026 - 0.9797987759475352 1.04967216107607] - [Timestamp('2002-10-14 00:00:00') nan 1.9715830754914851 - 1.9366463829272178 2.0065197680557527] - [Timestamp('2002-10-15 00:00:00') nan 2.5972183471409287 - 2.5622816545766613 2.632155039705196] - [Timestamp('2002-10-16 00:00:00') nan 1.5716006376177303 - 1.536663945053463 1.6065373301819976] - [Timestamp('2002-10-17 00:00:00') nan 2.3065954333086185 - 2.271658740744351 2.341532125872886] - [Timestamp('2002-10-18 00:00:00') nan 2.8602859757057013 - 2.825349283141434 2.8952226682699687] - [Timestamp('2002-10-19 00:00:00') nan 1.9721920615715374 - 1.93725536900727 2.0071287541358047] - [Timestamp('2002-10-20 00:00:00') nan 1.0147354685118026 - 0.9797987759475352 1.04967216107607]] + [[Timestamp('2002-10-09 00:00:00') nan 1.5713584642121448 + 1.5365281543364049 1.6061887740878846] + [Timestamp('2002-10-10 00:00:00') nan 2.305952878380745 + 2.2711225685050054 2.3407831882564847] + [Timestamp('2002-10-11 00:00:00') nan 2.8604900572678007 + 2.825659747392061 2.8953203671435404] + [Timestamp('2002-10-12 00:00:00') nan 1.974004839511577 + 1.9391745296358371 2.0088351493873167] + [Timestamp('2002-10-13 00:00:00') nan 1.0145098788655087 + 0.9796795689897689 1.0493401887412486] + [Timestamp('2002-10-14 00:00:00') nan 1.9714538029790005 + 1.9366234931032607 2.00628411285474] + [Timestamp('2002-10-15 00:00:00') nan 2.5970730635772314 + 2.562242753701492 2.631903373452971] + [Timestamp('2002-10-16 00:00:00') nan 1.5713584642121448 + 1.5365281543364049 1.6061887740878846] + [Timestamp('2002-10-17 00:00:00') nan 2.305952878380745 + 2.2711225685050054 2.3407831882564847] + [Timestamp('2002-10-18 00:00:00') nan 2.8604900572678007 + 2.825659747392061 2.8953203671435404] + [Timestamp('2002-10-19 00:00:00') nan 1.974004839511577 + 1.9391745296358371 2.0088351493873167] + [Timestamp('2002-10-20 00:00:00') nan 1.0145098788655087 + 0.9796795689897689 1.0493401887412486]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "0.015305085191048775", - "MAPE": "0.0091", - "MASE": "0.0188", - "RMSE": "0.0178248431450344" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.015237553203738768", + "MAPE": "0.009", + "MASE": "0.0187", + "RMSE": "0.017770566263132545" + } } } @@ -107,7 +118,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":2.8762956847,"1001":1.9768058665,"1002":1.0468009074,"1003":1.988323312,"1004":2.5952563039,"1005":1.5993801952,"1006":2.323717344,"1007":2.8713372908,"1008":2.0071345655,"1009":1.0600092572,"1010":1.9877574872,"1011":2.6115031754,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":2.8602859757,"1001":1.9721920616,"1002":1.0147354685,"1003":1.9715830755,"1004":2.5972183471,"1005":1.5716006376,"1006":2.3065954333,"1007":2.8602859757,"1008":1.9721920616,"1009":1.0147354685,"1010":1.9715830755,"1011":2.5972183471,"1012":1.5716006376,"1013":2.3065954333,"1014":2.8602859757,"1015":1.9721920616,"1016":1.0147354685,"1017":1.9715830755,"1018":2.5972183471,"1019":1.5716006376,"1020":2.3065954333,"1021":2.8602859757,"1022":1.9721920616,"1023":1.0147354685},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.5366639451,"1013":2.2716587407,"1014":2.8253492831,"1015":1.937255369,"1016":0.9797987759,"1017":1.9366463829,"1018":2.5622816546,"1019":1.5366639451,"1020":2.2716587407,"1021":2.8253492831,"1022":1.937255369,"1023":0.9797987759},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.6065373302,"1013":2.3415321259,"1014":2.8952226683,"1015":2.0071287541,"1016":1.0496721611,"1017":2.0065197681,"1018":2.6321550397,"1019":1.6065373302,"1020":2.3415321259,"1021":2.8952226683,"1022":2.0071287541,"1023":1.0496721611}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":2.8762956847,"1001":1.9768058665,"1002":1.0468009074,"1003":1.988323312,"1004":2.5952563039,"1005":1.5993801952,"1006":2.323717344,"1007":2.8713372908,"1008":2.0071345655,"1009":1.0600092572,"1010":1.9877574872,"1011":2.6115031754,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":2.8604900573,"1001":1.9740048395,"1002":1.0145098789,"1003":1.971453803,"1004":2.5970730636,"1005":1.5713584642,"1006":2.3059528784,"1007":2.8604900573,"1008":1.9740048395,"1009":1.0145098789,"1010":1.971453803,"1011":2.5970730636,"1012":1.5713584642,"1013":2.3059528784,"1014":2.8604900573,"1015":1.9740048395,"1016":1.0145098789,"1017":1.971453803,"1018":2.5970730636,"1019":1.5713584642,"1020":2.3059528784,"1021":2.8604900573,"1022":1.9740048395,"1023":1.0145098789},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.5365281543,"1013":2.2711225685,"1014":2.8256597474,"1015":1.9391745296,"1016":0.979679569,"1017":1.9366234931,"1018":2.5622427537,"1019":1.5365281543,"1020":2.2711225685,"1021":2.8256597474,"1022":1.9391745296,"1023":0.979679569},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":1.6061887741,"1013":2.3407831883,"1014":2.8953203671,"1015":2.0088351494,"1016":1.0493401887,"1017":2.0062841129,"1018":2.6319033735,"1019":1.6061887741,"1020":2.3407831883,"1021":2.8953203671,"1022":2.0088351494,"1023":1.0493401887}} diff --git a/tests/references/bugs_issue_34_test_artificial_1024_sqrt_linear_30_12_20.log b/tests/references/bugs_issue_34_test_artificial_1024_sqrt_linear_30_12_20.log index 44eaae4b9..3472c1bdd 100644 --- a/tests/references/bugs_issue_34_test_artificial_1024_sqrt_linear_30_12_20.log +++ b/tests/references/bugs_issue_34_test_artificial_1024_sqrt_linear_30_12_20.log @@ -1,41 +1,51 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_1024_D_0_linear_30_sqrt_0.0_20 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 42.728050231933594 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 63.09064960479736 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=1012 Min=0.9795076808675534 Max=3.5070970371414028 Mean=2.5719039613700563 StdDev=0.5484485193612941 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=0.9795076808675534 Max=3.5070970371414028 Mean=2.5719039613700563 StdDev=0.5484485193612941 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [ConstantTrend + Cycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.007 MAPE_Forecast=0.0088 MAPE_Test=0.0115 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.007 SMAPE_Forecast=0.0088 SMAPE_Test=0.0116 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0257 MASE_Forecast=0.0419 MASE_Test=0.0447 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.015716029742579796 L1_Forecast=0.022380380283140367 L1_Test=0.030673504146500902 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.023459392073342644 L2_Forecast=0.029601168067773655 L2_Test=0.037014879165893064 -INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)' [PolyTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_PolyTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0178 MAPE_Forecast=0.0071 MAPE_Test=0.0069 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0169 SMAPE_Forecast=0.0071 SMAPE_Test=0.0069 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0614 MASE_Forecast=0.0361 MASE_Test=0.0292 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.03753794906052775 L1_Forecast=0.019284770428669447 L1_Test=0.020058070619109986 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10139410131244458 L2_Forecast=0.0242487375092436 L2_Test=0.022816723171608787 +INFO:pyaf.std:MODEL_COMPLEXITY 80 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (2.2567811906693165, array([ 0.4634626 , 0.0974266 , -0.05407428])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_0.01_PolyTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag30 0.2690515330633028 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag60 0.20096958246064678 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag7 0.10921309570226022 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 0.07573445174431347 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag2 0.07158362465427857 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag26 -0.06827035626891648 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag23 0.05816797164511076 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag6 0.05680784575647437 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag9 0.05329157592114642 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_Lag16 0.051948780756433374 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag30 0.8118124309477646 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag8 -0.23585597862435612 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag4 -0.22393184579428832 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag37 -0.20369038680262416 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag7 0.20072539315534832 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag38 0.19893782723852305 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag34 0.18576109094633955 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag9 0.1820386804253378 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag39 -0.18171875819559774 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_0.01_PolyTrend_residue_zeroCycle_residue_Lag2 0.16246084239156527 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.5066518783569336 +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 1.239441156387329 Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', - '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', - 'cycle_internal', '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)', - '_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)_residue', + 'Date_Normalized_^2', 'Date_Normalized_^3', '_Signal_0.01_PolyTrend', + '_Signal_0.01_PolyTrend_residue', + '_Signal_0.01_PolyTrend_residue_zeroCycle', + '_Signal_0.01_PolyTrend_residue_zeroCycle_residue', + '_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)', + '_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)_residue', '_Signal_0.01_Trend', '_Signal_0.01_Trend_residue', '_Signal_0.01_Cycle', '_Signal_0.01_Cycle_residue', '_Signal_0.01_AR', '_Signal_0.01_AR_residue', '_Signal_0.01_TransformedForecast', @@ -56,59 +66,61 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 40.1 KB Forecasts - [[Timestamp('2002-10-09 00:00:00') nan 2.850259244177824 - 2.7922409547649876 2.9082775335906605] - [Timestamp('2002-10-10 00:00:00') nan 2.4858968032964848 - 2.4019691944236783 2.5698244121692913] - [Timestamp('2002-10-11 00:00:00') nan 2.557019798857418 - 2.4551654135999774 2.6588741841148584] - [Timestamp('2002-10-12 00:00:00') nan 3.2311726754554315 - 3.1153830782107748 3.3469622727000883] - [Timestamp('2002-10-13 00:00:00') nan 3.1685710373143907 - 3.04121697104866 3.2959251035801214] - [Timestamp('2002-10-14 00:00:00') nan 3.0111479777737356 - 2.873754253490287 3.148541702057184] - [Timestamp('2002-10-15 00:00:00') nan 3.1576943995928044 - 3.011458221341441 3.303930577844168] - [Timestamp('2002-10-16 00:00:00') nan 2.304232021910817 - 2.1502471681185993 2.4582168757030347] - [Timestamp('2002-10-17 00:00:00') nan 2.089381404218575 - 1.928664543732832 2.250098264704318] - [Timestamp('2002-10-18 00:00:00') nan 2.4280509431352963 - 2.261515983004716 2.5945859032658767] - [Timestamp('2002-10-19 00:00:00') nan 2.5453595676095615 - 2.373810535894355 2.716908599324768] - [Timestamp('2002-10-20 00:00:00') nan 3.1596181806481356 - 2.983760799314323 3.3354755619819483]] + [[Timestamp('2002-10-09 00:00:00') nan 2.8629421393718184 + 2.815414613853701 2.910469664889936] + [Timestamp('2002-10-10 00:00:00') nan 2.553273732012636 + 2.4785969531448004 2.627950510880472] + [Timestamp('2002-10-11 00:00:00') nan 2.640962228328552 + 2.5345943533037816 2.7473301033533226] + [Timestamp('2002-10-12 00:00:00') nan 3.2616685413671362 + 3.1024195069039653 3.420917575830307] + [Timestamp('2002-10-13 00:00:00') nan 3.2134385059537687 + 2.925995600105262 3.5008814118022755] + [Timestamp('2002-10-14 00:00:00') nan 3.053656039753346 + 2.4174216652924265 3.6898904142142657] + [Timestamp('2002-10-15 00:00:00') nan 3.2203143489281234 + 1.615223469476356 4.825405228379891] + [Timestamp('2002-10-16 00:00:00') nan 2.3588454861427284 + -1.5592095914425026 6.27690056372796] + [Timestamp('2002-10-17 00:00:00') nan 2.2305104749177294 + -7.579247639347502 12.04026858918296] + [Timestamp('2002-10-18 00:00:00') nan 2.5008932889713225 + -23.116392865477255 28.1181794434199] + [Timestamp('2002-10-19 00:00:00') nan 2.636857168576677 + -66.76650564815903 72.04021998531239] + [Timestamp('2002-10-20 00:00:00') nan 3.212431414536902 + -188.42688648155536 194.85174931062915]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1012 }, - "Training_Signal_Length": 1012 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.022380380283140367", - "MAPE": "0.0088", - "MASE": "0.0419", - "RMSE": "0.029601168067773655" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_0.01_PolyTrend_residue_zeroCycle_residue_AR(64)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "80", + "MAE": "0.019284770428669447", + "MAPE": "0.0071", + "MASE": "0.0361", + "RMSE": "0.0242487375092436" + } } } @@ -117,7 +129,7 @@ Forecasts -{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":3.4440453119,"1001":2.7715482413,"1002":3.492013137,"1003":2.0016227935,"1004":2.3916807016,"1005":2.4646217171,"1006":3.3756708225,"1007":2.8901250976,"1008":3.4277297925,"1009":3.0730001408,"1010":2.3129894983,"1011":3.4694024229,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":3.4068992368,"1001":2.7618478821,"1002":3.5382349987,"1003":1.9215026341,"1004":2.3363787252,"1005":2.4444726928,"1006":3.4040239246,"1007":2.8954813634,"1008":3.4495286213,"1009":3.0647271166,"1010":2.2884740208,"1011":3.5005483184,"1012":2.8502592442,"1013":2.4858968033,"1014":2.5570197989,"1015":3.2311726755,"1016":3.1685710373,"1017":3.0111479778,"1018":3.1576943996,"1019":2.3042320219,"1020":2.0893814042,"1021":2.4280509431,"1022":2.5453595676,"1023":3.1596181806},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.7922409548,"1013":2.4019691944,"1014":2.4551654136,"1015":3.1153830782,"1016":3.041216971,"1017":2.8737542535,"1018":3.0114582213,"1019":2.1502471681,"1020":1.9286645437,"1021":2.261515983,"1022":2.3738105359,"1023":2.9837607993},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.9082775336,"1013":2.5698244122,"1014":2.6588741841,"1015":3.3469622727,"1016":3.2959251036,"1017":3.1485417021,"1018":3.3039305778,"1019":2.4582168757,"1020":2.2500982647,"1021":2.5945859033,"1022":2.7169085993,"1023":3.335475562}} +{"Date":{"1000":"2002-09-27T00:00:00.000Z","1001":"2002-09-28T00:00:00.000Z","1002":"2002-09-29T00:00:00.000Z","1003":"2002-09-30T00:00:00.000Z","1004":"2002-10-01T00:00:00.000Z","1005":"2002-10-02T00:00:00.000Z","1006":"2002-10-03T00:00:00.000Z","1007":"2002-10-04T00:00:00.000Z","1008":"2002-10-05T00:00:00.000Z","1009":"2002-10-06T00:00:00.000Z","1010":"2002-10-07T00:00:00.000Z","1011":"2002-10-08T00:00:00.000Z","1012":"2002-10-09T00:00:00.000Z","1013":"2002-10-10T00:00:00.000Z","1014":"2002-10-11T00:00:00.000Z","1015":"2002-10-12T00:00:00.000Z","1016":"2002-10-13T00:00:00.000Z","1017":"2002-10-14T00:00:00.000Z","1018":"2002-10-15T00:00:00.000Z","1019":"2002-10-16T00:00:00.000Z","1020":"2002-10-17T00:00:00.000Z","1021":"2002-10-18T00:00:00.000Z","1022":"2002-10-19T00:00:00.000Z","1023":"2002-10-20T00:00:00.000Z"},"Signal_0.01":{"1000":3.4440453119,"1001":2.7715482413,"1002":3.492013137,"1003":2.0016227935,"1004":2.3916807016,"1005":2.4646217171,"1006":3.3756708225,"1007":2.8901250976,"1008":3.4277297925,"1009":3.0730001408,"1010":2.3129894983,"1011":3.4694024229,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null},"Signal_0.01_Forecast":{"1000":3.4075132941,"1001":2.7971186339,"1002":3.5172069112,"1003":2.0063077847,"1004":2.3604978705,"1005":2.4473256061,"1006":3.3861897001,"1007":2.9012460012,"1008":3.4089905416,"1009":3.0720144785,"1010":2.3400600812,"1011":3.5012038751,"1012":2.8629421394,"1013":2.553273732,"1014":2.6409622283,"1015":3.2616685414,"1016":3.213438506,"1017":3.0536560398,"1018":3.2203143489,"1019":2.3588454861,"1020":2.2305104749,"1021":2.500893289,"1022":2.6368571686,"1023":3.2124314145},"Signal_0.01_Forecast_Lower_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.8154146139,"1013":2.4785969531,"1014":2.5345943533,"1015":3.1024195069,"1016":2.9259956001,"1017":2.4174216653,"1018":1.6152234695,"1019":-1.5592095914,"1020":-7.5792476393,"1021":-23.1163928655,"1022":-66.7665056482,"1023":-188.4268864816},"Signal_0.01_Forecast_Upper_Bound":{"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":2.9104696649,"1013":2.6279505109,"1014":2.7473301034,"1015":3.4209175758,"1016":3.5008814118,"1017":3.6898904142,"1018":4.8254052284,"1019":6.2769005637,"1020":12.0402685892,"1021":28.1181794434,"1022":72.0402199853,"1023":194.8517493106}} diff --git a/tests/references/bugs_issue_36_display_version_info.log b/tests/references/bugs_issue_36_display_version_info.log index 36c0143fe..0fa8f085c 100644 --- a/tests/references/bugs_issue_36_display_version_info.log +++ b/tests/references/bugs_issue_36_display_version_info.log @@ -1 +1 @@ -[('PyAF_version', '1.2.4'), ('matplotlib_version', '3.1.3'), ('numpy_version', '1.18.1'), ('pandas_version', '1.0.1'), ('pydot_version', '1.4.1'), ('python_implementation', 'CPython'), ('python_version', '3.8.2'), ('scipy_version', '1.4.1'), ('sklearn_version', '0.22.2'), ('sqlalchemy_version', '1.3.13'), ('system_platform', 'Linux-5.5.0-1-amd64-x86_64-with-glibc2.29'), ('system_processor', ''), ('system_uname', uname_result(system='Linux', node='z600', release='5.5.0-1-amd64', version='#1 SMP Debian 5.5.13-2 (2020-03-30)', machine='x86_64', processor=''))] +[('PyAF_version', '2.0'), ('matplotlib_version', '3.1.3'), ('numpy_version', '1.19.0'), ('pandas_version', '1.0.1'), ('pydot_version', '1.4.1'), ('python_implementation', 'CPython'), ('python_version', '3.8.4'), ('scipy_version', '1.4.1'), ('sklearn_version', '0.23.1'), ('sqlalchemy_version', '1.3.13'), ('system_platform', 'Linux-5.7.0-1-amd64-x86_64-with-glibc2.29'), ('system_processor', ''), ('system_uname', uname_result(system='Linux', node='z600', release='5.7.0-1-amd64', version='#1 SMP Debian 5.7.6-1 (2020-06-24)', machine='x86_64', processor=''))] diff --git a/tests/references/bugs_issue_36_issue_36_version_info.log b/tests/references/bugs_issue_36_issue_36_version_info.log index f29465942..e897d06c9 100644 --- a/tests/references/bugs_issue_36_issue_36_version_info.log +++ b/tests/references/bugs_issue_36_issue_36_version_info.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 6.205386161804199 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.088032245635986 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,31 +17,40 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013038 L1_Test=0.42992050508902374 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507155 L2_Test=0.5519432695595545 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033716 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533314 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225448 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389378 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.1409304557420878 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046362 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481855 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102227 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045818 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947771 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END -PyAF_version 1.2.4 +PyAF_version 2.0 matplotlib_version 3.1.3 -numpy_version 1.18.1 +numpy_version 1.19.0 pandas_version 1.0.1 pydot_version 1.4.1 python_implementation CPython -python_version 3.8.2 +python_version 3.8.4 scipy_version 1.4.1 -sklearn_version 0.22.2 +sklearn_version 0.23.1 sqlalchemy_version 1.3.13 -system_platform Linux-5.5.0-1-amd64-x86_64-with-glibc2.29 +system_platform Linux-5.7.0-1-amd64-x86_64-with-glibc2.29 system_processor -system_uname uname_result(system='Linux', node='z600', release='5.5.0-1-amd64', version='#1 SMP Debian 5.5.13-2 (2020-03-30)', machine='x86_64', processor='') +system_uname uname_result(system='Linux', node='z600', release='5.7.0-1-amd64', version='#1 SMP Debian 5.7.6-1 (2020-06-24)', machine='x86_64', processor='') diff --git a/tests/references/bugs_issue_4_issue_4.log b/tests/references/bugs_issue_4_issue_4.log index f246e5251..31ff64807 100644 --- a/tests/references/bugs_issue_4_issue_4.log +++ b/tests/references/bugs_issue_4_issue_4.log @@ -23,7 +23,7 @@ memory usage: 98.3+ KB 1253 1253 AAPL 51.622111 ... 390.980003 2011-08-03 392.570000 [5 rows x 9 columns] -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Close' 6.45369553565979 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Close']' 10.925302028656006 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2011-07-28T00:00:00.000000 TimeMax=2015-07-20T00:00:00.000000 TimeDelta= Horizon=7 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Close' Length=1258 Min=90.279999 Max=702.100021 Mean=337.3416532639109 StdDev=207.08814386340208 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Close' Min=90.279999 Max=702.100021 Mean=337.3416532639109 StdDev=207.08814386340208 @@ -38,10 +38,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.999 MASE_Forecast=0.9998 MASE_Test=0.8607 INFO:pyaf.std:MODEL_L1 L1_Fit=5.512609779000003 L1_Forecast=1.3764142828685255 L1_Test=1.3857138571428567 INFO:pyaf.std:MODEL_L2 L2_Fit=19.109797100061108 L2_Forecast=1.897196294559433 L2_Test=2.4644094633140545 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 391.819996 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Close_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.34877681732177734 +INFO:pyaf.std:START_FORECASTING '['Close']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Close']' 0.3647611141204834 Forecast Columns Index(['Date', 'Close', 'row_number', 'Date_Normalized', '_Close', '_Close_Lag1Trend', '_Close_Lag1Trend_residue', '_Close_Lag1Trend_residue_zeroCycle', diff --git a/tests/references/bugs_issue_55_grouping_issue_55_notebook.log b/tests/references/bugs_issue_55_grouping_issue_55_notebook.log index 895e3fb72..e2c85a779 100644 --- a/tests/references/bugs_issue_55_grouping_issue_55_notebook.log +++ b/tests/references/bugs_issue_55_grouping_issue_55_notebook.log @@ -1,249 +1,973 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.4151287078857422 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.2743091583251953 INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ALSACE_BLANC_BE'), (0, 'ALSACE_BLANC_CN'), (0, 'ALSACE_BLANC_DE'), (0, 'ALSACE_BLANC_GB'), (0, 'ALSACE_BLANC_US'), (0, 'BEAUJOLAIS_ROUGE_BE'), (0, 'BEAUJOLAIS_ROUGE_CN'), (0, 'BEAUJOLAIS_ROUGE_DE'), (0, 'BEAUJOLAIS_ROUGE_GB'), (0, 'BEAUJOLAIS_ROUGE_US'), (0, 'BORDEAUX_BLANC_BE'), (0, 'BORDEAUX_BLANC_CN'), (0, 'BORDEAUX_BLANC_DE'), (0, 'BORDEAUX_BLANC_GB'), (0, 'BORDEAUX_BLANC_US'), (0, 'BORDEAUX_ROUGE_BE'), (0, 'BORDEAUX_ROUGE_CN'), (0, 'BORDEAUX_ROUGE_DE'), (0, 'BORDEAUX_ROUGE_GB'), (0, 'BORDEAUX_ROUGE_US'), (1, '_BLANC_BE'), (1, '_BLANC_CN'), (1, '_BLANC_DE'), (1, '_BLANC_GB'), (1, '_BLANC_US'), (1, '_ROUGE_BE'), (1, '_ROUGE_CN'), (1, '_ROUGE_DE'), (1, '_ROUGE_GB'), (1, '_ROUGE_US'), (2, '__BE'), (2, '__CN'), (2, '__DE'), (2, '__GB'), (2, '__US'), (3, '__')] +INFO:pyaf.std:START_TRAINING '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' {'Levels': None, 'Data': None, 'Groups': {'Country': ['GB', 'US', 'DE', 'BE', 'CN'], 'Variant': ['BLANC', 'ROUGE'], 'Wine': ['ALSACE', 'BEAUJOLAIS', 'BORDEAUX']}, 'GroupOrder': ['Wine', 'Variant', 'Country'], 'Type': 'Grouped'} -{0: {'ALSACE_BLANC_BE': set(), 'ALSACE_BLANC_CN': set(), 'ALSACE_BLANC_DE': set(), 'ALSACE_BLANC_GB': set(), 'ALSACE_BLANC_US': set(), 'BEAUJOLAIS_ROUGE_BE': set(), 'BEAUJOLAIS_ROUGE_CN': set(), 'BEAUJOLAIS_ROUGE_DE': set(), 'BEAUJOLAIS_ROUGE_GB': set(), 'BEAUJOLAIS_ROUGE_US': set(), 'BORDEAUX_BLANC_BE': set(), 'BORDEAUX_BLANC_CN': set(), 'BORDEAUX_BLANC_DE': set(), 'BORDEAUX_BLANC_GB': set(), 'BORDEAUX_BLANC_US': set(), 'BORDEAUX_ROUGE_BE': set(), 'BORDEAUX_ROUGE_CN': set(), 'BORDEAUX_ROUGE_DE': set(), 'BORDEAUX_ROUGE_GB': set(), 'BORDEAUX_ROUGE_US': set()}, 1: {'_BLANC_BE': {'BORDEAUX_BLANC_BE', 'ALSACE_BLANC_BE'}, '_BLANC_CN': {'ALSACE_BLANC_CN', 'BORDEAUX_BLANC_CN'}, '_BLANC_DE': {'BORDEAUX_BLANC_DE', 'ALSACE_BLANC_DE'}, '_BLANC_GB': {'ALSACE_BLANC_GB', 'BORDEAUX_BLANC_GB'}, '_BLANC_US': {'ALSACE_BLANC_US', 'BORDEAUX_BLANC_US'}, '_ROUGE_BE': {'BORDEAUX_ROUGE_BE', 'BEAUJOLAIS_ROUGE_BE'}, '_ROUGE_CN': {'BEAUJOLAIS_ROUGE_CN', 'BORDEAUX_ROUGE_CN'}, '_ROUGE_DE': {'BORDEAUX_ROUGE_DE', 'BEAUJOLAIS_ROUGE_DE'}, '_ROUGE_GB': {'BORDEAUX_ROUGE_GB', 'BEAUJOLAIS_ROUGE_GB'}, '_ROUGE_US': {'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_ROUGE_US'}}, 2: {'__BE': {'_BLANC_BE', '_ROUGE_BE'}, '__CN': {'_BLANC_CN', '_ROUGE_CN'}, '__DE': {'_BLANC_DE', '_ROUGE_DE'}, '__GB': {'_BLANC_GB', '_ROUGE_GB'}, '__US': {'_BLANC_US', '_ROUGE_US'}}, 3: {'__': {'__BE', '__US', '__DE', '__CN', '__GB'}}} -INFO:pyaf.std:START_TRAINING 'ALSACE_BLANC_BE' -INFO:pyaf.std:START_TRAINING 'ALSACE_BLANC_DE' -INFO:pyaf.std:START_TRAINING 'ALSACE_BLANC_US' -INFO:pyaf.std:START_TRAINING 'BEAUJOLAIS_ROUGE_CN' -INFO:pyaf.std:START_TRAINING 'BEAUJOLAIS_ROUGE_GB' -INFO:pyaf.std:START_TRAINING 'BORDEAUX_BLANC_BE' -INFO:pyaf.std:START_TRAINING 'BORDEAUX_BLANC_US' -INFO:pyaf.std:START_TRAINING 'BORDEAUX_BLANC_DE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_BLANC_DE' 6.511942386627197 -INFO:pyaf.std:START_TRAINING 'BORDEAUX_BLANC_GB' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_BLANC_US' 6.68208384513855 -INFO:pyaf.std:START_TRAINING 'BORDEAUX_ROUGE_BE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ALSACE_BLANC_DE' 6.867068290710449 -INFO:pyaf.std:START_TRAINING 'ALSACE_BLANC_GB' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BEAUJOLAIS_ROUGE_CN' 7.782456636428833 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_BLANC_BE' 7.768961191177368 -INFO:pyaf.std:START_TRAINING 'BORDEAUX_BLANC_CN' -INFO:pyaf.std:START_TRAINING 'BEAUJOLAIS_ROUGE_DE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ALSACE_BLANC_BE' 7.980624198913574 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BEAUJOLAIS_ROUGE_GB' 7.885958194732666 -INFO:pyaf.std:START_TRAINING 'ALSACE_BLANC_CN' -INFO:pyaf.std:START_TRAINING 'BEAUJOLAIS_ROUGE_US' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ALSACE_BLANC_US' 8.507474184036255 -INFO:pyaf.std:START_TRAINING 'BEAUJOLAIS_ROUGE_BE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_ROUGE_BE' 7.421518087387085 -INFO:pyaf.std:START_TRAINING 'BORDEAUX_ROUGE_CN' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_BLANC_GB' 7.95048713684082 -INFO:pyaf.std:START_TRAINING 'BORDEAUX_ROUGE_GB' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BEAUJOLAIS_ROUGE_DE' 7.331292152404785 -INFO:pyaf.std:START_TRAINING '_BLANC_BE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_BLANC_CN' 8.054427146911621 -INFO:pyaf.std:START_TRAINING '_BLANC_DE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BEAUJOLAIS_ROUGE_BE' 7.752685308456421 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ALSACE_BLANC_GB' 9.46520447731018 -INFO:pyaf.std:START_TRAINING '_BLANC_US' -INFO:pyaf.std:START_TRAINING '_ROUGE_CN' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ALSACE_BLANC_CN' 8.482020616531372 -INFO:pyaf.std:START_TRAINING '_ROUGE_GB' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BEAUJOLAIS_ROUGE_US' 8.643223285675049 -INFO:pyaf.std:START_TRAINING '__BE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_ROUGE_CN' 6.7529802322387695 -INFO:pyaf.std:START_TRAINING 'BORDEAUX_ROUGE_DE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_ROUGE_GB' 7.1798095703125 -INFO:pyaf.std:START_TRAINING 'BORDEAUX_ROUGE_US' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_BLANC_BE' 6.605534076690674 -INFO:pyaf.std:START_TRAINING '_BLANC_CN' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_BLANC_DE' 6.735365152359009 -INFO:pyaf.std:START_TRAINING '_BLANC_GB' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_ROUGE_GB' 6.685166597366333 -INFO:pyaf.std:START_TRAINING '_ROUGE_US' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_BLANC_US' 7.02920126914978 -INFO:pyaf.std:START_TRAINING '_ROUGE_BE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '__BE' 7.371317148208618 -INFO:pyaf.std:START_TRAINING '__CN' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_ROUGE_CN' 7.617284059524536 -INFO:pyaf.std:START_TRAINING '_ROUGE_DE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_ROUGE_DE' 6.696187496185303 -INFO:pyaf.std:START_TRAINING '__DE' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_BLANC_CN' 6.073875665664673 -INFO:pyaf.std:START_TRAINING '__US' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BORDEAUX_ROUGE_US' 6.522196531295776 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_BLANC_GB' 5.937647819519043 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_ROUGE_BE' 6.081986665725708 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '__CN' 5.587504625320435 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_ROUGE_US' 6.46966290473938 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_ROUGE_DE' 6.15573263168335 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '__DE' 4.797291040420532 -INFO:pyaf.std:START_TRAINING '__GB' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '__US' 4.748921871185303 -INFO:pyaf.std:START_TRAINING '__' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '__' 4.288451194763184 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '__GB' 4.519926071166992 -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ALSACE_BLANC_BE'), (0, 'ALSACE_BLANC_CN'), (0, 'ALSACE_BLANC_DE'), (0, 'ALSACE_BLANC_GB'), (0, 'ALSACE_BLANC_US'), (0, 'BEAUJOLAIS_ROUGE_BE'), (0, 'BEAUJOLAIS_ROUGE_CN'), (0, 'BEAUJOLAIS_ROUGE_DE'), (0, 'BEAUJOLAIS_ROUGE_GB'), (0, 'BEAUJOLAIS_ROUGE_US'), (0, 'BORDEAUX_BLANC_BE'), (0, 'BORDEAUX_BLANC_CN'), (0, 'BORDEAUX_BLANC_DE'), (0, 'BORDEAUX_BLANC_GB'), (0, 'BORDEAUX_BLANC_US'), (0, 'BORDEAUX_ROUGE_BE'), (0, 'BORDEAUX_ROUGE_CN'), (0, 'BORDEAUX_ROUGE_DE'), (0, 'BORDEAUX_ROUGE_GB'), (0, 'BORDEAUX_ROUGE_US'), (1, '_BLANC_BE'), (1, '_BLANC_CN'), (1, '_BLANC_DE'), (1, '_BLANC_GB'), (1, '_BLANC_US'), (1, '_ROUGE_BE'), (1, '_ROUGE_CN'), (1, '_ROUGE_DE'), (1, '_ROUGE_GB'), (1, '_ROUGE_US'), (2, '__BE'), (2, '__CN'), (2, '__DE'), (2, '__GB'), (2, '__US'), (3, '__')] -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.0650782585144043 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.15339303016662598 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.0778648853302002 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.0773916244506836 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.06245088577270508 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.057175397872924805 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.056793212890625 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.05188751220703125 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.06361889839172363 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.053084611892700195 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.02879929542541504 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.05890202522277832 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.05069255828857422 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.06350946426391602 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.052474260330200195 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.051283836364746094 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.06387019157409668 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.04843902587890625 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.04794454574584961 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.05652451515197754 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.05813741683959961 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.04352569580078125 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.04756522178649902 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.07496261596679688 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.056856393814086914 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.05247187614440918 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.0576324462890625 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.0509035587310791 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.07494044303894043 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.062145233154296875 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.06197094917297363 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.0629420280456543 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.05172371864318848 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.04593825340270996 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.04519343376159668 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.06953811645507812 -INFO:pyaf.std:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.std:DATASET_COLUMNS Index(['Month', 'ALSACE_BLANC_BE', 'ALSACE_BLANC_BE_Forecast', - 'ALSACE_BLANC_CN', 'ALSACE_BLANC_CN_Forecast', 'ALSACE_BLANC_DE', - 'ALSACE_BLANC_DE_Forecast', 'ALSACE_BLANC_GB', - 'ALSACE_BLANC_GB_Forecast', 'ALSACE_BLANC_US', +{0: {'ALSACE_BLANC_BE': [], 'ALSACE_BLANC_CN': [], 'ALSACE_BLANC_DE': [], 'ALSACE_BLANC_GB': [], 'ALSACE_BLANC_US': [], 'BEAUJOLAIS_ROUGE_BE': [], 'BEAUJOLAIS_ROUGE_CN': [], 'BEAUJOLAIS_ROUGE_DE': [], 'BEAUJOLAIS_ROUGE_GB': [], 'BEAUJOLAIS_ROUGE_US': [], 'BORDEAUX_BLANC_BE': [], 'BORDEAUX_BLANC_CN': [], 'BORDEAUX_BLANC_DE': [], 'BORDEAUX_BLANC_GB': [], 'BORDEAUX_BLANC_US': [], 'BORDEAUX_ROUGE_BE': [], 'BORDEAUX_ROUGE_CN': [], 'BORDEAUX_ROUGE_DE': [], 'BORDEAUX_ROUGE_GB': [], 'BORDEAUX_ROUGE_US': []}, 1: {'_BLANC_BE': ['ALSACE_BLANC_BE', 'BORDEAUX_BLANC_BE'], '_BLANC_CN': ['ALSACE_BLANC_CN', 'BORDEAUX_BLANC_CN'], '_BLANC_DE': ['ALSACE_BLANC_DE', 'BORDEAUX_BLANC_DE'], '_BLANC_GB': ['ALSACE_BLANC_GB', 'BORDEAUX_BLANC_GB'], '_BLANC_US': ['ALSACE_BLANC_US', 'BORDEAUX_BLANC_US'], '_ROUGE_BE': ['BEAUJOLAIS_ROUGE_BE', 'BORDEAUX_ROUGE_BE'], '_ROUGE_CN': ['BEAUJOLAIS_ROUGE_CN', 'BORDEAUX_ROUGE_CN'], '_ROUGE_DE': ['BEAUJOLAIS_ROUGE_DE', 'BORDEAUX_ROUGE_DE'], '_ROUGE_GB': ['BEAUJOLAIS_ROUGE_GB', 'BORDEAUX_ROUGE_GB'], '_ROUGE_US': ['BEAUJOLAIS_ROUGE_US', 'BORDEAUX_ROUGE_US']}, 2: {'__BE': ['_BLANC_BE', '_ROUGE_BE'], '__CN': ['_BLANC_CN', '_ROUGE_CN'], '__DE': ['_BLANC_DE', '_ROUGE_DE'], '__GB': ['_BLANC_GB', '_ROUGE_GB'], '__US': ['_BLANC_US', '_ROUGE_US']}, 3: {'__': ['__BE', '__CN', '__DE', '__GB', '__US']}} +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' 43.26012134552002 +INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS +INFO:pyaf.std:START_FORECASTING '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' 8.786235570907593 +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD +INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Month', 'ALSACE_BLANC_BE', 'ALSACE_BLANC_BE_Forecast', + 'ALSACE_BLANC_BE_Forecast_Lower_Bound', + 'ALSACE_BLANC_BE_Forecast_Upper_Bound', 'ALSACE_BLANC_CN', + 'ALSACE_BLANC_CN_Forecast', 'ALSACE_BLANC_CN_Forecast_Lower_Bound', + 'ALSACE_BLANC_CN_Forecast_Upper_Bound', 'ALSACE_BLANC_DE', ... 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[NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff__ROUGE_DE_LinearTrend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.347 MAPE_Forecast=0.2428 MAPE_Test=0.5046 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3 SMAPE_Forecast=0.1972 SMAPE_Test=0.403 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.0612 MASE_Forecast=0.7929 MASE_Test=None +INFO:pyaf.std:MODEL_L1 L1_Fit=1800173.5358779323 L1_Forecast=876283.8253709921 L1_Test=1549688.5003144704 +INFO:pyaf.std:MODEL_L2 L2_Fit=2277685.553655341 L2_Forecast=1118073.196200151 L2_Test=1549688.5003144704 +INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 5803328.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (50875.054710970326, array([-112299.32557484])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES 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+INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '___DE_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '___DE_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '___DE_Lag1Trend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '___DE_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2663 MAPE_Forecast=0.2377 MAPE_Test=0.2269 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2562 SMAPE_Forecast=0.2098 SMAPE_Test=0.2037 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9722 MASE_Forecast=1.0209 MASE_Test=None +INFO:pyaf.std:MODEL_L1 L1_Fit=1780306.3333333333 L1_Forecast=1107360.888888889 L1_Test=868251.0 +INFO:pyaf.std:MODEL_L2 L2_Fit=2608066.128208228 L2_Forecast=1311702.0584794695 L2_Test=868251.0 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 6508552.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES ___DE_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Diff___CN_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.3585143511228835 -INFO:pyaf.std:AR_MODEL_COEFF 2 Diff___CN_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.35356774613799713 -INFO:pyaf.std:AR_MODEL_COEFF 3 Diff___CN_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.2558327092791704 -INFO:pyaf.std:AR_MODEL_COEFF 4 Diff___CN_ConstantTrend_residue_zeroCycle_residue_Lag7 0.1268650172641117 -INFO:pyaf.std:AR_MODEL_COEFF 5 Diff___CN_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.08451565226966903 -INFO:pyaf.std:AR_MODEL_COEFF 6 Diff___CN_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.05216781461233465 -INFO:pyaf.std:AR_MODEL_COEFF 7 Diff___CN_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.03460543608523329 -INFO:pyaf.std:AR_MODEL_COEFF 8 Diff___CN_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.030658322709511424 -INFO:pyaf.std:AR_MODEL_COEFF 9 Diff___CN_ConstantTrend_residue_zeroCycle_residue_Lag1 0.017510237302299364 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:TIME_DETAIL TimeVariable='Month' TimeMin=2012-01-01T00:00:00.000000 TimeMax=2014-12-01T00:00:00.000000 TimeDelta= Horizon=1 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='__GB' Length=46 Min=4584449 Max=34054975 Mean=13642437.52173913 StdDev=7330809.1345297 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='___GB' Min=4584449 Max=34054975 Mean=13642437.52173913 StdDev=7330809.1345297 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '___GB_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '___GB_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '___GB_Lag1Trend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '___GB_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3503 MAPE_Forecast=0.1579 MAPE_Test=0.0961 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3066 SMAPE_Forecast=0.1538 SMAPE_Test=0.1009 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9722 MASE_Forecast=0.9576 MASE_Test=None +INFO:pyaf.std:MODEL_L1 L1_Fit=4173764.3333333335 L1_Forecast=1230684.111111111 L1_Test=840216.0 +INFO:pyaf.std:MODEL_L2 L2_Fit=5023345.709032571 L2_Forecast=1567222.4394758958 L2_Test=840216.0 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 16811090 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES ___GB_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:TIME_DETAIL TimeVariable='Month' TimeMin=2012-01-01T00:00:00.000000 TimeMax=2014-12-01T00:00:00.000000 TimeDelta= Horizon=1 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='__US' Length=46 Min=5868951 Max=17185987 Mean=9945897.913043479 StdDev=2510136.266406258 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='___US' Min=5868951 Max=17185987 Mean=9945897.913043479 StdDev=2510136.266406258 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '___US_LinearTrend_residue_zeroCycle_residue_NoAR' [LinearTrend + NoCycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '___US_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '___US_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '___US_LinearTrend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1917 MAPE_Forecast=0.2209 MAPE_Test=0.2236 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1847 SMAPE_Forecast=0.2023 SMAPE_Test=0.2011 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7802 MASE_Forecast=0.7157 MASE_Test=None +INFO:pyaf.std:MODEL_L1 L1_Fit=1922713.7690322092 L1_Forecast=1789928.428711845 L1_Test=1632606.6700245067 +INFO:pyaf.std:MODEL_L2 L2_Fit=2510501.287288395 L2_Forecast=2096609.4319779796 L2_Test=1632606.6700245067 +INFO:pyaf.std:MODEL_COMPLEXITY 16 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (10887439.458791293, array([-1314881.70988409])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES ___US_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:TIME_DETAIL TimeVariable='Month' TimeMin=2012-01-01T00:00:00.000000 TimeMax=2014-12-01T00:00:00.000000 TimeDelta= Horizon=1 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='__' Length=46 Min=28850872.5 Max=74341125.5 Mean=49180545.10869565 StdDev=11452870.277739467 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='___' Min=28850872.5 Max=74341125.5 Mean=49180545.10869565 StdDev=11452870.277739467 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '____LinearTrend_residue_zeroCycle_residue_AR(11)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '____LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '____LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '____LinearTrend_residue_zeroCycle_residue_AR(11)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1073 MAPE_Forecast=0.1367 MAPE_Test=0.0933 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.104 SMAPE_Forecast=0.1411 SMAPE_Test=0.0891 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.634 MASE_Forecast=0.7528 MASE_Test=None +INFO:pyaf.std:MODEL_L1 L1_Fit=5311578.465220075 L1_Forecast=5309854.235871161 L1_Test=3345292.239105746 +INFO:pyaf.std:MODEL_L2 L2_Fit=6571495.053618537 L2_Forecast=6516614.471320467 L2_Test=3345292.239105746 +INFO:pyaf.std:MODEL_COMPLEXITY 25 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (60068064.09053329, array([-16842619.64399239])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES ____LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 ____LinearTrend_residue_zeroCycle_residue_Lag1 0.6742205685754659 +INFO:pyaf.std:AR_MODEL_COEFF 2 ____LinearTrend_residue_zeroCycle_residue_Lag2 -0.33971284196078616 +INFO:pyaf.std:AR_MODEL_COEFF 3 ____LinearTrend_residue_zeroCycle_residue_Lag9 -0.2978646833417023 +INFO:pyaf.std:AR_MODEL_COEFF 4 ____LinearTrend_residue_zeroCycle_residue_Lag5 0.15990040041970066 +INFO:pyaf.std:AR_MODEL_COEFF 5 ____LinearTrend_residue_zeroCycle_residue_Lag6 -0.11134600418577098 +INFO:pyaf.std:AR_MODEL_COEFF 6 ____LinearTrend_residue_zeroCycle_residue_Lag10 0.1059902481535163 +INFO:pyaf.std:AR_MODEL_COEFF 7 ____LinearTrend_residue_zeroCycle_residue_Lag3 -0.059348058951261634 +INFO:pyaf.std:AR_MODEL_COEFF 8 ____LinearTrend_residue_zeroCycle_residue_Lag8 0.0348269220197896 +INFO:pyaf.std:AR_MODEL_COEFF 9 ____LinearTrend_residue_zeroCycle_residue_Lag4 0.016475437012483496 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 1.1659939289093018 +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Trend' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Trend_residue' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Trend_residue' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Cycle' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Cycle_residue' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Cycle_residue' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_AR' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_AR_residue' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_TransformedForecast' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_TransformedResidue' which cannot be automatically added to the legend. + ax.legend(patched_names) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.778441190719604 INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ALSACE_BLANC_BE'), (0, 'ALSACE_BLANC_CN'), (0, 'ALSACE_BLANC_DE'), (0, 'ALSACE_BLANC_GB'), (0, 'ALSACE_BLANC_US'), (0, 'BEAUJOLAIS_ROUGE_BE'), (0, 'BEAUJOLAIS_ROUGE_CN'), (0, 'BEAUJOLAIS_ROUGE_DE'), (0, 'BEAUJOLAIS_ROUGE_GB'), (0, 'BEAUJOLAIS_ROUGE_US'), (0, 'BORDEAUX_BLANC_BE'), (0, 'BORDEAUX_BLANC_CN'), (0, 'BORDEAUX_BLANC_DE'), (0, 'BORDEAUX_BLANC_GB'), (0, 'BORDEAUX_BLANC_US'), (0, 'BORDEAUX_ROUGE_BE'), (0, 'BORDEAUX_ROUGE_CN'), (0, 'BORDEAUX_ROUGE_DE'), (0, 'BORDEAUX_ROUGE_GB'), (0, 'BORDEAUX_ROUGE_US'), (1, '_BLANC_BE'), (1, '_BLANC_CN'), (1, '_BLANC_DE'), (1, '_BLANC_GB'), (1, '_BLANC_US'), (1, '_ROUGE_BE'), (1, '_ROUGE_CN'), (1, '_ROUGE_DE'), (1, '_ROUGE_GB'), (1, '_ROUGE_US'), (2, '__BE'), (2, '__CN'), (2, '__DE'), (2, '__GB'), (2, '__US'), (3, '__')] +INFO:pyaf.std:START_FORECASTING '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' RangeIndex: 50 entries, 0 to 49 Columns: 177 entries, Month to RHÔNE_ROUGE_US dtypes: datetime64[ns](1), float64(56), int64(120) memory usage: 69.3 KB dict_keys(['Structure', 'Models']) -{0: {'ALSACE_BLANC_BE': set(), 'ALSACE_BLANC_CN': set(), 'ALSACE_BLANC_DE': set(), 'ALSACE_BLANC_GB': set(), 'ALSACE_BLANC_US': set(), 'BEAUJOLAIS_ROUGE_BE': set(), 'BEAUJOLAIS_ROUGE_CN': set(), 'BEAUJOLAIS_ROUGE_DE': set(), 'BEAUJOLAIS_ROUGE_GB': set(), 'BEAUJOLAIS_ROUGE_US': set(), 'BORDEAUX_BLANC_BE': set(), 'BORDEAUX_BLANC_CN': set(), 'BORDEAUX_BLANC_DE': set(), 'BORDEAUX_BLANC_GB': set(), 'BORDEAUX_BLANC_US': set(), 'BORDEAUX_ROUGE_BE': set(), 'BORDEAUX_ROUGE_CN': set(), 'BORDEAUX_ROUGE_DE': set(), 'BORDEAUX_ROUGE_GB': set(), 'BORDEAUX_ROUGE_US': set()}, 1: {'_BLANC_BE': {'BORDEAUX_BLANC_BE', 'ALSACE_BLANC_BE'}, '_BLANC_CN': {'ALSACE_BLANC_CN', 'BORDEAUX_BLANC_CN'}, '_BLANC_DE': {'BORDEAUX_BLANC_DE', 'ALSACE_BLANC_DE'}, '_BLANC_GB': {'ALSACE_BLANC_GB', 'BORDEAUX_BLANC_GB'}, '_BLANC_US': {'ALSACE_BLANC_US', 'BORDEAUX_BLANC_US'}, '_ROUGE_BE': {'BORDEAUX_ROUGE_BE', 'BEAUJOLAIS_ROUGE_BE'}, '_ROUGE_CN': {'BEAUJOLAIS_ROUGE_CN', 'BORDEAUX_ROUGE_CN'}, '_ROUGE_DE': {'BORDEAUX_ROUGE_DE', 'BEAUJOLAIS_ROUGE_DE'}, '_ROUGE_GB': {'BORDEAUX_ROUGE_GB', 'BEAUJOLAIS_ROUGE_GB'}, '_ROUGE_US': {'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_ROUGE_US'}}, 2: {'__BE': {'_BLANC_BE', '_ROUGE_BE'}, '__CN': {'_BLANC_CN', '_ROUGE_CN'}, '__DE': {'_BLANC_DE', '_ROUGE_DE'}, '__GB': {'_BLANC_GB', '_ROUGE_GB'}, '__US': {'_BLANC_US', '_ROUGE_US'}}, 3: {'__': {'__BE', '__US', '__DE', '__CN', '__GB'}}} +{0: {'ALSACE_BLANC_BE': [], 'ALSACE_BLANC_CN': [], 'ALSACE_BLANC_DE': [], 'ALSACE_BLANC_GB': [], 'ALSACE_BLANC_US': [], 'BEAUJOLAIS_ROUGE_BE': [], 'BEAUJOLAIS_ROUGE_CN': [], 'BEAUJOLAIS_ROUGE_DE': [], 'BEAUJOLAIS_ROUGE_GB': [], 'BEAUJOLAIS_ROUGE_US': [], 'BORDEAUX_BLANC_BE': [], 'BORDEAUX_BLANC_CN': [], 'BORDEAUX_BLANC_DE': [], 'BORDEAUX_BLANC_GB': [], 'BORDEAUX_BLANC_US': [], 'BORDEAUX_ROUGE_BE': [], 'BORDEAUX_ROUGE_CN': [], 'BORDEAUX_ROUGE_DE': [], 'BORDEAUX_ROUGE_GB': [], 'BORDEAUX_ROUGE_US': []}, 1: {'_BLANC_BE': ['ALSACE_BLANC_BE', 'BORDEAUX_BLANC_BE'], '_BLANC_CN': ['ALSACE_BLANC_CN', 'BORDEAUX_BLANC_CN'], '_BLANC_DE': ['ALSACE_BLANC_DE', 'BORDEAUX_BLANC_DE'], '_BLANC_GB': ['ALSACE_BLANC_GB', 'BORDEAUX_BLANC_GB'], '_BLANC_US': ['ALSACE_BLANC_US', 'BORDEAUX_BLANC_US'], '_ROUGE_BE': ['BEAUJOLAIS_ROUGE_BE', 'BORDEAUX_ROUGE_BE'], '_ROUGE_CN': ['BEAUJOLAIS_ROUGE_CN', 'BORDEAUX_ROUGE_CN'], '_ROUGE_DE': ['BEAUJOLAIS_ROUGE_DE', 'BORDEAUX_ROUGE_DE'], '_ROUGE_GB': ['BEAUJOLAIS_ROUGE_GB', 'BORDEAUX_ROUGE_GB'], '_ROUGE_US': ['BEAUJOLAIS_ROUGE_US', 'BORDEAUX_ROUGE_US']}, 2: {'__BE': ['_BLANC_BE', '_ROUGE_BE'], '__CN': ['_BLANC_CN', '_ROUGE_CN'], '__DE': ['_BLANC_DE', '_ROUGE_DE'], '__GB': ['_BLANC_GB', '_ROUGE_GB'], '__US': ['_BLANC_US', '_ROUGE_US']}, 3: {'__': ['__BE', '__CN', '__DE', '__GB', '__US']}} dict_keys(['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']) -{'Dataset': {'Time': {'TimeVariable': 'Month', 'TimeMinMax': ['2012-01-01 00:00:00', '2016-05-01 00:00:00'], 'Horizon': 1}, 'Signal': 'BORDEAUX_ROUGE_CN', 'Training_Signal_Length': 46}, 'Model': {'Best_Decomposition': 'Diff_BORDEAUX_ROUGE_CN_ConstantTrend_residue_zeroCycle_residue_AR(11)', 'Signal_Transoformation': 'Difference', 'Trend': 'ConstantTrend', 'Cycle': 'NoCycle', 'AR_Model': 'AR'}, 'Model_Performance': {'MAPE': '0.2216', 'MASE': '0.7022', 'MAE': '2110502.468960095', 'RMSE': '2646130.4232281293', 'COMPLEXITY': '41'}} +{'Dataset': {'Time': {'TimeVariable': 'Month', 'TimeMinMax': ['2012-01-01 00:00:00', '2016-05-01 00:00:00'], 'Horizon': 1}, 'Signal': 'BORDEAUX_ROUGE_CN', 'Training_Signal_Length': 46}, 'Model': {'Best_Decomposition': 'Diff_BORDEAUX_ROUGE_CN_ConstantTrend_residue_zeroCycle_residue_AR(11)', 'Signal_Transoformation': 'Difference', 'Trend': 'ConstantTrend', 'Cycle': 'NoCycle', 'AR_Model': 'AR'}, 'Model_Performance': {'MAPE': '0.2216', 'MASE': '0.7022', 'MAE': '2110502.4689600933', 'RMSE': '2646130.4232281283', 'COMPLEXITY': '41'}} Model RMSE MAPE -13 BORDEAUX_BLANC_GB 99936.68789248551 0.133 -30 __ 6516614.471320466 0.1367 +13 BORDEAUX_BLANC_GB 99936.68789248545 0.133 +30 __ 6516614.471320467 0.1367 22 _BLANC_DE 141608.55178546874 0.137 3 ALSACE_BLANC_GB 66480.16663925201 0.1402 -23 _BLANC_GB 194430.4785616541 0.1475 -29 _ROUGE_US 1353323.9885370731 0.1533 +23 _BLANC_GB 194430.47856165405 0.1475 +29 _ROUGE_US 1353323.9885370727 0.1533 34 __GB 1567222.4394758958 0.1579 18 BORDEAUX_ROUGE_GB 1259434.9100598427 0.1592 28 _ROUGE_GB 1442701.9862164112 0.1611 -31 __BE 1150941.4629769186 0.1693 +31 __BE 1150941.4629769188 0.1693 2 ALSACE_BLANC_DE 144769.4922736975 0.1906 -15 BORDEAUX_ROUGE_BE 921082.2727095842 0.1915 +15 BORDEAUX_ROUGE_BE 921082.2727095841 0.1915 24 _BLANC_US 406290.2059059647 0.1927 -19 BORDEAUX_ROUGE_US 1279973.4076019498 0.1988 +19 BORDEAUX_ROUGE_US 1279973.40760195 0.1988 25 _ROUGE_BE 946236.4320116949 0.2045 -14 BORDEAUX_BLANC_US 252977.83561553678 0.2096 +14 BORDEAUX_BLANC_US 252977.83561553687 0.2096 35 __US 2096609.4319779796 0.2209 -16 BORDEAUX_ROUGE_CN 2646130.4232281293 0.2216 -26 _ROUGE_CN 2626225.262002647 0.2253 -32 __CN 2852608.7810445046 0.2353 +16 BORDEAUX_ROUGE_CN 2646130.4232281283 0.2216 +26 _ROUGE_CN 2626225.262002648 0.2253 +32 __CN 2852608.7810445037 0.2353 33 __DE 1311702.0584794695 0.2377 4 ALSACE_BLANC_US 178019.5721837483 0.238 27 _ROUGE_DE 1118073.196200151 0.2428 12 BORDEAUX_BLANC_DE 201387.2590557804 0.2533 21 _BLANC_CN 447221.0158893082 0.255 20 _BLANC_BE 536239.7343081034 0.2702 -17 BORDEAUX_ROUGE_DE 1186136.6973808934 0.272 +17 BORDEAUX_ROUGE_DE 1186136.6973808932 0.272 9 BEAUJOLAIS_ROUGE_US 774435.6013471647 0.2867 0 ALSACE_BLANC_BE 444837.29072987556 0.2979 11 BORDEAUX_BLANC_CN 477151.95433478977 0.306 10 BORDEAUX_BLANC_BE 133791.73715896055 0.3373 -1 ALSACE_BLANC_CN 86153.69023954056 0.3401 +1 ALSACE_BLANC_CN 86153.69023954055 0.3401 8 BEAUJOLAIS_ROUGE_GB 485564.32192152576 0.3584 -5 BEAUJOLAIS_ROUGE_BE 126806.98617848834 0.4458 +5 BEAUJOLAIS_ROUGE_BE 126806.9861784883 0.4458 7 BEAUJOLAIS_ROUGE_DE 155871.73582628762 0.6144 6 BEAUJOLAIS_ROUGE_CN 190554.48176906724 1.0 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.22686529159545898 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.21401214599609375 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.4576742649078369 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.2838611602783203 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.29390645027160645 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.3293135166168213 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.24890351295471191 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.22737717628479004 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.3069181442260742 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.17641544342041016 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.13461828231811523 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.2714345455169678 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.30959463119506836 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.23703384399414062 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.15935897827148438 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.2013230323791504 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.2022850513458252 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.26719212532043457 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.16005229949951172 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.23560547828674316 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.1842513084411621 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.13289308547973633 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.13127660751342773 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.169769287109375 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.14304780960083008 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.22907161712646484 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.18456220626831055 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.14737606048583984 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.16698002815246582 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.24358081817626953 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.395519495010376 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.26930713653564453 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.19596576690673828 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.13898587226867676 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.13396286964416504 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.17552876472473145 -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 18.52554750442505 -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB' which cannot be automatically added to the legend. - ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_Forecast' which cannot be automatically added to the legend. - ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_BU_Forecast' which cannot be automatically added to the legend. - ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_PHA_TD_Forecast' which cannot be automatically added to the legend. - ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_AHP_TD_Forecast' which cannot be automatically added to the legend. - ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_MO_Forecast' which cannot be automatically added to the legend. - ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_OC_Forecast' which cannot be automatically added to the legend. +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' 16.19733691215515 +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 16.418500900268555 +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig = self.plt.figure(figsize=self.figsize) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB' which cannot be automatically added to the legend. + ax.legend(handles, labels, loc="best", title=title) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_Forecast' which cannot be automatically added to the legend. + ax.legend(handles, labels, loc="best", title=title) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_BU_Forecast' which cannot be automatically added to the legend. + ax.legend(handles, labels, loc="best", title=title) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_PHA_TD_Forecast' which cannot be automatically added to the legend. + ax.legend(handles, labels, loc="best", title=title) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_AHP_TD_Forecast' which cannot be automatically added to the legend. + ax.legend(handles, labels, loc="best", title=title) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_MO_Forecast' which cannot be automatically added to the legend. + ax.legend(handles, labels, loc="best", title=title) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig = self.plt.figure(figsize=self.figsize) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig = self.plt.figure(figsize=self.figsize) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig = self.plt.figure(figsize=self.figsize) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig = self.plt.figure(figsize=self.figsize) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). + fig = self.plt.figure(figsize=self.figsize) +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. diff --git a/tests/references/bugs_issue_56_issue_56_order1.log b/tests/references/bugs_issue_56_issue_56_order1.log index 8c191af16..9cb868489 100644 --- a/tests/references/bugs_issue_56_issue_56_order1.log +++ b/tests/references/bugs_issue_56_issue_56_order1.log @@ -1,46 +1,35 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 3.310774326324463 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 3.304539442062378 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 3.321798324584961 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 3.416581869125366 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 3.511353015899658 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 3.5192408561706543 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 3.9580304622650146 -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.3872344493865967 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.39468955993652344 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.3873710632324219 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.37558674812316895 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.36716556549072266 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.2927122116088867 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.43752455711364746 -INFO:pyaf.std:STRUCTURE [0, 1, 2] -INFO:pyaf.std:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female_Forecast', 'NSW_male', - 'NSW_male_Forecast', 'VIC_female', 'VIC_female_Forecast', 'VIC_male', - 'VIC_male_Forecast', '_female', '_female_Forecast', '_male', - '_male_Forecast', '_', '__Forecast', 'NSW_female_BU_Forecast', - 'NSW_male_BU_Forecast', 'VIC_female_BU_Forecast', - 'VIC_male_BU_Forecast', '_female_BU_Forecast', '_male_BU_Forecast', - '__BU_Forecast', '__AHP_TD_Forecast', '_female_AHP_TD_Forecast', - '_male_AHP_TD_Forecast', 'NSW_female_AHP_TD_Forecast', - 'VIC_female_AHP_TD_Forecast', 'NSW_male_AHP_TD_Forecast', - 'VIC_male_AHP_TD_Forecast', '__PHA_TD_Forecast', - '_female_PHA_TD_Forecast', '_male_PHA_TD_Forecast', +INFO:pyaf.std:START_TRAINING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 8.575263023376465 +INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 1.5078446865081787 +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD +INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female_Forecast', + 'NSW_female_Forecast_Lower_Bound', 'NSW_female_Forecast_Upper_Bound', + 'NSW_male', 'NSW_male_Forecast', 'NSW_male_Forecast_Lower_Bound', + 'NSW_male_Forecast_Upper_Bound', 'VIC_female', 'VIC_female_Forecast', + 'VIC_female_Forecast_Lower_Bound', 'VIC_female_Forecast_Upper_Bound', + 'VIC_male', 'VIC_male_Forecast', 'VIC_male_Forecast_Lower_Bound', + 'VIC_male_Forecast_Upper_Bound', '_female', '_female_Forecast', + '_female_Forecast_Lower_Bound', '_female_Forecast_Upper_Bound', '_male', + '_male_Forecast', '_male_Forecast_Lower_Bound', + '_male_Forecast_Upper_Bound', '_', '__Forecast', + '__Forecast_Lower_Bound', '__Forecast_Upper_Bound', + 'NSW_female_BU_Forecast', 'NSW_male_BU_Forecast', + 'VIC_female_BU_Forecast', 'VIC_male_BU_Forecast', '_female_BU_Forecast', + '_male_BU_Forecast', '__BU_Forecast', '__AHP_TD_Forecast', + '_female_AHP_TD_Forecast', '_male_AHP_TD_Forecast', + 'NSW_female_AHP_TD_Forecast', 'VIC_female_AHP_TD_Forecast', + 'NSW_male_AHP_TD_Forecast', 'VIC_male_AHP_TD_Forecast', + '__PHA_TD_Forecast', '_female_PHA_TD_Forecast', '_male_PHA_TD_Forecast', 'NSW_female_PHA_TD_Forecast', 'VIC_female_PHA_TD_Forecast', 'NSW_male_PHA_TD_Forecast', 'VIC_male_PHA_TD_Forecast', '_female_MO_Forecast', '_male_MO_Forecast', 'NSW_female_MO_Forecast', @@ -50,83 +39,80 @@ INFO:pyaf.std:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female_Forecast 'VIC_male_OC_Forecast', '_female_OC_Forecast', '_male_OC_Forecast', '__OC_Forecast'], dtype='object') -INFO:pyaf.std:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.std:MODEL_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.std:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.std:MODEL_LEVEL (1, ['_female', '_male']) -INFO:pyaf.std:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.std:MODEL_LEVEL (2, ['_']) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_AHP_TD', 57.10145594629301, 0.0694, 61.88312394961418, 0.0782) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_AHP_TD', 57.10145594629301, 0.0694, 61.88312394961418, 0.0782) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_BU', 57.10145594629301, 0.0694, 57.10145594629301, 0.0694) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_BU', 57.10145594629301, 0.0694, 57.10145594629301, 0.0694) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_MO', 57.10145594629301, 0.0694, 62.32278723251837, 0.0813) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_MO', 57.10145594629301, 0.0694, 62.32278723251837, 0.0813) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_OC', 57.10145594629301, 0.0694, 57.10145594629303, 0.0694) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_OC', 57.10145594629301, 0.0694, 57.10145594629303, 0.0694) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_PHA_TD', 57.10145594629301, 0.0694, 62.016771833343164, 0.0799) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_PHA_TD', 57.10145594629301, 0.0694, 62.016771833343164, 0.0799) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_AHP_TD', 71.16643181268225, 0.0666, 70.39300898637983, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_AHP_TD', 71.16643181268225, 0.0666, 70.39300898637983, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_BU', 71.16643181268225, 0.0666, 71.16643181268225, 0.0666) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_BU', 71.16643181268225, 0.0666, 71.16643181268225, 0.0666) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_MO', 71.16643181268225, 0.0666, 71.08956064805535, 0.0699) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_MO', 71.16643181268225, 0.0666, 71.08956064805535, 0.0699) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_OC', 71.16643181268225, 0.0666, 71.16643181268222, 0.0666) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_OC', 71.16643181268225, 0.0666, 71.16643181268222, 0.0666) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_PHA_TD', 71.16643181268225, 0.0666, 70.70421689408323, 0.0696) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_PHA_TD', 71.16643181268225, 0.0666, 70.70421689408323, 0.0696) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_AHP_TD', 44.6959597006186, 0.0908, 42.920214691597465, 0.086) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_AHP_TD', 44.6959597006186, 0.0908, 42.920214691597465, 0.086) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_BU', 44.6959597006186, 0.0908, 44.6959597006186, 0.0908) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_BU', 44.6959597006186, 0.0908, 44.6959597006186, 0.0908) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_MO', 44.6959597006186, 0.0908, 43.077644041671846, 0.0877) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_MO', 44.6959597006186, 0.0908, 43.077644041671846, 0.0877) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_OC', 44.6959597006186, 0.0908, 44.69595970061861, 0.0908) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_OC', 44.6959597006186, 0.0908, 44.69595970061861, 0.0908) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_PHA_TD', 44.6959597006186, 0.0908, 42.28932734562721, 0.0844) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_PHA_TD', 44.6959597006186, 0.0908, 42.28932734562721, 0.0844) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_AHP_TD', 53.68662996769434, 0.0852, 49.09404571362918, 0.0755) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_AHP_TD', 53.68662996769434, 0.0852, 49.09404571362918, 0.0755) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_BU', 53.68662996769434, 0.0852, 53.68662996769434, 0.0852) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_BU', 53.68662996769434, 0.0852, 53.68662996769434, 0.0852) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_MO', 53.68662996769434, 0.0852, 50.005918797978644, 0.0767) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_MO', 53.68662996769434, 0.0852, 50.005918797978644, 0.0767) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_OC', 53.68662996769434, 0.0852, 53.68662996769434, 0.0852) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_OC', 53.68662996769434, 0.0852, 53.68662996769434, 0.0852) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_PHA_TD', 53.68662996769434, 0.0852, 47.833484231171475, 0.074) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_PHA_TD', 53.68662996769434, 0.0852, 47.833484231171475, 0.074) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__AHP_TD', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__AHP_TD', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__BU', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__BU', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__MO', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__MO', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__OC', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__OC', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__PHA_TD', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__PHA_TD', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_AHP_TD', 82.58390699715808, 0.0683, 80.93013515525995, 0.065) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_AHP_TD', 82.58390699715808, 0.0683, 80.93013515525995, 0.065) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_BU', 82.58390699715808, 0.0683, 82.58390699715808, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_BU', 82.58390699715808, 0.0683, 82.58390699715808, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_MO', 82.58390699715808, 0.0683, 82.58390699715808, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_MO', 82.58390699715808, 0.0683, 82.58390699715808, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_OC', 82.58390699715808, 0.0683, 82.58390699715814, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_OC', 82.58390699715808, 0.0683, 82.58390699715814, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_PHA_TD', 82.58390699715808, 0.0683, 81.16027794640222, 0.0655) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_PHA_TD', 82.58390699715808, 0.0683, 81.16027794640222, 0.0655) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_AHP_TD', 97.29737746217495, 0.0588, 94.32738181170546, 0.0578) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_AHP_TD', 97.29737746217495, 0.0588, 94.32738181170546, 0.0578) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_BU', 97.29737746217495, 0.0588, 97.29737746217495, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_BU', 97.29737746217495, 0.0588, 97.29737746217495, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_MO', 97.29737746217495, 0.0588, 97.29737746217495, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_MO', 97.29737746217495, 0.0588, 97.29737746217495, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_OC', 97.29737746217495, 0.0588, 97.29737746217488, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_OC', 97.29737746217495, 0.0588, 97.29737746217488, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_PHA_TD', 97.29737746217495, 0.0588, 93.96986777755437, 0.0576) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_PHA_TD', 97.29737746217495, 0.0588, 93.96986777755437, 0.0576) -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 5.642602443695068 +INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) +INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) +INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0782, 'RMSE': 61.88312394961418, 'MAE': 47.33891833501301, 'SMAPE': 0.0765, 'ErrorMean': 3.1970833853646603, 'ErrorStdDev': 61.800482907420204, 'R2': 0.911769621603849, 'Pearson': 0.957496276648925} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0782, 'RMSE': 61.88312394961418, 'MAE': 47.33891833501301, 'SMAPE': 0.0765, 'ErrorMean': 3.1970833853646603, 'ErrorStdDev': 61.800482907420204, 'R2': 0.911769621603849, 'Pearson': 0.957496276648925} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0813, 'RMSE': 62.32278723251837, 'MAE': 48.70371692503398, 'SMAPE': 0.079, 'ErrorMean': 7.869221952578178, 'ErrorStdDev': 61.82398526697237, 'R2': 0.9105114608134931, 'Pearson': 0.9559136222127095} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0813, 'RMSE': 62.32278723251837, 'MAE': 48.70371692503398, 'SMAPE': 0.079, 'ErrorMean': 7.869221952578178, 'ErrorStdDev': 61.82398526697237, 'R2': 0.9105114608134931, 'Pearson': 0.9559136222127095} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629308, 'MAE': 43.25423728813573, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389831088, 'ErrorStdDev': 56.547824189519446, 'R2': 0.9248778434795352, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629308, 'MAE': 43.25423728813573, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389831088, 'ErrorStdDev': 56.547824189519446, 'R2': 0.9248778434795352, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0799, 'RMSE': 62.016771833343164, 'MAE': 47.68929397311254, 'SMAPE': 0.0775, 'ErrorMean': 8.015543376062395, 'ErrorStdDev': 61.49659383262953, 'R2': 0.9113881109148605, 'Pearson': 0.957496276648925} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0799, 'RMSE': 62.016771833343164, 'MAE': 47.68929397311254, 'SMAPE': 0.0775, 'ErrorMean': 8.015543376062395, 'ErrorStdDev': 61.49659383262953, 'R2': 0.9113881109148605, 'Pearson': 0.957496276648925} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 70.39300898637983, 'MAE': 55.09053975912032, 'SMAPE': 0.0667, 'ErrorMean': 7.628548216350093, 'ErrorStdDev': 69.97843215068035, 'R2': 0.9319325977712878, 'Pearson': 0.9663700630732057} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 70.39300898637983, 'MAE': 55.09053975912032, 'SMAPE': 0.0667, 'ErrorMean': 7.628548216350093, 'ErrorStdDev': 69.97843215068035, 'R2': 0.9319325977712878, 'Pearson': 0.9663700630732057} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731265} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731265} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0699, 'RMSE': 71.08956064805535, 'MAE': 56.25353847722968, 'SMAPE': 0.0679, 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+INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.975820255065713} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.975820255065713} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 10.817551612854004 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_female' Length=59 Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_NSW_female' Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 @@ -141,6 +127,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=43.25423728813559 L1_Forecast=43.25423728813559 L1_Test=43.25423728813559 INFO:pyaf.std:MODEL_L2 L2_Fit=57.10145594629301 L2_Forecast=57.10145594629301 L2_Test=57.10145594629301 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 738 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _NSW_female_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -157,6 +152,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=54.45762711864407 L1_Forecast=54.45762711864407 L1_Test=54.45762711864407 INFO:pyaf.std:MODEL_L2 L2_Fit=71.16643181268225 L2_Forecast=71.16643181268225 L2_Test=71.16643181268225 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1002 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _NSW_male_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -173,6 +177,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=35.932203389830505 L1_Forecast=35.932203389830505 L1_Test=35.932203389830505 INFO:pyaf.std:MODEL_L2 L2_Fit=44.6959597006186 L2_Forecast=44.6959597006186 L2_Test=44.6959597006186 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 486 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _VIC_female_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -189,6 +202,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=43.610169491525426 L1_Forecast=43.610169491525426 L1_Test=43.610169491525426 INFO:pyaf.std:MODEL_L2 L2_Fit=53.68662996769434 L2_Forecast=53.68662996769434 L2_Test=53.68662996769434 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 662 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _VIC_male_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -205,6 +227,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=67.86440677966101 L1_Forecast=67.86440677966101 L1_Test=67.86440677966101 INFO:pyaf.std:MODEL_L2 L2_Fit=82.58390699715808 L2_Forecast=82.58390699715808 L2_Test=82.58390699715808 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1224 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES __female_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -221,6 +252,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=77.49152542372882 L1_Forecast=77.49152542372882 L1_Test=77.49152542372882 INFO:pyaf.std:MODEL_L2 L2_Fit=97.29737746217495 L2_Forecast=97.29737746217495 L2_Test=97.29737746217495 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1664 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES __male_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -237,22 +277,24 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=132.03389830508473 L1_Forecast=132.03389830508473 L1_Test=132.03389830508473 INFO:pyaf.std:MODEL_L2 L2_Fit=168.23812824865286 L2_Forecast=168.23812824865286 L2_Test=168.23812824865286 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 2888 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES ___Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.3933887481689453 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.4311645030975342 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.4813876152038574 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.39844560623168945 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.4415781497955322 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5108492374420166 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5069351196289062 -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 1.4561889171600342 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 1.3469524383544922 +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 1.4635648727416992 diff --git a/tests/references/bugs_issue_56_issue_56_order2.log b/tests/references/bugs_issue_56_issue_56_order2.log index e47a05b2f..5966035c3 100644 --- a/tests/references/bugs_issue_56_issue_56_order2.log +++ b/tests/references/bugs_issue_56_issue_56_order2.log @@ -1,46 +1,35 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 3.2098910808563232 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 3.381171226501465 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 3.413740634918213 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 3.432687759399414 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 3.465195417404175 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 3.514465093612671 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 3.6388423442840576 -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.6127288341522217 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.45219898223876953 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5830032825469971 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.40995335578918457 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.4679081439971924 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.37804269790649414 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5165717601776123 -INFO:pyaf.std:STRUCTURE [0, 1, 2] -INFO:pyaf.std:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female_Forecast', 'NSW_male', - 'NSW_male_Forecast', 'VIC_female', 'VIC_female_Forecast', 'VIC_male', - 'VIC_male_Forecast', '_female', '_female_Forecast', '_male', - '_male_Forecast', '_', '__Forecast', 'NSW_female_BU_Forecast', - 'NSW_male_BU_Forecast', 'VIC_female_BU_Forecast', - 'VIC_male_BU_Forecast', '_female_BU_Forecast', '_male_BU_Forecast', - '__BU_Forecast', '__AHP_TD_Forecast', '_female_AHP_TD_Forecast', - '_male_AHP_TD_Forecast', 'NSW_female_AHP_TD_Forecast', - 'VIC_female_AHP_TD_Forecast', 'NSW_male_AHP_TD_Forecast', - 'VIC_male_AHP_TD_Forecast', '__PHA_TD_Forecast', - '_female_PHA_TD_Forecast', '_male_PHA_TD_Forecast', +INFO:pyaf.std:START_TRAINING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 8.967294216156006 +INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 1.9829635620117188 +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD +INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female_Forecast', + 'NSW_female_Forecast_Lower_Bound', 'NSW_female_Forecast_Upper_Bound', + 'NSW_male', 'NSW_male_Forecast', 'NSW_male_Forecast_Lower_Bound', + 'NSW_male_Forecast_Upper_Bound', 'VIC_female', 'VIC_female_Forecast', + 'VIC_female_Forecast_Lower_Bound', 'VIC_female_Forecast_Upper_Bound', + 'VIC_male', 'VIC_male_Forecast', 'VIC_male_Forecast_Lower_Bound', + 'VIC_male_Forecast_Upper_Bound', '_female', '_female_Forecast', + '_female_Forecast_Lower_Bound', '_female_Forecast_Upper_Bound', '_male', + '_male_Forecast', '_male_Forecast_Lower_Bound', + '_male_Forecast_Upper_Bound', '_', '__Forecast', + '__Forecast_Lower_Bound', '__Forecast_Upper_Bound', + 'NSW_female_BU_Forecast', 'NSW_male_BU_Forecast', + 'VIC_female_BU_Forecast', 'VIC_male_BU_Forecast', '_female_BU_Forecast', + '_male_BU_Forecast', '__BU_Forecast', '__AHP_TD_Forecast', + '_female_AHP_TD_Forecast', '_male_AHP_TD_Forecast', + 'NSW_female_AHP_TD_Forecast', 'VIC_female_AHP_TD_Forecast', + 'NSW_male_AHP_TD_Forecast', 'VIC_male_AHP_TD_Forecast', + '__PHA_TD_Forecast', '_female_PHA_TD_Forecast', '_male_PHA_TD_Forecast', 'NSW_female_PHA_TD_Forecast', 'VIC_female_PHA_TD_Forecast', 'NSW_male_PHA_TD_Forecast', 'VIC_male_PHA_TD_Forecast', '_female_MO_Forecast', '_male_MO_Forecast', 'NSW_female_MO_Forecast', @@ -50,83 +39,80 @@ INFO:pyaf.std:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female_Forecast 'VIC_male_OC_Forecast', '_female_OC_Forecast', '_male_OC_Forecast', '__OC_Forecast'], dtype='object') -INFO:pyaf.std:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.std:MODEL_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.std:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.std:MODEL_LEVEL (1, ['_female', '_male']) -INFO:pyaf.std:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.std:MODEL_LEVEL (2, ['_']) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_AHP_TD', 57.10145594629301, 0.0694, 61.88312394961418, 0.0782) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_AHP_TD', 57.10145594629301, 0.0694, 61.88312394961418, 0.0782) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_BU', 57.10145594629301, 0.0694, 57.10145594629301, 0.0694) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_BU', 57.10145594629301, 0.0694, 57.10145594629301, 0.0694) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_MO', 57.10145594629301, 0.0694, 62.32278723251837, 0.0813) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_MO', 57.10145594629301, 0.0694, 62.32278723251837, 0.0813) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_OC', 57.10145594629301, 0.0694, 57.10145594629303, 0.0694) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_OC', 57.10145594629301, 0.0694, 57.10145594629303, 0.0694) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_female_PHA_TD', 57.10145594629301, 0.0694, 62.016771833343164, 0.0799) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_female_PHA_TD', 57.10145594629301, 0.0694, 62.016771833343164, 0.0799) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_AHP_TD', 71.16643181268225, 0.0666, 70.39300898637983, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_AHP_TD', 71.16643181268225, 0.0666, 70.39300898637983, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_BU', 71.16643181268225, 0.0666, 71.16643181268225, 0.0666) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_BU', 71.16643181268225, 0.0666, 71.16643181268225, 0.0666) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_MO', 71.16643181268225, 0.0666, 71.08956064805535, 0.0699) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_MO', 71.16643181268225, 0.0666, 71.08956064805535, 0.0699) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_OC', 71.16643181268225, 0.0666, 71.16643181268222, 0.0666) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_OC', 71.16643181268225, 0.0666, 71.16643181268222, 0.0666) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('NSW_male_PHA_TD', 71.16643181268225, 0.0666, 70.70421689408323, 0.0696) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('NSW_male_PHA_TD', 71.16643181268225, 0.0666, 70.70421689408323, 0.0696) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_AHP_TD', 44.6959597006186, 0.0908, 42.920214691597465, 0.086) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_AHP_TD', 44.6959597006186, 0.0908, 42.920214691597465, 0.086) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_BU', 44.6959597006186, 0.0908, 44.6959597006186, 0.0908) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_BU', 44.6959597006186, 0.0908, 44.6959597006186, 0.0908) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_MO', 44.6959597006186, 0.0908, 43.077644041671846, 0.0877) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_MO', 44.6959597006186, 0.0908, 43.077644041671846, 0.0877) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_OC', 44.6959597006186, 0.0908, 44.69595970061861, 0.0908) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_OC', 44.6959597006186, 0.0908, 44.69595970061861, 0.0908) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_female_PHA_TD', 44.6959597006186, 0.0908, 42.28932734562721, 0.0844) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_female_PHA_TD', 44.6959597006186, 0.0908, 42.28932734562721, 0.0844) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_AHP_TD', 53.68662996769434, 0.0852, 49.09404571362918, 0.0755) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_AHP_TD', 53.68662996769434, 0.0852, 49.09404571362918, 0.0755) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_BU', 53.68662996769434, 0.0852, 53.68662996769434, 0.0852) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_BU', 53.68662996769434, 0.0852, 53.68662996769434, 0.0852) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_MO', 53.68662996769434, 0.0852, 50.005918797978644, 0.0767) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_MO', 53.68662996769434, 0.0852, 50.005918797978644, 0.0767) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_OC', 53.68662996769434, 0.0852, 53.68662996769434, 0.0852) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_OC', 53.68662996769434, 0.0852, 53.68662996769434, 0.0852) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('VIC_male_PHA_TD', 53.68662996769434, 0.0852, 47.833484231171475, 0.074) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('VIC_male_PHA_TD', 53.68662996769434, 0.0852, 47.833484231171475, 0.074) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__AHP_TD', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__AHP_TD', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__BU', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__BU', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__MO', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__MO', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__OC', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__OC', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('__PHA_TD', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('__PHA_TD', 168.23812824865286, 0.0581, 168.23812824865286, 0.0581) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_AHP_TD', 82.58390699715808, 0.0683, 80.93013515525995, 0.065) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_AHP_TD', 82.58390699715808, 0.0683, 80.93013515525995, 0.065) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_BU', 82.58390699715808, 0.0683, 82.58390699715808, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_BU', 82.58390699715808, 0.0683, 82.58390699715808, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_MO', 82.58390699715808, 0.0683, 82.58390699715808, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_MO', 82.58390699715808, 0.0683, 82.58390699715808, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_OC', 82.58390699715808, 0.0683, 82.58390699715814, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_OC', 82.58390699715808, 0.0683, 82.58390699715814, 0.0683) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_female_PHA_TD', 82.58390699715808, 0.0683, 81.16027794640222, 0.0655) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_female_PHA_TD', 82.58390699715808, 0.0683, 81.16027794640222, 0.0655) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_AHP_TD', 97.29737746217495, 0.0588, 94.32738181170546, 0.0578) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_AHP_TD', 97.29737746217495, 0.0588, 94.32738181170546, 0.0578) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_BU', 97.29737746217495, 0.0588, 97.29737746217495, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_BU', 97.29737746217495, 0.0588, 97.29737746217495, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_MO', 97.29737746217495, 0.0588, 97.29737746217495, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_MO', 97.29737746217495, 0.0588, 97.29737746217495, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_OC', 97.29737746217495, 0.0588, 97.29737746217488, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_OC', 97.29737746217495, 0.0588, 97.29737746217488, 0.0588) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_FIT_PERF ('_male_PHA_TD', 97.29737746217495, 0.0588, 93.96986777755437, 0.0576) -INFO:pyaf.std:REPORT_COMBINED_FORECASTS_VALID_PERF ('_male_PHA_TD', 97.29737746217495, 0.0588, 93.96986777755437, 0.0576) -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 5.983930826187134 +INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) +INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) +INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_AHP_TD_Forecast', 'Length': 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57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0813, 'RMSE': 62.32278723251837, 'MAE': 48.70371692503398, 'SMAPE': 0.079, 'ErrorMean': 7.869221952578178, 'ErrorStdDev': 61.82398526697237, 'R2': 0.9105114608134931, 'Pearson': 0.9559136222127095} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0813, 'RMSE': 62.32278723251837, 'MAE': 48.70371692503398, 'SMAPE': 0.079, 'ErrorMean': 7.869221952578178, 'ErrorStdDev': 61.82398526697237, 'R2': 0.9105114608134931, 'Pearson': 0.9559136222127095} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629308, 'MAE': 43.25423728813573, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389831088, 'ErrorStdDev': 56.547824189519446, 'R2': 0.9248778434795352, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629308, 'MAE': 43.25423728813573, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389831088, 'ErrorStdDev': 56.547824189519446, 'R2': 0.9248778434795352, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0799, 'RMSE': 62.016771833343164, 'MAE': 47.68929397311254, 'SMAPE': 0.0775, 'ErrorMean': 8.015543376062395, 'ErrorStdDev': 61.49659383262953, 'R2': 0.9113881109148605, 'Pearson': 0.957496276648925} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0799, 'RMSE': 62.016771833343164, 'MAE': 47.68929397311254, 'SMAPE': 0.0775, 'ErrorMean': 8.015543376062395, 'ErrorStdDev': 61.49659383262953, 'R2': 0.9113881109148605, 'Pearson': 0.957496276648925} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 70.39300898637983, 'MAE': 55.09053975912032, 'SMAPE': 0.0667, 'ErrorMean': 7.628548216350093, 'ErrorStdDev': 69.97843215068035, 'R2': 0.9319325977712878, 'Pearson': 0.9663700630732057} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 70.39300898637983, 'MAE': 55.09053975912032, 'SMAPE': 0.0667, 'ErrorMean': 7.628548216350093, 'ErrorStdDev': 69.97843215068035, 'R2': 0.9319325977712878, 'Pearson': 0.9663700630732057} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731265} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731265} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0699, 'RMSE': 71.08956064805535, 'MAE': 56.25353847722968, 'SMAPE': 0.0679, 'ErrorMean': 11.006816792816519, 'ErrorStdDev': 70.23229753626826, 'R2': 0.9305788542591356, 'Pearson': 0.9663934541393859} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0699, 'RMSE': 71.08956064805535, 'MAE': 56.25353847722968, 'SMAPE': 0.0679, 'ErrorMean': 11.006816792816519, 'ErrorStdDev': 70.23229753626826, 'R2': 0.9305788542591356, 'Pearson': 0.9663934541393859} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268229, 'MAE': 54.45762711864411, 'SMAPE': 0.0653, 'ErrorMean': 11.06779661016978, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667542, 'Pearson': 0.9654607890731266} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268229, 'MAE': 54.45762711864411, 'SMAPE': 0.0653, 'ErrorMean': 11.06779661016978, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667542, 'Pearson': 0.9654607890731266} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0696, 'RMSE': 70.70421689408323, 'MAE': 55.925572084650426, 'SMAPE': 0.0677, 'ErrorMean': 10.858812414263125, 'ErrorStdDev': 69.86538828030237, 'R2': 0.931329414600923, 'Pearson': 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77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.975820255065713} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.975820255065713} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 11.806824684143066 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_female' Length=59 Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_NSW_female' Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 @@ -141,6 +127,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=43.25423728813559 L1_Forecast=43.25423728813559 L1_Test=43.25423728813559 INFO:pyaf.std:MODEL_L2 L2_Fit=57.10145594629301 L2_Forecast=57.10145594629301 L2_Test=57.10145594629301 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 738 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _NSW_female_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -157,6 +152,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=54.45762711864407 L1_Forecast=54.45762711864407 L1_Test=54.45762711864407 INFO:pyaf.std:MODEL_L2 L2_Fit=71.16643181268225 L2_Forecast=71.16643181268225 L2_Test=71.16643181268225 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1002 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _NSW_male_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -173,6 +177,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=35.932203389830505 L1_Forecast=35.932203389830505 L1_Test=35.932203389830505 INFO:pyaf.std:MODEL_L2 L2_Fit=44.6959597006186 L2_Forecast=44.6959597006186 L2_Test=44.6959597006186 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 486 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _VIC_female_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -189,6 +202,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=43.610169491525426 L1_Forecast=43.610169491525426 L1_Test=43.610169491525426 INFO:pyaf.std:MODEL_L2 L2_Fit=53.68662996769434 L2_Forecast=53.68662996769434 L2_Test=53.68662996769434 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 662 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _VIC_male_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -205,6 +227,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=67.86440677966101 L1_Forecast=67.86440677966101 L1_Test=67.86440677966101 INFO:pyaf.std:MODEL_L2 L2_Fit=82.58390699715808 L2_Forecast=82.58390699715808 L2_Test=82.58390699715808 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1224 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES __female_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -221,6 +252,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=77.49152542372882 L1_Forecast=77.49152542372882 L1_Test=77.49152542372882 INFO:pyaf.std:MODEL_L2 L2_Fit=97.29737746217495 L2_Forecast=97.29737746217495 L2_Test=97.29737746217495 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1664 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES __male_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 @@ -237,22 +277,24 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9831 MASE_Forecast=0.9831 MASE_Test=0.9831 INFO:pyaf.std:MODEL_L1 L1_Fit=132.03389830508473 L1_Forecast=132.03389830508473 L1_Test=132.03389830508473 INFO:pyaf.std:MODEL_L2 L2_Fit=168.23812824865286 L2_Forecast=168.23812824865286 L2_Test=168.23812824865286 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 2888 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES ___Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.4690370559692383 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.42551183700561523 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.42539453506469727 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.40848565101623535 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.39978861808776855 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.38193392753601074 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.40616393089294434 -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 1.5433399677276611 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 1.6137306690216064 +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 1.6616365909576416 diff --git a/tests/references/bugs_issue_58_issue_58_1_categorical_exogenous.log b/tests/references/bugs_issue_58_issue_58_1_categorical_exogenous.log index 90138678d..3828a6cf7 100644 --- a/tests/references/bugs_issue_58_issue_58_1_categorical_exogenous.log +++ b/tests/references/bugs_issue_58_issue_58_1_categorical_exogenous.log @@ -8,133 +8,134 @@ INFO:pyaf.std:START_TRAINING 'Ozone2' Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64') Index(['AQ', 'AR', 'AS', 'AT', 'AU', 'AV', 'AW', 'AX', 'AY', 'AZ', 'A[', 'A\'], dtype='object') Index(['P_Q', 'P_R', 'P_S', 'P_T', 'P_U', 'P_V', 'P_W', 'P_X'], dtype='object') -INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS Diff_Ozone2 0.049643516540527344 -INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS _Ozone2 0.08797597885131836 -INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS RelDiff_Ozone2 0.1203458309173584 -INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS CumSum_Ozone2 0.10645174980163574 -INFO:pyaf.std:TREND_TIME_IN_SECONDS Diff_Ozone2 0.10193562507629395 -INFO:pyaf.std:TREND_TIME_IN_SECONDS _Ozone2 0.08894896507263184 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_ConstantTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_ConstantTrend_residue_zeroCycle' 0.02 +INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS Diff_Ozone2 0.05217576026916504 +INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS _Ozone2 0.08349108695983887 +INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS RelDiff_Ozone2 0.06050848960876465 +INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS CumSum_Ozone2 0.07452082633972168 +INFO:pyaf.std:TREND_TIME_IN_SECONDS Diff_Ozone2 0.2486095428466797 +INFO:pyaf.std:TREND_TIME_IN_SECONDS _Ozone2 0.2637360095977783 +INFO:pyaf.std:TREND_TIME_IN_SECONDS RelDiff_Ozone2 0.2291109561920166 +INFO:pyaf.std:TREND_TIME_IN_SECONDS CumSum_Ozone2 0.23345732688903809 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_ConstantTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_Lag1Trend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_LinearTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_PolyTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS Diff_Ozone2 0.07030892372131348 +INFO:pyaf.std:AR_MODEL_ADD_EXOGENOUS '204 15 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_ConstantTrend_residue_zeroCycle' 0.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle' 0.02 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_Lag1Trend_residue_zeroCycle' 0.02 -INFO:pyaf.std:TREND_TIME_IN_SECONDS RelDiff_Ozone2 0.09966468811035156 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_Lag1Trend_residue_zeroCycle' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle' 0.02 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_LinearTrend_residue_zeroCycle' 0.01 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_LinearTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_LinearTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_PolyTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS _Ozone2 0.10039401054382324 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_PolyTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS Diff_Ozone2 0.13432097434997559 -INFO:pyaf.std:TREND_TIME_IN_SECONDS CumSum_Ozone2 0.14670300483703613 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_PolyTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS CumSum_Ozone2 0.1327512264251709 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_PolyTrend_residue_zeroCycle' 0.02 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS _Ozone2 0.16069507598876953 INFO:pyaf.std:AR_MODEL_ADD_EXOGENOUS '204 15 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS RelDiff_Ozone2 0.07630372047424316 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle' 0.01 INFO:pyaf.std:AR_MODEL_ADD_EXOGENOUS '204 15 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle' 0.04 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS RelDiff_Ozone2 0.22513055801391602 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 INFO:pyaf.std:AR_MODEL_ADD_EXOGENOUS '204 15 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle' 0.03 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_LinearTrend_residue_zeroCycle' 0.01 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_PolyTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS CumSum_Ozone2 0.12268424034118652 -INFO:pyaf.std:AR_MODEL_ADD_EXOGENOUS '204 15 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 0.8849411010742188 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 0.908930778503418 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1588110657.7841418 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 0.9919707775115967 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1588110657.7936733 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 1.0847415924072266 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1588110657.8146932 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1596039991.0538392 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.07 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.05 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 1.259061336517334 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 1.2145593166351318 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1588110658.0213325 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822 0.7890381813049316 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1588110658.959283 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 1.0883710384368896 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596039992.0358164 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 0.7922561168670654 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1588110658.9608834 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 1.1357977390289307 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1588110658.9628303 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596039992.1320422 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.05 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.05 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.05 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 1.4318256378173828 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596039992.5760245 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 0.7905464172363281 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596039992.85888 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 1.3544697761535645 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596039993.1983852 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 1.0076892375946045 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 1.0662147998809814 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596039993.2534826 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 1.121978998184204 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1588110659.7443159 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596039993.735373 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 0.8519961833953857 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1588110659.8693712 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 0.9430062770843506 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 1.107661485671997 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596039993.9981515 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS Diff_Ozone2 4.073060989379883 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 0.8100147247314453 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596039994.0964684 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS _Ozone2 4.057394981384277 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 1.3100762367248535 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1588110659.975998 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596039994.5403607 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.04 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 1.3835437297821045 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1588110660.3761168 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 0.9370822906494141 +INFO:pyaf.std:PERF_TIME_IN_SECONDS Diff_Ozone2 4 0.6661605834960938 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS Diff_Ozone2 5.189763784408569 +INFO:pyaf.std:PERF_TIME_IN_SECONDS _Ozone2 4 0.7034308910369873 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS _Ozone2 5.326339960098267 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 1.194091796875 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1588110660.7146013 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.05 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS CumSum_Ozone2 3.955875873565674 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 1.1324520111083984 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1588110661.0365102 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS Diff_Ozone2 4.378824710845947 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 1.067380428314209 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596039994.9638138 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS CumSum_Ozone2 4.935167074203491 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 0.9147300720214844 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1588110661.0782757 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.05 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS RelDiff_Ozone2 4.403326034545898 -INFO:pyaf.std:PERF_TIME_IN_SECONDS CumSum_Ozone2 4 0.839993953704834 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS CumSum_Ozone2 5.272615671157837 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 1.2471551895141602 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1588110661.6540573 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS _Ozone2 5.0020506381988525 -INFO:pyaf.std:PERF_TIME_IN_SECONDS Diff_Ozone2 4 1.0717408657073975 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS Diff_Ozone2 5.817035675048828 -INFO:pyaf.std:PERF_TIME_IN_SECONDS _Ozone2 4 0.7898235321044922 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS _Ozone2 6.153170108795166 -INFO:pyaf.std:PERF_TIME_IN_SECONDS RelDiff_Ozone2 4 1.3601117134094238 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS RelDiff_Ozone2 6.158268928527832 -INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS _Ozone2 0.011799097061157227 -INFO:pyaf.std:PREDICTION_INTERVAL_TIME_IN_SECONDS Ozone2 1.3995459079742432 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone2' 7.861649513244629 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596039995.5036402 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS RelDiff_Ozone2 5.391361951828003 +INFO:pyaf.std:PERF_TIME_IN_SECONDS CumSum_Ozone2 4 0.7235720157623291 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS CumSum_Ozone2 6.175582647323608 +INFO:pyaf.std:PERF_TIME_IN_SECONDS RelDiff_Ozone2 4 0.794609785079956 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS RelDiff_Ozone2 6.8018529415130615 +INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone2 0.014703035354614258 +INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone2 0.017276287078857422 +INFO:pyaf.std:PREDICTION_INTERVAL_TIME_IN_SECONDS Ozone2 1.0943667888641357 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone2']' 8.48215103149414 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone2' Length=204 Min=0.0 Max=26.099999999999998 Mean=5.529411764705882 StdDev=3.838506864406639 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone2' Min=0.0 Max=26.099999999999998 Mean=5.529411764705882 StdDev=3.838506864406639 @@ -147,35 +148,44 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=2109152020.0645 MAPE_Forecast=0.3014 MAPE_Test=0.3076 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3346 SMAPE_Forecast=0.2841 SMAPE_Test=0.2663 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5056 MASE_Forecast=0.5276 MASE_Test=0.6554 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.6476645845311328 L1_Forecast=1.4994398611500221 L1_Test=1.167784932189634 -INFO:pyaf.std:MODEL_L2 L2_Fit=2.451342315346559 L2_Forecast=2.0577037763863926 L2_Test=1.308046379562417 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.64766458453113 L1_Forecast=1.4994398611500228 L1_Test=1.1677849321896348 +INFO:pyaf.std:MODEL_L2 L2_Fit=2.4513423153465532 L2_Forecast=2.0577037763863935 L2_Test=1.3080463795624186 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.700495969156669, array([-0.27815778])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone2_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Exog2=4_Lag5 1.669198966101168 -INFO:pyaf.std:AR_MODEL_COEFF 2 Exog3=AU_Lag4 1.669198966101164 -INFO:pyaf.std:AR_MODEL_COEFF 3 Exog2=5_Lag4 1.6691989661011624 -INFO:pyaf.std:AR_MODEL_COEFF 4 Exog3=AS_Lag6 1.669198966101162 -INFO:pyaf.std:AR_MODEL_COEFF 5 Exog2=2_Lag7 1.6691989661011573 -INFO:pyaf.std:AR_MODEL_COEFF 6 Exog2=3_Lag6 1.669198966101153 -INFO:pyaf.std:AR_MODEL_COEFF 7 Exog3=AR_Lag7 1.6691989661011526 -INFO:pyaf.std:AR_MODEL_COEFF 8 Exog3=AT_Lag5 1.6691989661011482 -INFO:pyaf.std:AR_MODEL_COEFF 9 Exog3=AR_Lag31 -0.838355095925206 -INFO:pyaf.std:AR_MODEL_COEFF 10 Exog3=AT_Lag29 -0.8383550959251977 +INFO:pyaf.std:AR_MODEL_COEFF 1 Exog2=3_Lag6 1.6691989661011786 +INFO:pyaf.std:AR_MODEL_COEFF 2 Exog2=4_Lag5 1.6691989661011652 +INFO:pyaf.std:AR_MODEL_COEFF 3 Exog2=2_Lag7 1.6691989661011597 +INFO:pyaf.std:AR_MODEL_COEFF 4 Exog3=AR_Lag7 1.6691989661011553 +INFO:pyaf.std:AR_MODEL_COEFF 5 Exog3=AS_Lag6 1.6691989661011526 +INFO:pyaf.std:AR_MODEL_COEFF 6 Exog3=AT_Lag5 1.669198966101151 +INFO:pyaf.std:AR_MODEL_COEFF 7 Exog3=AU_Lag4 1.6691989661011466 +INFO:pyaf.std:AR_MODEL_COEFF 8 Exog2=5_Lag4 1.6691989661011444 +INFO:pyaf.std:AR_MODEL_COEFF 9 Exog2=5_Lag28 -0.8383550959251904 +INFO:pyaf.std:AR_MODEL_COEFF 10 Exog2=3_Lag30 -0.838355095925184 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 19.202141046524048 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.1526856422424316 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 17.799492120742798 +INFO:pyaf.std:START_FORECASTING '['Ozone2']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone2']' 0.6904833316802979 Forecast Columns Index(['Time', 'Ozone2', 'row_number', 'Time_Normalized', '_Ozone2', '_Ozone2_LinearTrend', '_Ozone2_LinearTrend_residue', '_Ozone2_LinearTrend_residue_zeroCycle', @@ -204,46 +214,48 @@ memory usage: 5.2 KB None Forecasts [[Timestamp('1972-01-01 00:00:00') nan 3.79791112998043] - [Timestamp('1972-02-01 00:00:00') nan 3.3115498555176224] - [Timestamp('1972-03-01 00:00:00') nan 3.445890790432581] + [Timestamp('1972-02-01 00:00:00') nan 3.311549855517625] + [Timestamp('1972-03-01 00:00:00') nan 3.445890790432582] [Timestamp('1972-04-01 00:00:00') nan 3.7802236485042746] - [Timestamp('1972-05-01 00:00:00') nan 5.099824743306214] - [Timestamp('1972-06-01 00:00:00') nan 5.393125188036234] - [Timestamp('1972-07-01 00:00:00') nan 5.721529861809877] - [Timestamp('1972-08-01 00:00:00') nan 5.985167766318565] - [Timestamp('1972-09-01 00:00:00') nan 12.445090140884398] - [Timestamp('1972-10-01 00:00:00') nan 4.221582996482067] + [Timestamp('1972-05-01 00:00:00') nan 5.099824743306211] + [Timestamp('1972-06-01 00:00:00') nan 5.393125188036231] + [Timestamp('1972-07-01 00:00:00') nan 5.7215298618098736] + [Timestamp('1972-08-01 00:00:00') nan 5.985167766318559] + [Timestamp('1972-09-01 00:00:00') nan 12.44509014088441] + [Timestamp('1972-10-01 00:00:00') nan 4.221582996482066] [Timestamp('1972-11-01 00:00:00') nan 3.8469490889239393] - [Timestamp('1972-12-01 00:00:00') nan 3.4695045447746207]] + [Timestamp('1972-12-01 00:00:00') nan 3.4695045447746224]] { - "Dataset": { - "Signal": "Ozone2", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone2": { + "Dataset": { + "Signal": "Ozone2", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "ARX", + "Best_Decomposition": "_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "ARX", - "Best_Decomposition": "_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "1.4994398611500221", - "MAPE": "0.3014", - "MASE": "0.5276", - "RMSE": "2.0577037763863926" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "1.4994398611500228", + "MAPE": "0.3014", + "MASE": "0.5276", + "RMSE": "2.0577037763863935" + } } } diff --git a/tests/references/bugs_issue_69_issue_69_example_1_no_rectifier.log b/tests/references/bugs_issue_69_issue_69_example_1_no_rectifier.log index a8bdc8aa1..a94ec1f42 100644 --- a/tests/references/bugs_issue_69_issue_69_example_1_no_rectifier.log +++ b/tests/references/bugs_issue_69_issue_69_example_1_no_rectifier.log @@ -1,31 +1,40 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 5.377027273178101 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 5.524866342544556 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-01-25T00:00:00.000000 TimeMax=2023-12-07T00:00:00.000000 TimeDelta= Horizon=7 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3600 Min=1.0683809059828678e-09 Max=1.094546884664861 Mean=0.3287516228189823 StdDev=0.3863227759312388 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0683809059828678e-09 Max=1.094546884664861 Mean=0.3287516228189823 StdDev=0.3863227759312388 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3600 Min=3.885288840385726e-10 Max=1.0918868391620093 Mean=0.3281951516502585 StdDev=0.38607000992505003 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=3.885288840385726e-10 Max=1.0918868391620093 Mean=0.3281951516502585 StdDev=0.38607000992505003 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [PolyTrend + Seasonal_DayOfWeek + AR] -INFO:pyaf.std:TREND_DETAIL '_Signal_PolyTrend' [PolyTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_PolyTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=577.4451 MAPE_Forecast=4832.494 MAPE_Test=72.8329 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.641 SMAPE_Forecast=0.6574 SMAPE_Test=1.3406 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.763 MASE_Forecast=0.764 MASE_Test=1.8914 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.01512956129079079 L1_Forecast=0.014996045135015018 L1_Test=0.009651586182278378 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.0208982681435578 L2_Forecast=0.020889653680289957 L2_Test=0.010736328783972265 -INFO:pyaf.std:MODEL_COMPLEXITY 84 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64)' [LinearTrend + Cycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_LinearTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=3259.8651 MAPE_Forecast=3622.1696 MAPE_Test=148.8339 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.7023 SMAPE_Forecast=0.7378 SMAPE_Test=1.1592 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8476 MASE_Forecast=0.8452 MASE_Test=1.0895 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.016231439572887685 L1_Forecast=0.01703393874803506 L1_Test=0.016097060855300226 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.022234108071893775 L2_Forecast=0.023915352956773623 L2_Test=0.01878751114829279 +INFO:pyaf.std:MODEL_COMPLEXITY 88 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.35651805467312264, array([-0.05545494])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_LinearTrend_residue_bestCycle_byMAPE 60 -0.2766741950165408 {0: -0.308103941392317, 1: -0.2846793071659676, 2: -0.2498585625760706, 3: -0.221821417236655, 4: -0.1798802614904713, 5: -0.14998063391468808, 6: -0.11198444028575934, 7: -0.08268678456339748, 8: -0.04438134649224815, 9: -0.006799770208358841, 10: 0.021040779397637377, 11: 0.052063532185492006, 12: 0.08119196266931236, 13: 0.11248777430214593, 14: 0.14349300334092063, 15: 0.15965865120853046, 16: 0.14917343819434928, 17: 0.1252313434176934, 18: 0.08527107978603748, 19: 0.048985742794448645, 20: 0.02075402601495624, 21: -0.01090542575277545, 22: -0.04765278930547012, 23: -0.06793269623945025, 24: -0.1039850083424585, 25: -0.1384359865901169, 26: -0.18010506610011576, 27: -0.2202008120131766, 28: -0.24568734777566253, 29: -0.2807443334839318, 30: -0.3061487408842068, 31: -0.3027071268530487, 32: -0.30629943191276143, 33: -0.3075862321211942, 34: -0.3074840621249422, 35: -0.30951027486908145, 36: -0.304872100627654, 37: -0.30761226196987207, 38: -0.30745394988487407, 39: -0.3052295523334428, 40: -0.3043657872526405, 41: -0.30867403049280157, 42: -0.30781885022364686, 43: -0.30838281015861035, 44: -0.3001947653687071, 45: -0.30748542181572114, 46: -0.30708145829976385, 47: -0.2996116451811961, 48: -0.30380112356353195, 49: -0.3046701965979487, 50: -0.2999460102538381, 51: -0.30664719124835116, 52: -0.29969339873038486, 53: -0.3092083025115245, 54: -0.30316914000655715, 55: -0.3083971743389113, 56: -0.30503129492756165, 57: -0.30216017854001537, 58: -0.3058223769326453, 59: -0.30574312010094906} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag1 0.5058170662182284 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag2 0.343725847481561 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag3 0.20622483595924931 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag4 0.12138648407239808 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag8 -0.04616053919568374 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag5 0.04462850578528603 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag10 -0.03962787054508215 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag9 -0.036554182126489095 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag12 -0.030898715982875462 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag11 -0.028436695162444896 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag1 0.6382294145429854 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag2 0.3649233834911286 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag3 0.15949129745313198 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag48 0.06927957749232741 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag18 0.0631944487089572 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag11 -0.05431365236302221 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag17 0.05186541190890171 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag62 -0.051100232965025194 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag50 0.04953348963639562 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag4 0.04936296381218387 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.7868764400482178 -MIN__FORECAST -0.049524822684502035 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.8145904541015625 +MIN__FORECAST -0.04798309920545002 diff --git a/tests/references/bugs_issue_69_issue_69_example_1_with_rectifier.log b/tests/references/bugs_issue_69_issue_69_example_1_with_rectifier.log index 01c20f9b9..b0be7402b 100644 --- a/tests/references/bugs_issue_69_issue_69_example_1_with_rectifier.log +++ b/tests/references/bugs_issue_69_issue_69_example_1_with_rectifier.log @@ -1,31 +1,40 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 5.569751501083374 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 4.540101766586304 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-01-25T00:00:00.000000 TimeMax=2023-12-07T00:00:00.000000 TimeDelta= Horizon=7 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3600 Min=5.774089022643961e-10 Max=1.0717711376392982 Mean=0.32822169593807543 StdDev=0.38645195452730424 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=5.774089022643961e-10 Max=1.0717711376392982 Mean=0.32822169593807543 StdDev=0.38645195452730424 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3600 Min=3.901343491789633e-09 Max=1.0676809650055774 Mean=0.3281879903068972 StdDev=0.38567009199434776 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=3.901343491789633e-09 Max=1.0676809650055774 Mean=0.3281879903068972 StdDev=0.38567009199434776 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_PolyTrend_residue_zeroCycle_residue_AR(64)' [PolyTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Signal_PolyTrend' [PolyTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_PolyTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_PolyTrend_residue_zeroCycle_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=26297.5658 MAPE_Forecast=4046.7011 MAPE_Test=687.0741 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.648 SMAPE_Forecast=0.6906 SMAPE_Test=1.3566 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7567 MASE_Forecast=0.7595 MASE_Test=0.8874 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.014476226175209902 L1_Forecast=0.014939889078080627 L1_Test=0.00781726644022261 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.019758449201429006 L2_Forecast=0.02131789294111614 L2_Test=0.00927965461757013 -INFO:pyaf.std:MODEL_COMPLEXITY 80 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64)' [LinearTrend + Seasonal_DayOfNthWeekOfMonth + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth' [Seasonal_DayOfNthWeekOfMonth] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=732.9726 MAPE_Forecast=365.9562 MAPE_Test=257020.6971 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.7615 SMAPE_Forecast=0.7909 SMAPE_Test=1.0956 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.2529 MASE_Forecast=1.2271 MASE_Test=8.1961 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02455794808972164 L1_Forecast=0.02476470201531443 L1_Test=0.04009062204526481 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.04563598850434382 L2_Forecast=0.03875078769575722 L2_Test=0.07277711979085466 +INFO:pyaf.std:MODEL_COMPLEXITY 84 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.3564716912591534, array([-0.05540577])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth -0.2729941309354197 {-336: 0.4530470333445391, -335: 0.46127878683517065, -334: 0.4793770219109907, -333: 0.5125190569427105, -332: 0.5183010948742592, -331: 0.5633386470400812, -330: 0.5971083548678295, 7: -0.11361391855673966, 8: -0.12109090617480874, 9: -0.2572470192670618, 10: -0.2574601181304961, 11: -0.29098666032387055, 12: -0.26555026846750496, 13: -0.26637139881720073, 14: -0.28307573111167517, 15: -0.2756008035128612, 16: -0.2669039814573789, 17: -0.2806234901542123, 18: -0.25561171695245943, 19: -0.2757058956517695, 20: -0.28541123229595555, 21: -0.2955666782652839, 22: -0.2683068441866291, 23: -0.24669961908142002, 24: -0.2775489888080205, 25: -0.27628448270700656, 26: -0.27652634279998967, 27: -0.28432270189405434, 28: -0.28487049825380756, 29: -0.28362294046718917, 30: -0.27767469310389326, 31: -0.2776290849925179, 32: -0.283698172078316, 33: -0.2965793995183502, 34: -0.28688829023069523, 35: -0.2827263900264375, 36: -0.2999931457138727, 37: -0.29992825077936525, 38: -0.28598279227241946, 39: -0.2818077012862127, 40: -0.2911658495272194, 41: -0.2958502414620684, 42: -0.3023616808527861, 43: -0.29981261095094114, -350: 0.07214857155200757, -349: 0.12435890486039719, -348: 0.1390640214850782, -347: 0.1703905691718668, -346: 0.23109380510170185, -345: 0.23378382870080933, -344: 0.26576156598949935, -343: 0.29752730044698833, -342: 0.3217242796594636, -341: 0.3399759485694326, -340: 0.3653856722220676, -339: 0.39767394648203047, -338: 0.4195575952532321, -337: 0.4437824377005091, -329: 0.568739961914662, -328: 0.5930901648943032, -327: 0.6094366527546614, -326: 0.6141820603638608, -325: 0.6303079654931122, -324: 0.6401692517011046, -323: 0.647164081223603, -322: 0.0314300031372603, -321: 0.0657677352010817, -357: -0.11023604251964289, -356: -0.07957328702096231, -355: -0.004405678751998876, -354: -0.001398567903182435, -353: 0.020890738964716604, -352: 0.05625614728107753, -351: 0.08573161151761644} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_PolyTrend_residue_zeroCycle_residue_Lag1 0.5185989073198383 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_PolyTrend_residue_zeroCycle_residue_Lag2 0.34808833988910504 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_PolyTrend_residue_zeroCycle_residue_Lag3 0.21036903018497966 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_PolyTrend_residue_zeroCycle_residue_Lag4 0.11765468866202763 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_PolyTrend_residue_zeroCycle_residue_Lag9 -0.051333251040822495 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_PolyTrend_residue_zeroCycle_residue_Lag8 -0.036805869378418565 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_PolyTrend_residue_zeroCycle_residue_Lag7 -0.0298913912546354 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_PolyTrend_residue_zeroCycle_residue_Lag10 -0.02661490938947795 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_PolyTrend_residue_zeroCycle_residue_Lag11 -0.026564376586190708 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_PolyTrend_residue_zeroCycle_residue_Lag12 -0.025070994828207606 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag1 0.7827501311793292 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag2 0.2275999807382199 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag30 0.11325147328806356 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag28 -0.06348842059899971 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag34 -0.05036358548548821 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag64 -0.04594941497599092 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag4 0.038056025726822935 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag63 0.036534695253323604 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag57 -0.03330100316242354 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag6 -0.03285679124644539 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.4806389808654785 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.634974479675293 MIN__FORECAST 0.0 diff --git a/tests/references/bugs_issue_70_test_artificial_filter_seasonals_day.log b/tests/references/bugs_issue_70_test_artificial_filter_seasonals_day.log index dad1495ee..43b4976a6 100644 --- a/tests/references/bugs_issue_70_test_artificial_filter_seasonals_day.log +++ b/tests/references/bugs_issue_70_test_artificial_filter_seasonals_day.log @@ -1,132 +1,357 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_AR 68 0.026 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_NoAR 4 0.6409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 68 0.0392 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 4 0.502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 68 0.1096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 4 0.4926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 68 0.0249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 4 0.5098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 68 0.0286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 4 0.508 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 68 0.0626 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 4 0.5089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0122 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.5041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(64) 100 0.0389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 36 0.6783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 100 0.0427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 0.6915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.0265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.6818 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(64) 100 0.0332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 0.6807 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 0.0522 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 36 0.656 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 84 0.0391 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 0.5023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 84 0.1099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 84 0.0251 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 84 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 0.5084 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 84 0.0624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 0.5093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.5045 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 84 0.0397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 84 0.1081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.4903 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 84 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.5075 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 84 0.0282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 0.5055 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 84 0.0642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 0.5065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.5016 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.8875 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 535.1638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 2.7384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.7071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.4128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 0.0277 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 670.4008 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0412 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 4.5365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 534.7636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 1.0024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 3.7097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 4.0527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 12.5066 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 532.6148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 2.3086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 4.9748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 11.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 3.3467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.0435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 1.4566 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.4039 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 0.04 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.6744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 0.1417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 0.5549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 0.1736 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 0.4257 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.0259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.5013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 0.1009 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 0.5194 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.0679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 0.4838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 0.0821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 0.5113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 0.5266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 0.5904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.0331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.3834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 0.1981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 0.4622 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.1564 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 0.5092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.4871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfMonth_AR 68 0.038 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfMonth_NoAR 4 0.643 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_AR 68 0.1114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 4 0.6657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_AR 68 0.0286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_NoAR 4 0.641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfMonth_AR 68 0.0432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfMonth_NoAR 4 0.642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfYear_AR 68 0.0518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfYear_NoAR 4 0.6538 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_AR 72 0.0178 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 0.0174 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_AR 64 0.0259 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 0.6409 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_AR 100 0.0282 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_NoAR 36 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0245 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfMonth_AR 100 0.0356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfMonth_NoAR 36 0.8419 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_AR 100 0.0791 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_NoAR 36 0.867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_AR 100 0.0285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_NoAR 36 0.8427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfMonth_AR 100 0.0422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfMonth_NoAR 36 0.8415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfYear_AR 100 0.0437 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfYear_NoAR 36 0.8449 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0244 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_AR 96 0.0282 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_NoAR 32 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_AR 84 0.026 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_NoAR 20 0.6413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfMonth_AR 84 0.038 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfMonth_NoAR 20 0.6434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfNthWeekOfMonth_AR 84 0.1114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 20 0.666 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_AR 84 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_NoAR 20 0.6415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfMonth_AR 84 0.0432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfMonth_NoAR 20 0.6424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfYear_AR 84 0.0519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfYear_NoAR 20 0.6542 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_AR 88 0.018 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_AR 80 0.026 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_NoAR 16 0.6413 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_AR 84 0.0261 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_NoAR 20 0.639 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.0179 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfMonth_AR 84 0.0381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfMonth_NoAR 20 0.6411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfNthWeekOfMonth_AR 84 0.1115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 20 0.664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_AR 84 0.0286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_NoAR 20 0.6394 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfMonth_AR 84 0.0433 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfMonth_NoAR 20 0.6402 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfYear_AR 84 0.0517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfYear_NoAR 20 0.6518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0176 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_AR 80 0.0261 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_NoAR 16 0.639 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.4115 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 0.4741 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 0.0259 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.1681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.4816 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 521.762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 2.8193 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_AR 96 0.3793 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_NoAR 32 0.4735 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0553 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 1.0151 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 0.0391 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0348 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 653.4679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.1701 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_AR 128 0.0461 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_AR 116 2.1882 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 0.9409 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 0.5122 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 0.5003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_AR 116 2.2458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 521.9988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 0.5099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 3.1398 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_AR 112 2.0122 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_NoAR 48 0.9366 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_AR 116 5.287 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 1.6084 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 1.0189 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 1.1798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_AR 116 5.3731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 520.9331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 1.0152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 3.3441 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_AR 112 4.8601 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_NoAR 48 1.6038 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_AR 100 24707022.0852 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_AR 100 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_NoAR 40 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_AR 96 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_NoAR 32 24707021.1999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_AR 132 24707022.0852 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 24707021.9962 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 24707022.1308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 24707021.9759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_AR 132 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 24707022.0174 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_AR 128 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_AR 116 24707021.8633 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 24707022.0852 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 24707021.8177 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_NoAR 56 24707022.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfMonth_AR 116 24707021.3408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 24707021.4018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_AR 116 24707021.3608 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfMonth_AR 116 24707022.5738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfMonth_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfYear_AR 116 24707021.2657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfYear_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_None_AR 120 24707021.3605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_None_NoAR 56 24707021.3605 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_AR 112 24707021.3605 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_NoAR 48 24707021.3605 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_AR 116 24707022.0852 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 24707022.0852 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 24707022.1308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_NoAR 56 24707022.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfMonth_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfMonth_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfYear_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfYear_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_None_AR 120 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_None_NoAR 56 24707021.9062 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_AR 112 24707021.9062 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_NoAR 48 24707021.9062 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.0451 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 1.7111 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0451 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.7111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.0449 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 1.4221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.9517 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_AR 96 0.0436 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0446 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0256 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 0.815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0246 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_AR 128 0.0439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_AR 116 0.0372 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_NoAR 52 0.6388 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0257 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfMonth_AR 116 0.1654 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfMonth_NoAR 52 0.6574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 0.3482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 0.7313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_AR 116 0.0376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_NoAR 52 0.6397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfMonth_AR 116 0.1795 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfMonth_NoAR 52 0.6746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfYear_AR 116 0.1881 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfYear_NoAR 52 0.6728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0282 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_AR 112 0.034 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_NoAR 48 0.6387 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_AR 116 0.0402 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_NoAR 52 0.6021 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.1106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfMonth_AR 116 0.2083 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfMonth_NoAR 52 0.6528 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 0.726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 0.7107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_AR 116 0.0401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_NoAR 52 0.6126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfMonth_AR 116 0.3883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfMonth_NoAR 52 0.6603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_AR 116 0.3207 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_NoAR 52 0.6614 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.6215 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_AR 112 0.0361 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 0.6016 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 22.035074472427368 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 31.11028027534485 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2008-09-14T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3988 Min=1.0 Max=10.694835872107333 Mean=6.042416327750525 StdDev=2.801426173695981 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.694835872107333 Mean=6.042416327750525 StdDev=2.801426173695981 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.018 MAPE_Forecast=0.0174 MAPE_Test=0.0121 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.018 SMAPE_Forecast=0.0173 SMAPE_Test=0.0121 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.018 MAPE_Forecast=0.0174 MAPE_Test=0.0122 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.018 SMAPE_Forecast=0.0174 SMAPE_Test=0.0122 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0252 MASE_Forecast=0.0248 MASE_Test=0.0263 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07989849853732847 L1_Forecast=0.07860989368042928 L1_Test=0.06414746537239628 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09982593054682697 L2_Forecast=0.09804344569647 L2_Test=0.08047657715069793 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07974029314534574 L1_Forecast=0.0785321039028717 L1_Test=0.0639597501080064 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10007733848586725 L2_Forecast=0.0981572063280222 L2_Test=0.07985530660970212 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.042336938156116 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 24 0.0030733457294287447 {0: -3.9205093728686653, 1: -2.2918060320399545, 2: -1.4430526395707624, 3: 2.725041627333467, 4: 3.549178383274623, 5: 2.3335367452325135, 6: -3.52690980948847, 7: 4.414688421777947, 8: -2.6743069240623267, 9: -0.19034286059738292, 10: -4.782173619898836, 11: -2.7129595358958056, 12: -2.261812976703247, 13: 4.393168135000041, 14: 0.6471518767607152, 15: 1.8934320834645266, 16: -3.0910050185009927, 17: 0.2357867077965552, 18: -1.8577890525822398, 19: -1.4207121916583185, 20: 3.1450813821518873, 21: 2.719160522848914, 22: 1.4654824832773419, 23: 2.7488170026903918} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END GENERATING_RANDOM_DATASET Signal_4000_D_0_constant_24_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0121 0.0263 -1 None _Signal ... 0.0121 0.0263 -2 None _Signal ... 0.0125 0.0270 -9 None _Signal ... 0.0249 0.0477 -6 None CumSum_Signal ... 0.0192 0.0324 - -[5 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0121 0.0263 -1 None _Signal ... 0.0121 0.0263 -2 None _Signal ... 0.0125 0.0270 -3 None _Signal ... 0.0119 0.0264 -4 None _Signal ... 0.0124 0.0273 +0 None _Signal ... 0.0122 0.0263 +1 None _Signal ... 0.0121 0.0261 +2 None _Signal ... 0.0123 0.0262 +3 None _Signal ... 0.0120 0.0263 +4 None _Signal ... 0.0120 0.0266 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_70_test_artificial_filter_seasonals_hour.log b/tests/references/bugs_issue_70_test_artificial_filter_seasonals_hour.log index e4347d517..13b9988c4 100644 --- a/tests/references/bugs_issue_70_test_artificial_filter_seasonals_hour.log +++ b/tests/references/bugs_issue_70_test_artificial_filter_seasonals_hour.log @@ -1,101 +1,269 @@ INFO:pyaf.std:START_TRAINING 'Signal' +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 68 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 4 0.0122 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0122 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.5041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 100 0.0223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 36 0.0233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 84 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 20 0.0121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.5045 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 84 0.0125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 20 0.0123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.5016 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 0.0272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 2.7384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 2.7384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.7071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.4128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 0.0424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0412 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 1.0024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 3.7097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 1.0024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 3.7097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 4.0527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 2.3086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 4.9748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 2.3086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 4.9748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 11.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 3.3467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 0.5659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.4039 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 0.024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.0227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.0353 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.1132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.4871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.448 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_AR 68 0.0178 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_NoAR 4 0.0174 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_AR 72 0.0178 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 0.0174 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_AR 64 0.0259 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 0.6409 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_AR 100 0.0245 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_NoAR 36 0.0248 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0245 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_AR 100 0.0241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_NoAR 36 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0244 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_AR 96 0.0282 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_NoAR 32 0.8149 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_AR 84 0.018 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_NoAR 20 0.0174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_NoAR 20 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_AR 88 0.018 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_AR 80 0.026 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_NoAR 16 0.6413 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_AR 84 0.0179 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_NoAR 20 0.0177 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.0179 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_AR 84 0.018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_NoAR 20 0.0176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0176 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_AR 80 0.0261 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_NoAR 16 0.639 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_AR 100 0.0259 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_NoAR 36 0.1681 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 0.0259 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.1681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_AR 100 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_NoAR 36 2.8193 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 2.8193 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_AR 96 0.3793 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_NoAR 32 0.4735 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_AR 132 0.0391 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_NoAR 68 0.0287 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 0.0391 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_AR 132 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_NoAR 68 0.1701 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.1701 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_AR 128 0.0461 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_AR 116 0.5122 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_NoAR 52 0.5003 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 0.5122 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 0.5003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_AR 116 0.5099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_NoAR 52 3.1398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 0.5099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 3.1398 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_AR 112 2.0122 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_NoAR 48 0.9366 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_AR 116 1.0189 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_NoAR 52 1.1798 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 1.0189 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 1.1798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_AR 116 1.0152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_NoAR 52 3.3441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 1.0152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 3.3441 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_AR 112 4.8601 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_NoAR 48 1.6038 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_AR 100 24707021.9405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_AR 100 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_NoAR 40 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_AR 96 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_NoAR 32 24707021.1999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_AR 132 24707021.1999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 24707021.1999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 24707022.1308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 24707021.9759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_AR 132 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_NoAR 68 24707022.0174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 24707022.0174 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_AR 128 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_AR 116 24707021.8 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 24707022.1485 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 24707021.8177 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_NoAR 56 24707022.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_AR 116 24707022.6497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_EightHourOfWeek_AR 116 24707022.6416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_EightHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_FourHourOfWeek_AR 116 24707022.6376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_FourHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_HourOfWeek_AR 116 24707022.6232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_HourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_AR 116 24707022.5941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_SixHourOfWeek_AR 116 24707021.3108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_SixHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_ThreeHourOfWeek_AR 116 24707021.3108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_ThreeHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_TwelveHourOfWeek_AR 116 24707022.6376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_TwelveHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_TwoHourOfWeek_AR 116 24707022.6376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_TwoHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfYear_AR 116 24707021.3327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfYear_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_None_AR 120 24707021.3605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_None_NoAR 56 24707021.3605 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_AR 112 24707021.3605 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_NoAR 48 24707021.3605 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_AR 116 24707022.1485 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 24707022.1485 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 24707022.1308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_NoAR 56 24707022.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_EightHourOfWeek_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_EightHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_FourHourOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_FourHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_HourOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_HourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_SixHourOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_SixHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_ThreeHourOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_ThreeHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_TwelveHourOfWeek_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_TwelveHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_TwoHourOfWeek_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_TwoHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfYear_AR 116 24707021.8518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfYear_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_None_AR 120 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_None_NoAR 56 24707021.9062 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_AR 112 24707021.9062 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_NoAR 48 24707021.9062 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_AR 100 0.033 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_NoAR 36 2.2786 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0451 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.7111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_WeekOfYear_AR 100 10.4044 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_WeekOfYear_NoAR 36 6.7985 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.9517 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_AR 96 0.0436 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_AR 132 0.0256 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_NoAR 68 0.0244 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0256 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_AR 132 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_NoAR 68 0.0246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0246 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_AR 128 0.0439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_AR 116 0.0257 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_NoAR 52 0.0231 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0257 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfYear_AR 116 0.1176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfYear_NoAR 52 0.6569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0282 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_AR 112 0.034 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_NoAR 48 0.6387 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_AR 116 0.0308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_NoAR 52 0.1106 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.1106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_AR 116 0.1925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_NoAR 52 0.6425 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.6215 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_AR 112 0.0361 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 0.6016 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 24.471457958221436 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 29.748845100402832 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-05-12T11:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3988 Min=1.0 Max=10.694835872107333 Mean=6.042416327750525 StdDev=2.801426173695981 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.694835872107333 Mean=6.042416327750525 StdDev=2.801426173695981 @@ -104,29 +272,30 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_Hour_r INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour' [Seasonal_Hour] INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.018 MAPE_Forecast=0.0174 MAPE_Test=0.0121 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.018 SMAPE_Forecast=0.0173 SMAPE_Test=0.0121 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.018 MAPE_Forecast=0.0174 MAPE_Test=0.0122 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.018 SMAPE_Forecast=0.0174 SMAPE_Test=0.0122 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0252 MASE_Forecast=0.0248 MASE_Test=0.0263 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07989849853732847 L1_Forecast=0.07860989368042928 L1_Test=0.06414746537239628 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09982593054682697 L2_Forecast=0.09804344569647 L2_Test=0.08047657715069793 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07974029314534574 L1_Forecast=0.0785321039028717 L1_Test=0.0639597501080064 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10007733848586725 L2_Forecast=0.0981572063280222 L2_Test=0.07985530660970212 INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.042336938156116 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_Hour 0.0030733457294287447 {0: -3.9205093728686653, 1: -2.2918060320399545, 2: -1.4430526395707624, 3: 2.725041627333467, 4: 3.549178383274623, 5: 2.3335367452325135, 6: -3.52690980948847, 7: 4.414688421777947, 8: -2.6743069240623267, 9: -0.19034286059738292, 10: -4.782173619898836, 11: -2.7129595358958056, 12: -2.261812976703247, 13: 4.393168135000041, 14: 0.6471518767607152, 15: 1.8934320834645266, 16: -3.0910050185009927, 17: 0.2357867077965552, 18: -1.8577890525822398, 19: -1.4207121916583185, 20: 3.1450813821518873, 21: 2.719160522848914, 22: 1.4654824832773419, 23: 2.7488170026903918} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END GENERATING_RANDOM_DATASET Signal_4000_H_0_constant_24_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0121 0.0263 -1 None _Signal ... 0.0121 0.0263 -2 None _Signal ... 0.0121 0.0263 -4 None _Signal ... 0.0125 0.0270 -3 None _Signal ... 0.0121 0.0263 - -[5 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0121 0.0263 -1 None _Signal ... 0.0121 0.0263 -2 None _Signal ... 0.0121 0.0263 -3 None _Signal ... 0.0121 0.0263 -4 None _Signal ... 0.0125 0.0270 +0 None _Signal ... 0.0122 0.0263 +1 None _Signal ... 0.0122 0.0263 +2 None _Signal ... 0.0121 0.0261 +3 None _Signal ... 0.0121 0.0261 +4 None _Signal ... 0.0123 0.0262 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_70_test_artificial_filter_seasonals_min.log b/tests/references/bugs_issue_70_test_artificial_filter_seasonals_min.log index 10b1d4dec..a543a1fec 100644 --- a/tests/references/bugs_issue_70_test_artificial_filter_seasonals_min.log +++ b/tests/references/bugs_issue_70_test_artificial_filter_seasonals_min.log @@ -1,652 +1,875 @@ INFO:pyaf.std:START_TRAINING 'Signal' -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Minute_AR 68 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Minute_NoAR 4 0.4491 +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 68 0.0386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 4 0.4823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Minute_residue_AR(64) 68 0.0848 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 4 0.0682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.5026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 100 0.0265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 36 0.6724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Minute_residue_AR(64) 100 0.143 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 36 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0206 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 84 0.0386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 20 0.4822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Minute_residue_AR(64) 84 0.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Minute_residue_NoAR 20 0.0682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.5026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 84 0.0384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 20 0.481 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Minute_residue_AR(64) 84 0.0873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Minute_residue_NoAR 20 0.0672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0168 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0236 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.5014 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 3.1163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 5140.8958 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 3.3458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 6.8482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.3945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 29.873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 6756.1441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 4.0354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 3.2734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 7.8058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 3.0029 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 5139.7975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 3.3968 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.5631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 6.9989 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.4778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 2.9267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 5134.1633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Minute_residue_AR(64) 116 143.0617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Minute_residue_NoAR 52 105.3154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 3.3982 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 6.443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 6.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 6.6748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.7953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.7953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 24396472.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 0.6326 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Minute_residue_AR(64) 100 1.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 36 0.023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.3937 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 0.049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Minute_residue_AR(64) 132 0.172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 68 0.0805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 0.0436 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Minute_residue_AR(64) 116 0.0488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Minute_residue_NoAR 52 0.454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 0.056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 0.4758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Minute_residue_AR(64) 116 0.1103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Minute_residue_NoAR 52 0.4616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.4758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_AR 68 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_NoAR 4 0.6153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Minute_AR 68 0.1724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Minute_NoAR 4 0.4321 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_AR 72 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_AR 64 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 0.6173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Minute_AR 100 0.0223 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Minute_NoAR 36 0.5478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_AR 100 0.0274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_NoAR 36 0.8107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Minute_AR 100 0.1916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Minute_NoAR 36 0.5434 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0207 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0243 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_AR 96 0.0237 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_NoAR 32 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Minute_AR 84 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Minute_NoAR 20 0.4491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_AR 84 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_NoAR 20 0.6153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Minute_AR 84 0.1723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Minute_NoAR 20 0.432 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_AR 88 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_AR 80 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_NoAR 16 0.6173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Minute_AR 84 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Minute_NoAR 20 0.4484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_AR 84 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_NoAR 20 0.6145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Minute_AR 84 0.1719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Minute_NoAR 20 0.4314 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_AR 80 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_NoAR 16 0.6165 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Minute_AR 100 4.4026 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Minute_NoAR 36 0.4381 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 3.2372 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.1991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_AR 100 3.3411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_NoAR 36 4943.4232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 3.3274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.0881 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_AR 96 6.9983 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_NoAR 32 0.458 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Minute_AR 132 1.9648 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Minute_NoAR 68 0.5949 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 4.1867 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_AR 132 31.0933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_NoAR 68 6495.6385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 3.6397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 3.1158 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_AR 128 7.5123 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Minute_AR 116 3.3271 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Minute_NoAR 52 0.9651 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 2.5127 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 0.432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_AR 116 4.9488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_NoAR 52 4942.9548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 2.5942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 1.0679 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_AR 112 4.4567 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_NoAR 48 0.8655 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Minute_AR 116 3.9829 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Minute_NoAR 52 2.9652 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 2.3993 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 2.4343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_AR 116 13.5559 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_NoAR 52 4940.5403 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Minute_AR 116 127.4476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Minute_NoAR 52 104.9606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 2.3594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 2.766 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_AR 112 5.4989 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_NoAR 48 2.8441 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Hour_AR 100 26067285.4233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Hour_AR 100 26067285.2065 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Hour_NoAR 36 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 26067285.4545 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 26067285.6044 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_NoAR 40 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_AR 96 26067285.1893 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_NoAR 32 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_AR 132 26067285.3823 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_NoAR 68 26067285.4714 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 26067285.3964 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 26067285.399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_AR 132 26067285.4607 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_NoAR 68 0.7813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 26067285.6431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.7813 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_AR 128 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_AR 116 26067284.6895 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_AR 116 26067285.4111 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_NoAR 52 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 26067285.4104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 26067285.3462 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_NoAR 56 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_AR 112 26067286.0661 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_NoAR 48 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_AR 116 26067285.9919 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_NoAR 52 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 26067285.3905 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_AR 116 26067285.4016 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_NoAR 52 26067286.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 26067285.4712 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_NoAR 56 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_AR 112 26067285.0189 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_NoAR 48 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Minute_AR 100 1.0096 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Minute_NoAR 36 2.6448 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0421 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.6869 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_AR 100 6.6553 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_NoAR 36 4.9266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Minute_AR 100 1.0102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Minute_NoAR 36 2.5887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.9035 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_AR 96 0.0307 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Minute_AR 132 0.0277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Minute_NoAR 68 0.5478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_AR 132 0.0365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_NoAR 68 0.7824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Minute_AR 132 0.2217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Minute_NoAR 68 0.5469 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0246 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0243 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_AR 128 0.0308 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Minute_AR 116 0.027 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Minute_NoAR 52 0.449 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0246 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_AR 116 0.0363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_NoAR 52 0.6179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Minute_AR 116 0.0471 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Minute_NoAR 52 0.446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_None_AR 120 0.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_None_NoAR 56 0.6171 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_AR 112 0.0292 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_NoAR 48 0.6171 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Minute_AR 116 0.0532 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Minute_NoAR 52 0.438 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.0518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_AR 116 0.0865 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_NoAR 52 0.6088 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Minute_AR 116 0.1377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Minute_NoAR 52 0.4458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0315 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.5987 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_AR 112 0.0297 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 0.5966 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 68.87536334991455 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 62.881274700164795 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-23T04:59:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=39988 Min=1.0 Max=10.897580959128534 Mean=6.140492375892452 StdDev=2.7988298627673656 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.897580959128534 Mean=6.140492375892452 StdDev=2.7988298627673656 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0175 MAPE_Test=0.0174 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.0174 SMAPE_Test=0.0173 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0255 MASE_Forecast=0.0252 MASE_Test=0.0366 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08045301833662358 L1_Forecast=0.07946557052934489 L1_Test=0.08811779996857634 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10078936173627877 L2_Forecast=0.09970028244744923 L2_Test=0.10062817922654986 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0175 MAPE_Test=0.0173 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.0174 SMAPE_Test=0.0172 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0255 MASE_Forecast=0.0252 MASE_Test=0.0364 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.0804348467171545 L1_Forecast=0.07948584226519684 L1_Test=0.08767596212085278 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10081207145485091 L2_Forecast=0.09971875121220657 L2_Test=0.10001242074031202 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.140462622354592 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 24 0.02263612957852823 {0: -3.9400060922641456, 1: -2.278158753022309, 2: -1.4362191046096884, 3: 2.72154194832852, 4: 3.555228542486237, 5: 2.3133176380447065, 6: -3.5200555446631014, 7: 4.39450688822107, 8: -2.6936526790187036, 9: -0.18775334547582467, 10: -4.774475484995788, 11: -2.695137639608304, 12: -2.2749431439086734, 13: 4.393595554394465, 14: 0.6423782837336147, 15: 1.8952557869710658, 16: -3.1000666485284656, 17: 0.22660077964108272, 18: -1.8589229456517278, 19: -1.4393011510930362, 20: 3.1373537989988822, 21: 2.722522700949633, 22: 1.472946552209819, 23: 2.7271353537859895} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END GENERATING_RANDOM_DATASET Signal_40000_min_0_constant_24_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0174 0.0366 -1 None _Signal ... 0.0174 0.0366 -2 None _Signal ... 0.0171 0.0366 -16 None _Signal ... 0.0253 0.0488 -6 None CumSum_Signal ... 0.0164 0.0356 - -[5 rows x 20 columns] Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0174 0.0366 -1 None _Signal ... 0.0174 0.0366 -2 None _Signal ... 0.0171 0.0366 -3 None _Signal ... 0.0170 0.0358 -4 None _Signal ... 0.0170 0.0358 +0 None _Signal ... 0.0173 0.0364 +1 None _Signal ... 0.0173 0.0364 +2 None _Signal ... 0.0170 0.0364 +3 None _Signal ... 0.0170 0.0357 +4 None _Signal ... 0.0169 0.0357 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_70_test_artificial_filter_seasonals_second.log b/tests/references/bugs_issue_70_test_artificial_filter_seasonals_second.log index cfd50831b..362154337 100644 --- a/tests/references/bugs_issue_70_test_artificial_filter_seasonals_second.log +++ b/tests/references/bugs_issue_70_test_artificial_filter_seasonals_second.log @@ -1,652 +1,749 @@ INFO:pyaf.std:START_TRAINING 'Signal' -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Second_AR 68 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Second_NoAR 4 0.4491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Second_residue_AR(64) 68 0.0848 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Second_residue_NoAR 4 0.0682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.5026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Second_residue_AR(64) 100 0.143 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Second_residue_NoAR 36 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0206 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Second_residue_AR(64) 84 0.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Second_residue_NoAR 20 0.0682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.5026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Second_residue_AR(64) 84 0.0873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Second_residue_NoAR 20 0.0672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0168 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0236 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.5014 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Second_residue_AR(64) 100 140.4611 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Second_residue_NoAR 36 111.726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 3.3458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 6.8482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.3945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Second_residue_AR(64) 132 189.263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Second_residue_NoAR 68 445.3032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 4.0354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 3.2734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 7.8058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Second_residue_AR(64) 116 143.2381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Second_residue_NoAR 52 110.5978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 3.3968 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.5631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 6.9989 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.4778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Second_residue_AR(64) 116 143.0617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Second_residue_NoAR 52 105.3154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 3.3982 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 6.443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 6.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 6.6748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Second_residue_AR(64) 100 24396473.0978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Second_residue_NoAR 36 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Second_residue_AR(64) 132 24396473.0978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Second_residue_NoAR 68 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.7953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Second_residue_AR(64) 116 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Second_residue_NoAR 52 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Second_residue_AR(64) 116 24396472.6293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Second_residue_NoAR 52 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Second_residue_AR(64) 100 1.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Second_residue_NoAR 36 0.023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.3937 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Second_residue_AR(64) 132 0.172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Second_residue_NoAR 68 0.0805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Second_residue_AR(64) 116 0.0488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Second_residue_NoAR 52 0.454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Second_residue_AR(64) 116 0.1103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Second_residue_NoAR 52 0.4616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.4758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Second_AR 68 0.1724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Second_NoAR 4 0.4321 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_AR 72 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_AR 64 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 0.6173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Second_AR 100 0.0223 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Second_NoAR 36 0.5478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Second_AR 100 0.1916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Second_NoAR 36 0.5434 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0207 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0243 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_AR 96 0.0237 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_NoAR 32 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Second_AR 84 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Second_NoAR 20 0.4491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Second_AR 84 0.1723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Second_NoAR 20 0.432 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_AR 88 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_AR 80 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_NoAR 16 0.6173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Second_AR 84 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Second_NoAR 20 0.4484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Second_AR 84 0.1719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Second_NoAR 20 0.4314 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_AR 80 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_NoAR 16 0.6165 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Second_AR 100 4.4026 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Second_NoAR 36 0.4381 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 3.2372 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.1991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Second_AR 100 136.4718 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Second_NoAR 36 107.5452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 3.3274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.0881 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_AR 96 6.9983 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_NoAR 32 0.458 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Second_AR 132 1.9648 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Second_NoAR 68 0.5949 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 4.1867 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Second_AR 132 184.2688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Second_NoAR 68 428.1516 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 3.6397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 3.1158 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_AR 128 7.5123 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Second_AR 116 3.3271 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Second_NoAR 52 0.9651 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 2.5127 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 0.432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Second_AR 116 97.4692 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Second_NoAR 52 107.0456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 2.5942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 1.0679 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_AR 112 4.4567 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_NoAR 48 0.8655 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Second_AR 116 3.9829 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Second_NoAR 52 2.9652 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 2.3993 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 2.4343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Second_AR 116 127.4476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Second_NoAR 52 104.9606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 2.3594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 2.766 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_AR 112 5.4989 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_NoAR 48 2.8441 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Second_AR 100 0.7813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Second_AR 100 26067285.2568 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Second_NoAR 36 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 26067285.4545 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 26067285.6044 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_NoAR 40 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_AR 96 26067285.1893 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_NoAR 32 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Second_AR 132 26067285.413 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Second_NoAR 68 26067285.4141 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 26067285.3964 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 26067285.399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Second_AR 132 26067285.3817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Second_NoAR 68 26067285.4406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 26067285.6431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.7813 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_AR 128 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Second_AR 116 20979257.8974 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Second_NoAR 52 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 26067285.4104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Second_AR 116 26067285.3924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Second_NoAR 52 26067286.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 26067285.3462 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_NoAR 56 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_AR 112 26067286.0661 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_NoAR 48 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Second_AR 116 26067285.3877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Second_AR 116 26067285.4283 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Second_NoAR 52 26067286.1927 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 26067285.3905 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 26067285.4712 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_NoAR 56 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_AR 112 26067285.0189 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_NoAR 48 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Second_AR 100 1.0096 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Second_NoAR 36 2.6448 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0421 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.6869 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Second_AR 100 1.0102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Second_NoAR 36 2.5887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.9035 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_AR 96 0.0307 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Second_AR 132 0.0277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Second_NoAR 68 0.5478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Second_AR 132 0.2217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Second_NoAR 68 0.5469 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0246 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0243 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_AR 128 0.0308 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Second_AR 116 0.027 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Second_NoAR 52 0.449 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0246 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Second_AR 116 0.0471 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Second_NoAR 52 0.446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_None_AR 120 0.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_None_NoAR 56 0.6171 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_AR 112 0.0292 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_NoAR 48 0.6171 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Second_AR 116 0.0532 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Second_NoAR 52 0.438 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.0518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Second_AR 116 0.1377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Second_NoAR 52 0.4458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0315 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.5987 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_AR 112 0.0297 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 0.5966 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 64.20085954666138 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 52.17273688316345 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-01T08:52:59.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=39988 Min=1.0 Max=10.897580959128534 Mean=6.140492375892452 StdDev=2.7988298627673656 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.897580959128534 Mean=6.140492375892452 StdDev=2.7988298627673656 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0175 MAPE_Test=0.0174 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.0174 SMAPE_Test=0.0173 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0255 MASE_Forecast=0.0252 MASE_Test=0.0366 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08045301833662358 L1_Forecast=0.07946557052934489 L1_Test=0.08811779996857634 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10078936173627877 L2_Forecast=0.09970028244744923 L2_Test=0.10062817922654986 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0175 MAPE_Test=0.0173 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.0174 SMAPE_Test=0.0172 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0255 MASE_Forecast=0.0252 MASE_Test=0.0364 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.0804348467171545 L1_Forecast=0.07948584226519684 L1_Test=0.08767596212085278 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10081207145485091 L2_Forecast=0.09971875121220657 L2_Test=0.10001242074031202 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.140462622354592 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 24 0.02263612957852823 {0: -3.9400060922641456, 1: -2.278158753022309, 2: -1.4362191046096884, 3: 2.72154194832852, 4: 3.555228542486237, 5: 2.3133176380447065, 6: -3.5200555446631014, 7: 4.39450688822107, 8: -2.6936526790187036, 9: -0.18775334547582467, 10: -4.774475484995788, 11: -2.695137639608304, 12: -2.2749431439086734, 13: 4.393595554394465, 14: 0.6423782837336147, 15: 1.8952557869710658, 16: -3.1000666485284656, 17: 0.22660077964108272, 18: -1.8589229456517278, 19: -1.4393011510930362, 20: 3.1373537989988822, 21: 2.722522700949633, 22: 1.472946552209819, 23: 2.7271353537859895} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END GENERATING_RANDOM_DATASET Signal_40000_S_0_constant_24_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0174 0.0366 -1 None _Signal ... 0.0174 0.0366 -2 None _Signal ... 0.0171 0.0366 -16 None _Signal ... 0.0253 0.0488 -6 None CumSum_Signal ... 0.0164 0.0356 - -[5 rows x 20 columns] Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0174 0.0366 -1 None _Signal ... 0.0174 0.0366 -2 None _Signal ... 0.0171 0.0366 -3 None _Signal ... 0.0170 0.0358 -4 None _Signal ... 0.0170 0.0358 +0 None _Signal ... 0.0173 0.0364 +1 None _Signal ... 0.0173 0.0364 +2 None _Signal ... 0.0170 0.0364 +3 None _Signal ... 0.0170 0.0357 +4 None _Signal ... 0.0169 0.0357 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_day.log b/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_day.log index f2377de90..b95253e1d 100644 --- a/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_day.log +++ b/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_day.log @@ -1,164 +1,485 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfMonth_AR 68 0.032 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfMonth_NoAR 4 0.6413 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_AR 68 0.026 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_NoAR 4 0.6409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 68 0.0392 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 4 0.502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 68 0.1096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 4 0.4926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 68 0.0249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 4 0.5098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 68 0.0286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 4 0.508 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 68 0.0626 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 4 0.5089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0122 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.5041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(64) 100 0.0389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 36 0.6783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 100 0.0427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 0.6915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.0265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.6818 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(64) 100 0.0332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 0.6807 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 0.0522 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 36 0.656 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 84 0.0391 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 0.5023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 84 0.1099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 84 0.0251 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 84 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 0.5084 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 84 0.0624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 0.5093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.5045 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 84 0.0397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 84 0.1081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.4903 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 84 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.5075 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 84 0.0282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 0.5055 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 84 0.0642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 0.5065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.5016 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 100 0.2142 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 36 532.8468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 100 1.2451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 519.1982 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.8875 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 535.1638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 100 0.5569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 521.2862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 0.8583 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 534.3802 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 2.7384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.7071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.4128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(64) 132 0.7383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 665.2607 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 132 3.2916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 682.8307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 0.0277 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 670.4008 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(64) 132 0.2683 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 661.9817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 132 0.7369 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 523.0715 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0412 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 2.9057 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 531.7543 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 4.843 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 516.9205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 4.5365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 534.7636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 2.3964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 521.3363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 2.7515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 533.3967 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 1.0024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 3.7097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 4.0527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 7.3486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 530.0647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 9.5599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 515.1232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 12.5066 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 532.6148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 4.81 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 519.6106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 7.3866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 531.9058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 2.3086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 4.9748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 11.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 3.3467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 100 1.1981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 36 3.8805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 100 9.757 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 5.7878 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.0435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 1.4566 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 100 4.2467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 3.2427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 3.6715 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 2.311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.4039 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(64) 132 0.0428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 0.6559 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 132 0.1279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 0.6843 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 0.04 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.6744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(64) 132 0.0432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 0.6579 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 132 0.0826 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.6808 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 0.1417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 0.5549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 0.1736 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 0.4257 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.0259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.5013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 0.1009 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 0.5194 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.0679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 0.4838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 0.0821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 0.5113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 0.5266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 0.5904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.0331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.3834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 0.1981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 0.4622 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.1564 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 0.5092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.4871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfMonth_AR 68 0.038 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfMonth_NoAR 4 0.643 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_AR 68 0.1114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 4 0.6657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_AR 68 0.0286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_NoAR 4 0.641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfMonth_AR 68 0.0432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfMonth_NoAR 4 0.642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfYear_AR 68 0.0518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfYear_NoAR 4 0.6538 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_AR 72 0.0178 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 0.0174 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_AR 64 0.0259 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 0.6409 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfMonth_AR 100 0.036 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfMonth_NoAR 36 0.8137 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_AR 100 0.0282 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_NoAR 36 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0245 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfMonth_AR 100 0.0356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfMonth_NoAR 36 0.8419 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_AR 100 0.0791 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_NoAR 36 0.867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_AR 100 0.0285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_NoAR 36 0.8427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfMonth_AR 100 0.0422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfMonth_NoAR 36 0.8415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfYear_AR 100 0.0437 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfYear_NoAR 36 0.8449 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0244 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_AR 96 0.0282 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_NoAR 32 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfMonth_AR 84 0.032 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfMonth_NoAR 20 0.6416 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_AR 84 0.026 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_NoAR 20 0.6413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfMonth_AR 84 0.038 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfMonth_NoAR 20 0.6434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfNthWeekOfMonth_AR 84 0.1114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 20 0.666 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_AR 84 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_NoAR 20 0.6415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfMonth_AR 84 0.0432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfMonth_NoAR 20 0.6424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfYear_AR 84 0.0519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfYear_NoAR 20 0.6542 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_AR 88 0.018 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_AR 80 0.026 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_NoAR 16 0.6413 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfMonth_AR 84 0.032 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfMonth_NoAR 20 0.6393 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_AR 84 0.0261 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_NoAR 20 0.639 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.0179 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfMonth_AR 84 0.0381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfMonth_NoAR 20 0.6411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfNthWeekOfMonth_AR 84 0.1115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 20 0.664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_AR 84 0.0286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_NoAR 20 0.6394 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfMonth_AR 84 0.0433 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfMonth_NoAR 20 0.6402 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfYear_AR 84 0.0517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfYear_NoAR 20 0.6518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0176 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_AR 80 0.0261 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_NoAR 16 0.639 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfMonth_AR 100 0.2608 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfMonth_NoAR 36 0.4847 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.4115 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 0.4741 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 0.0259 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.1681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfMonth_AR 100 0.0999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfMonth_NoAR 36 519.5248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_AR 100 1.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 36 506.731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.4816 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 521.762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_WeekOfMonth_AR 100 1.2875 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_WeekOfMonth_NoAR 36 508.8335 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_WeekOfYear_AR 100 0.6124 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_WeekOfYear_NoAR 36 520.9068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 2.8193 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_AR 96 0.3793 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_NoAR 32 0.4735 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfMonth_AR 132 0.7301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfMonth_NoAR 68 0.9879 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0553 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 1.0151 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 0.0391 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfMonth_AR 132 0.4733 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfMonth_NoAR 68 648.4255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_AR 132 2.1677 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_NoAR 68 666.6112 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0348 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 653.4679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_WeekOfMonth_AR 132 0.4844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_WeekOfMonth_NoAR 68 645.2576 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_WeekOfYear_AR 132 0.8084 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_WeekOfYear_NoAR 68 511.5418 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.1701 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_AR 128 0.0461 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfMonth_AR 116 1.6423 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfMonth_NoAR 52 0.9866 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_AR 116 2.1882 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 0.9409 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 0.5122 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 0.5003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfMonth_AR 116 1.4921 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfMonth_NoAR 52 519.0867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 1.9873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 505.1367 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_AR 116 2.2458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 521.9988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_WeekOfMonth_AR 116 1.5706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_WeekOfMonth_NoAR 52 509.5176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_WeekOfYear_AR 116 1.8512 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_WeekOfYear_NoAR 52 520.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 0.5099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 3.1398 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_AR 112 2.0122 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_NoAR 48 0.9366 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfMonth_AR 116 3.1862 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfMonth_NoAR 52 1.6567 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_AR 116 5.287 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 1.6084 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 1.0189 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 1.1798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfMonth_AR 116 3.2263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfMonth_NoAR 52 518.4689 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 3.7197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 504.4155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_AR 116 5.3731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 520.9331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_WeekOfMonth_AR 116 2.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_WeekOfMonth_NoAR 52 508.8663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_WeekOfYear_AR 116 3.6414 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_WeekOfYear_NoAR 52 520.1498 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 1.0152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 3.3441 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_AR 112 4.8601 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_NoAR 48 1.6038 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfMonth_AR 100 24707021.9712 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfMonth_AR 100 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfMonth_NoAR 36 24707021.1999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_AR 100 24707022.0852 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_AR 100 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_WeekOfMonth_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_WeekOfMonth_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_WeekOfYear_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_WeekOfYear_NoAR 36 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_NoAR 40 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_AR 96 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_NoAR 32 24707021.1999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfMonth_AR 132 24707021.9754 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfMonth_NoAR 68 24707021.9305 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_AR 132 24707022.0852 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 24707021.9962 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 24707022.1308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 24707021.9759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfMonth_AR 132 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfMonth_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_AR 132 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_AR 132 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_WeekOfMonth_AR 132 24707021.2062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_WeekOfMonth_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_WeekOfYear_AR 132 24707021.5327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_WeekOfYear_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 24707022.0174 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_AR 128 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfMonth_AR 116 24707021.9754 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfMonth_NoAR 52 24707021.9731 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_AR 116 24707021.8633 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 24707022.0852 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 24707021.8177 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_NoAR 56 24707022.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfMonth_AR 116 24707021.3408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 24707021.4018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_AR 116 24707021.3608 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfMonth_AR 116 24707022.5738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfMonth_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfYear_AR 116 24707021.2657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfYear_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_None_AR 120 24707021.3605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_None_NoAR 56 24707021.3605 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_AR 112 24707021.3605 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_NoAR 48 24707021.3605 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfMonth_AR 116 24707021.9731 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfMonth_NoAR 52 24707021.9754 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_AR 116 24707022.0852 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 24707022.0852 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 24707022.1308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_NoAR 56 24707022.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfMonth_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfMonth_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfMonth_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfYear_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfYear_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_None_AR 120 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_None_NoAR 56 24707021.9062 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_AR 112 24707021.9062 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_NoAR 48 24707021.9062 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfMonth_AR 100 2.5922 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfMonth_NoAR 36 2.608 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.0451 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 1.7111 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0451 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.7111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfMonth_AR 100 3.0735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfMonth_NoAR 36 3.2139 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_AR 100 22.4591 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 36 14.6575 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.0449 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 1.4221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_WeekOfMonth_AR 100 11.0387 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_WeekOfMonth_NoAR 36 6.9099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_WeekOfYear_AR 100 9.1237 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_WeekOfYear_NoAR 36 5.0758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.9517 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_AR 96 0.0436 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfMonth_AR 132 0.0537 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfMonth_NoAR 68 0.814 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0446 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0256 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfMonth_AR 132 0.0628 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfMonth_NoAR 68 0.8136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_AR 132 0.1629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfNthWeekOfMonth_NoAR 68 0.8684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 0.815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_WeekOfMonth_AR 132 0.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_WeekOfMonth_NoAR 68 0.817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_WeekOfYear_AR 132 0.0698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_WeekOfYear_NoAR 68 0.8175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0246 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_AR 128 0.0439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfMonth_AR 116 0.0525 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfMonth_NoAR 52 0.6388 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_AR 116 0.0372 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_NoAR 52 0.6388 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0257 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfMonth_AR 116 0.1654 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfMonth_NoAR 52 0.6574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 0.3482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 0.7313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_AR 116 0.0376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_NoAR 52 0.6397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfMonth_AR 116 0.1795 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfMonth_NoAR 52 0.6746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfYear_AR 116 0.1881 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfYear_NoAR 52 0.6728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0282 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_AR 112 0.034 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_NoAR 48 0.6387 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfMonth_AR 116 0.11 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfMonth_NoAR 52 0.6105 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_AR 116 0.0402 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_NoAR 52 0.6021 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.1106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfMonth_AR 116 0.2083 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfMonth_NoAR 52 0.6528 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfNthWeekOfMonth_AR 116 0.726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfNthWeekOfMonth_NoAR 52 0.7107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_AR 116 0.0401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_NoAR 52 0.6126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfMonth_AR 116 0.3883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfMonth_NoAR 52 0.6603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_AR 116 0.3207 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_NoAR 52 0.6614 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.6215 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_AR 112 0.0361 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 0.6016 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 31.662445783615112 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 45.19954299926758 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2008-09-14T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3988 Min=1.0 Max=10.694835872107333 Mean=6.042416327750525 StdDev=2.801426173695981 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.694835872107333 Mean=6.042416327750525 StdDev=2.801426173695981 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.018 MAPE_Forecast=0.0174 MAPE_Test=0.0121 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.018 SMAPE_Forecast=0.0173 SMAPE_Test=0.0121 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.018 MAPE_Forecast=0.0174 MAPE_Test=0.0122 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.018 SMAPE_Forecast=0.0174 SMAPE_Test=0.0122 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0252 MASE_Forecast=0.0248 MASE_Test=0.0263 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07989849853732847 L1_Forecast=0.07860989368042928 L1_Test=0.06414746537239628 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09982593054682697 L2_Forecast=0.09804344569647 L2_Test=0.08047657715069793 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07974029314534574 L1_Forecast=0.0785321039028717 L1_Test=0.0639597501080064 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10007733848586725 L2_Forecast=0.0981572063280222 L2_Test=0.07985530660970212 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.042336938156116 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 24 0.0030733457294287447 {0: -3.9205093728686653, 1: -2.2918060320399545, 2: -1.4430526395707624, 3: 2.725041627333467, 4: 3.549178383274623, 5: 2.3335367452325135, 6: -3.52690980948847, 7: 4.414688421777947, 8: -2.6743069240623267, 9: -0.19034286059738292, 10: -4.782173619898836, 11: -2.7129595358958056, 12: -2.261812976703247, 13: 4.393168135000041, 14: 0.6471518767607152, 15: 1.8934320834645266, 16: -3.0910050185009927, 17: 0.2357867077965552, 18: -1.8577890525822398, 19: -1.4207121916583185, 20: 3.1450813821518873, 21: 2.719160522848914, 22: 1.4654824832773419, 23: 2.7488170026903918} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END GENERATING_RANDOM_DATASET Signal_4000_D_0_constant_24_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0121 0.0263 -1 None _Signal ... 0.0121 0.0263 -2 None _Signal ... 0.0125 0.0270 -9 None _Signal ... 0.0249 0.0477 -6 None CumSum_Signal ... 0.0192 0.0324 - -[5 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0121 0.0263 -1 None _Signal ... 0.0121 0.0263 -2 None _Signal ... 0.0125 0.0270 -3 None _Signal ... 0.0119 0.0264 -4 None _Signal ... 0.0124 0.0273 +0 None _Signal ... 0.0122 0.0263 +1 None _Signal ... 0.0121 0.0261 +2 None _Signal ... 0.0123 0.0262 +3 None _Signal ... 0.0120 0.0263 +4 None _Signal ... 0.0120 0.0266 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_hour.log b/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_hour.log index 8d625ee49..a4873c2aa 100644 --- a/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_hour.log +++ b/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_hour.log @@ -1,133 +1,773 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_AR 68 0.0262 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_NoAR 4 0.6409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 68 0.0268 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 4 0.5089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 68 0.0274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 4 0.564 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 68 0.0227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 4 0.2352 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 68 0.0124 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR 4 0.0123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 68 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 4 0.0122 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 68 0.0291 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 4 0.5619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 68 0.0186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 4 0.2416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 68 0.0215 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 4 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 68 0.0253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 4 0.2017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 68 0.0257 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 4 0.5044 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0122 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.5041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.0285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.6833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 100 0.0421 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 0.6595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 100 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 0.6536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_AR(64) 100 0.0231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 36 0.0225 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 100 0.0223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 36 0.0233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 100 0.0467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 0.6698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 100 0.0364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 0.6703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 100 0.0296 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 0.6709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 100 0.0415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 0.5955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 0.03 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 36 0.6825 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 84 0.0267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.5093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 84 0.0274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 0.5646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 84 0.0227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 0.2355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 84 0.0123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 0.0121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 84 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 20 0.0121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 84 0.0291 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 0.5626 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 84 0.0186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 0.242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 84 0.0216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 0.4275 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 84 0.0257 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 0.2019 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 84 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 0.5048 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.5045 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 84 0.0282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.5061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 84 0.0275 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 20 0.5609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 84 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 20 0.2335 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 84 0.0131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 20 0.0121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 84 0.0125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 20 0.0123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 84 0.0288 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 20 0.5581 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 84 0.0198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 20 0.2388 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 84 0.0225 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 20 0.4249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 84 0.0238 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 20 0.2003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 84 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 0.502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.5016 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.5286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 536.275 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 100 0.4912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 463.5916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 100 0.2495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 36.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 100 0.022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR 36 3.5254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 0.0272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 2.7384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 100 0.3153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 89.6632 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 100 0.2454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 43.6894 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 100 0.0439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 530.0785 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 100 0.318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 35.1549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 1.0768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 537.2636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 2.7384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.7071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.4128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 0.4672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 676.5821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 132 0.3139 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 295.6565 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 132 0.4169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 80.3211 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_AR(64) 132 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 0.6554 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 0.0424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 132 0.3687 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 440.6657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 132 0.0615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 448.2573 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 132 0.3992 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 238.2334 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 132 0.0829 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 13.4452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 132 0.585 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 666.7135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0412 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 3.6846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 535.2052 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 116 4.6297 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 461.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 116 3.1716 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 34.53 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 116 0.9964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 4.7262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 1.0024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 3.7097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 116 3.9569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 87.8797 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 116 5.072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 45.0517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 116 5.6704 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 528.3423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 116 1.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 33.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 1.5304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 536.0505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 1.0024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 3.7097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 4.0527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 10.0743 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 533.1284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 116 9.7945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 460.0198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 116 7.0042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 32.7738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 116 2.2993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 6.3363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 2.3086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 4.9748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 116 8.6794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 86.2013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 116 11.3667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 46.6101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 116 12.6438 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 526.815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 116 4.0136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 31.9528 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 2.2765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 534.2424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 2.3086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 4.9748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 11.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 3.3467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR 36 0.7023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 0.7745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 100 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 132 22774023.1834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_AR(64) 132 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 22774022.8941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 132 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 22774022.8941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 22774022.8941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 132 22774023.0172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 22774022.9158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 132 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 22774023.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 22774021.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 116 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 22774022.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 22774022.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 13.1104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 6.6855 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 100 4.6397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 36 4.196 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 100 3.1367 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 36 2.2023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 100 0.0293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR 36 0.0231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 0.0586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 2.9197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 100 4.0665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 36 2.7859 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 100 2.6907 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 36 1.6783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 100 6.5336 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 36 4.0401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 100 1.9847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 36 1.3126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 0.5659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.4039 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 0.0443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.6678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 132 0.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR 68 0.705 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 132 0.0445 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_FourHourOfWeek_residue_NoAR 68 0.3566 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_AR(64) 132 0.025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_HourOfWeek_residue_NoAR 68 0.0231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 0.024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.0227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 132 0.0489 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_SixHourOfWeek_residue_NoAR 68 0.6307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 132 0.0295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 68 0.4434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 132 0.0353 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 68 0.5423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 132 0.037 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 68 0.3569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 132 0.0411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.0282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.5129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 116 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 0.6106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 116 0.0413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 0.3653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 116 0.0209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 0.0231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 0.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 0.0098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 116 0.0249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 0.5849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 116 0.0339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 0.3259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 116 0.0379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 0.6129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 116 0.0286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 0.2352 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.0353 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.0098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.1132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_AR(64) 116 0.0817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_EightHourOfWeek_residue_NoAR 52 0.5974 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_AR(64) 116 0.0942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR 52 0.2979 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_AR(64) 116 0.0219 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_HourOfWeek_residue_NoAR 52 0.1333 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 0.032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 0.1639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_AR(64) 116 0.1456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_SixHourOfWeek_residue_NoAR 52 0.5586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_AR(64) 116 0.1964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_ThreeHourOfWeek_residue_NoAR 52 0.3057 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_AR(64) 116 0.0925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwelveHourOfWeek_residue_NoAR 52 0.6225 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_AR(64) 116 0.1032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_TwoHourOfWeek_residue_NoAR 52 0.2535 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 0.448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.4871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_AR 68 0.0274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_DayOfWeek_NoAR 4 0.6425 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_EightHourOfWeek_AR 68 0.0364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_EightHourOfWeek_NoAR 4 0.6071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_FourHourOfWeek_AR 68 0.0255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_FourHourOfWeek_NoAR 4 0.3849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_HourOfWeek_AR 68 0.0187 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_HourOfWeek_NoAR 4 0.0183 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_AR 68 0.0178 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_NoAR 4 0.0174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_SixHourOfWeek_AR 68 0.026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_SixHourOfWeek_NoAR 4 0.49 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_ThreeHourOfWeek_AR 68 0.0253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_ThreeHourOfWeek_NoAR 4 0.3164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_TwelveHourOfWeek_AR 68 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_TwelveHourOfWeek_NoAR 4 0.4977 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_TwoHourOfWeek_AR 68 0.0259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_TwoHourOfWeek_NoAR 4 0.3187 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfYear_AR 68 0.0264 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_WeekOfYear_NoAR 4 0.6401 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_AR 72 0.0178 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 0.0174 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_AR 64 0.0259 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 0.6409 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_AR 100 0.0291 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_NoAR 36 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_AR 100 0.0245 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_NoAR 36 0.0248 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0245 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_AR 100 0.0296 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_DayOfWeek_NoAR 36 0.8428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_EightHourOfWeek_AR 100 0.0345 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_EightHourOfWeek_NoAR 36 0.8422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_FourHourOfWeek_AR 100 0.0337 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_FourHourOfWeek_NoAR 36 0.7722 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_HourOfWeek_AR 100 0.0254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_HourOfWeek_NoAR 36 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_AR 100 0.0241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_NoAR 36 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_SixHourOfWeek_AR 100 0.0342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_SixHourOfWeek_NoAR 36 0.7683 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_ThreeHourOfWeek_AR 100 0.0371 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_ThreeHourOfWeek_NoAR 36 0.6953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_TwelveHourOfWeek_AR 100 0.0323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_TwelveHourOfWeek_NoAR 36 0.8454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_TwoHourOfWeek_AR 100 0.064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_TwoHourOfWeek_NoAR 36 0.7763 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfYear_AR 100 0.0296 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_WeekOfYear_NoAR 36 0.8429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0244 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_AR 96 0.0282 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_NoAR 32 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_AR 84 0.0262 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_NoAR 20 0.6413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_AR 84 0.0274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_DayOfWeek_NoAR 20 0.6429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_EightHourOfWeek_AR 84 0.0365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_EightHourOfWeek_NoAR 20 0.6074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_FourHourOfWeek_AR 84 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_FourHourOfWeek_NoAR 20 0.3852 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_HourOfWeek_AR 84 0.0188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_HourOfWeek_NoAR 20 0.0184 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_AR 84 0.018 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_NoAR 20 0.0174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_NoAR 20 0.0175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_SixHourOfWeek_AR 84 0.026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_SixHourOfWeek_NoAR 20 0.4903 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_ThreeHourOfWeek_AR 84 0.0254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_ThreeHourOfWeek_NoAR 20 0.3167 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_TwelveHourOfWeek_AR 84 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_TwelveHourOfWeek_NoAR 20 0.4981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_TwoHourOfWeek_AR 84 0.0259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_TwoHourOfWeek_NoAR 20 0.319 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfYear_AR 84 0.0264 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_WeekOfYear_NoAR 20 0.6406 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_AR 88 0.018 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_AR 80 0.026 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_NoAR 16 0.6413 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_AR 84 0.0263 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_NoAR 20 0.639 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_AR 84 0.0179 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_NoAR 20 0.0177 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.0179 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_AR 84 0.0273 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_DayOfWeek_NoAR 20 0.6406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_EightHourOfWeek_AR 84 0.0363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_EightHourOfWeek_NoAR 20 0.6053 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_FourHourOfWeek_AR 84 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_FourHourOfWeek_NoAR 20 0.3838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_HourOfWeek_AR 84 0.0188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_HourOfWeek_NoAR 20 0.0185 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_AR 84 0.018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_NoAR 20 0.0176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_SixHourOfWeek_AR 84 0.026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_SixHourOfWeek_NoAR 20 0.4885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_ThreeHourOfWeek_AR 84 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_ThreeHourOfWeek_NoAR 20 0.3152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_TwelveHourOfWeek_AR 84 0.0267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_TwelveHourOfWeek_NoAR 20 0.496 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_TwoHourOfWeek_AR 84 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_TwoHourOfWeek_NoAR 20 0.3175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfYear_AR 84 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_WeekOfYear_NoAR 20 0.6385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0176 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_AR 80 0.0261 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_NoAR 16 0.639 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.5387 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 0.4716 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_AR 100 0.0259 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_NoAR 36 0.1681 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 0.0259 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.1681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_AR 100 0.227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 522.8125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_EightHourOfWeek_AR 100 0.3198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_EightHourOfWeek_NoAR 36 452.1724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_FourHourOfWeek_AR 100 0.1878 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_FourHourOfWeek_NoAR 36 35.3944 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_HourOfWeek_AR 100 0.0311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_HourOfWeek_NoAR 36 3.6034 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_AR 100 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_NoAR 36 2.8193 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_SixHourOfWeek_AR 100 0.2555 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_SixHourOfWeek_NoAR 36 87.2479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_ThreeHourOfWeek_AR 100 0.2654 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_ThreeHourOfWeek_NoAR 36 43.3091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_TwelveHourOfWeek_AR 100 0.3672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_TwelveHourOfWeek_NoAR 36 516.9105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_TwoHourOfWeek_AR 100 0.7131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_TwoHourOfWeek_NoAR 36 34.6268 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_WeekOfYear_AR 100 0.7993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_WeekOfYear_NoAR 36 524.3778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 2.8193 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_AR 96 0.3793 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_NoAR 32 0.4735 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.1056 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 1.0229 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_AR 132 0.0391 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_NoAR 68 0.0287 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 0.0391 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.0287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.2228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 659.838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_EightHourOfWeek_AR 132 0.2214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_EightHourOfWeek_NoAR 68 287.6754 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_FourHourOfWeek_AR 132 0.5467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_FourHourOfWeek_NoAR 68 78.7303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_HourOfWeek_AR 132 0.1081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_HourOfWeek_NoAR 68 0.6411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_AR 132 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_NoAR 68 0.1701 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_SixHourOfWeek_AR 132 0.3251 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_SixHourOfWeek_NoAR 68 428.9254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_ThreeHourOfWeek_AR 132 0.2005 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_ThreeHourOfWeek_NoAR 68 435.2656 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_TwelveHourOfWeek_AR 132 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_TwelveHourOfWeek_NoAR 68 232.9944 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_TwoHourOfWeek_AR 132 0.2434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_TwoHourOfWeek_NoAR 68 13.5066 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_WeekOfYear_AR 132 0.3126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_WeekOfYear_NoAR 68 650.1659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 0.0385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.1701 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_AR 128 0.0461 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_AR 116 2.6607 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 0.9181 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_AR 116 0.5122 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_NoAR 52 0.5003 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 0.5122 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 0.5003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_AR 116 1.9846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 522.3921 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_EightHourOfWeek_AR 116 2.3798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_EightHourOfWeek_NoAR 52 450.9362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_FourHourOfWeek_AR 116 1.6443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_FourHourOfWeek_NoAR 52 34.4004 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_HourOfWeek_AR 116 0.5189 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_HourOfWeek_NoAR 52 4.1462 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_AR 116 0.5099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_NoAR 52 3.1398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_SixHourOfWeek_AR 116 2.0717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_SixHourOfWeek_NoAR 52 86.1373 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_ThreeHourOfWeek_AR 116 2.6268 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_ThreeHourOfWeek_NoAR 52 44.0038 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_TwelveHourOfWeek_AR 116 3.1505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_TwelveHourOfWeek_NoAR 52 515.8448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_TwoHourOfWeek_AR 116 0.6568 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_TwoHourOfWeek_NoAR 52 33.9195 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_WeekOfYear_AR 116 1.0382 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_WeekOfYear_NoAR 52 523.7957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 0.5099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 3.1398 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_AR 112 2.0122 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_NoAR 48 0.9366 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_AR 116 6.3732 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 1.5834 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_AR 116 1.0189 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_NoAR 52 1.1798 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 1.0189 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 1.1798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_AR 116 4.4909 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 521.3999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_EightHourOfWeek_AR 116 4.3554 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_EightHourOfWeek_NoAR 52 450.3471 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_FourHourOfWeek_AR 116 3.1233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_FourHourOfWeek_NoAR 52 33.7173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_HourOfWeek_AR 116 1.0239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_HourOfWeek_NoAR 52 4.6868 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_AR 116 1.0152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_NoAR 52 3.3441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_SixHourOfWeek_AR 116 3.8884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_SixHourOfWeek_NoAR 52 85.5311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_ThreeHourOfWeek_AR 116 5.0566 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_ThreeHourOfWeek_NoAR 52 44.4925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_TwelveHourOfWeek_AR 116 5.8122 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_TwelveHourOfWeek_NoAR 52 515.3862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_TwoHourOfWeek_AR 116 1.4706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_TwoHourOfWeek_NoAR 52 33.1628 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_WeekOfYear_AR 116 1.3462 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_WeekOfYear_NoAR 52 523.0975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 1.0152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 3.3441 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_AR 112 4.8601 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_NoAR 48 1.6038 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_AR 100 24707021.9405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_AR 100 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_EightHourOfWeek_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_EightHourOfWeek_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_FourHourOfWeek_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_FourHourOfWeek_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_HourOfWeek_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_HourOfWeek_NoAR 36 0.6858 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Hour_AR 100 24707021.1999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Hour_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Hour_NoAR 36 0.7535 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_SixHourOfWeek_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_SixHourOfWeek_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_ThreeHourOfWeek_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_ThreeHourOfWeek_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_TwelveHourOfWeek_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_TwelveHourOfWeek_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_TwoHourOfWeek_AR 100 24707022.7486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_TwoHourOfWeek_NoAR 36 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_WeekOfYear_AR 100 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_WeekOfYear_NoAR 36 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_NoAR 40 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_AR 96 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_NoAR 32 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_AR 132 24707021.1999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 24707021.1999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_AR 132 24707021.9754 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_NoAR 68 24707021.9953 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 24707022.1308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 24707021.9759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_EightHourOfWeek_AR 132 24707022.1758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_EightHourOfWeek_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_FourHourOfWeek_AR 132 24707021.9516 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_FourHourOfWeek_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_HourOfWeek_AR 132 24707022.7486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_HourOfWeek_NoAR 68 24707021.871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_AR 132 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_NoAR 68 24707022.0174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_SixHourOfWeek_AR 132 24707021.9883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_SixHourOfWeek_NoAR 68 24707021.9753 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_ThreeHourOfWeek_AR 132 24707022.0463 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_ThreeHourOfWeek_NoAR 68 24707021.9681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_TwelveHourOfWeek_AR 132 24707022.0394 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_TwelveHourOfWeek_NoAR 68 24707022.2665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_TwoHourOfWeek_AR 132 24707021.75 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_TwoHourOfWeek_NoAR 68 24707021.9467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_WeekOfYear_AR 132 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_WeekOfYear_NoAR 68 0.7744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 24707021.1999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 24707022.0174 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_AR 128 24707021.1999 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_AR 116 24707021.8 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 24707022.1485 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_AR 116 24707021.9731 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_NoAR 52 24707021.9731 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 24707021.8177 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_NoAR 56 24707022.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_AR 116 24707022.6497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_DayOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_EightHourOfWeek_AR 116 24707022.6416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_EightHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_FourHourOfWeek_AR 116 24707022.6376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_FourHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_HourOfWeek_AR 116 24707022.6232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_HourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_AR 116 24707022.5941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_SixHourOfWeek_AR 116 24707021.3108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_SixHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_ThreeHourOfWeek_AR 116 24707021.3108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_ThreeHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_TwelveHourOfWeek_AR 116 24707022.6376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_TwelveHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_TwoHourOfWeek_AR 116 24707022.6376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_TwoHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfYear_AR 116 24707021.3327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_WeekOfYear_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_None_AR 120 24707021.3605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_None_NoAR 56 24707021.3605 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_AR 112 24707021.3605 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_NoAR 48 24707021.3605 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_AR 116 24707022.1485 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 24707022.1485 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_AR 116 24707021.9731 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_NoAR 52 24707021.9731 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 24707022.1308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_NoAR 56 24707022.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_DayOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_EightHourOfWeek_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_EightHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_FourHourOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_FourHourOfWeek_NoAR 52 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_HourOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_HourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_SixHourOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_SixHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_ThreeHourOfWeek_AR 116 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_ThreeHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_TwelveHourOfWeek_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_TwelveHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_TwoHourOfWeek_AR 116 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_TwoHourOfWeek_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfYear_AR 116 24707021.8518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_WeekOfYear_NoAR 52 24707022.0423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_None_AR 120 24707021.9062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_None_NoAR 56 24707021.9062 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_AR 112 24707021.9062 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_NoAR 48 24707021.9062 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_AR 100 7.1411 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 5.1961 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_AR 100 0.033 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_NoAR 36 2.2786 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0451 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.7111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_AR 100 8.2004 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_DayOfWeek_NoAR 36 5.912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_EightHourOfWeek_AR 100 5.4008 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_EightHourOfWeek_NoAR 36 5.135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_FourHourOfWeek_AR 100 4.9726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_FourHourOfWeek_NoAR 36 4.244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_HourOfWeek_AR 100 2.9728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_HourOfWeek_NoAR 36 2.4386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_AR 100 0.058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_NoAR 36 2.2809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_SixHourOfWeek_AR 100 5.6314 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_SixHourOfWeek_NoAR 36 4.913 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_ThreeHourOfWeek_AR 100 5.7354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_ThreeHourOfWeek_NoAR 36 4.0215 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_TwelveHourOfWeek_AR 100 5.406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_TwelveHourOfWeek_NoAR 36 4.8918 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_TwoHourOfWeek_AR 100 4.9584 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_TwoHourOfWeek_NoAR 36 3.8507 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_WeekOfYear_AR 100 10.4044 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_WeekOfYear_NoAR 36 6.7985 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.9517 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_AR 96 0.0436 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_AR 132 0.0447 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_DayOfWeek_NoAR 68 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_AR 132 0.0256 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_NoAR 68 0.0244 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0256 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_EightHourOfWeek_AR 132 0.0535 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_EightHourOfWeek_NoAR 68 0.8154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_FourHourOfWeek_AR 132 0.0408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_FourHourOfWeek_NoAR 68 0.6401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_HourOfWeek_AR 132 0.027 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_HourOfWeek_NoAR 68 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_AR 132 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_NoAR 68 0.0246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_SixHourOfWeek_AR 132 0.0429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_SixHourOfWeek_NoAR 68 0.772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_ThreeHourOfWeek_AR 132 0.0373 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_ThreeHourOfWeek_NoAR 68 0.645 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_TwelveHourOfWeek_AR 132 0.0414 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_TwelveHourOfWeek_NoAR 68 0.7796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_TwoHourOfWeek_AR 132 0.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_TwoHourOfWeek_NoAR 68 0.5726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_WeekOfYear_AR 132 0.0441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_WeekOfYear_NoAR 68 0.815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0246 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_AR 128 0.0439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_NoAR 64 0.8149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_AR 116 0.0653 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_NoAR 52 0.6383 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_AR 116 0.0257 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_NoAR 52 0.0231 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0257 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_AR 116 0.0717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_DayOfWeek_NoAR 52 0.6384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_EightHourOfWeek_AR 116 0.0634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_EightHourOfWeek_NoAR 52 0.6576 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_FourHourOfWeek_AR 116 0.0804 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_FourHourOfWeek_NoAR 52 0.544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_HourOfWeek_AR 116 0.0515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_HourOfWeek_NoAR 52 0.0404 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_AR 116 0.0281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_NoAR 52 0.0282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_SixHourOfWeek_AR 116 0.0718 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_SixHourOfWeek_NoAR 52 0.6882 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_ThreeHourOfWeek_AR 116 0.1007 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_ThreeHourOfWeek_NoAR 52 0.6005 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_TwelveHourOfWeek_AR 116 0.0598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_TwelveHourOfWeek_NoAR 52 0.6736 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_TwoHourOfWeek_AR 116 0.0775 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_TwoHourOfWeek_NoAR 52 0.3952 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfYear_AR 116 0.1176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_WeekOfYear_NoAR 52 0.6569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0282 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_AR 112 0.034 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_NoAR 48 0.6387 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_AR 116 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_NoAR 52 0.6526 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_AR 116 0.0308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_NoAR 52 0.1106 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0308 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.1106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_AR 116 0.1925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_DayOfWeek_NoAR 52 0.6425 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_EightHourOfWeek_AR 116 0.1766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_EightHourOfWeek_NoAR 52 0.6433 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_FourHourOfWeek_AR 116 0.1577 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_FourHourOfWeek_NoAR 52 0.5086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_HourOfWeek_AR 116 0.0788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_HourOfWeek_NoAR 52 0.1074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_AR 116 0.0299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_NoAR 52 0.0926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_SixHourOfWeek_AR 116 0.1956 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_SixHourOfWeek_NoAR 52 0.6962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_ThreeHourOfWeek_AR 116 0.2343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_ThreeHourOfWeek_NoAR 52 0.5915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_TwelveHourOfWeek_AR 116 0.1706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_TwelveHourOfWeek_NoAR 52 0.6575 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_TwoHourOfWeek_AR 116 0.1789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_TwoHourOfWeek_NoAR 52 0.3922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_AR 116 0.1587 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_WeekOfYear_NoAR 52 0.6029 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.6215 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_AR 112 0.0361 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 0.6016 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 29.985039472579956 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 69.47310042381287 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-05-12T11:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3988 Min=1.0 Max=10.694835872107333 Mean=6.042416327750525 StdDev=2.801426173695981 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.694835872107333 Mean=6.042416327750525 StdDev=2.801426173695981 @@ -136,29 +776,30 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_Hour_r INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour' [Seasonal_Hour] INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.018 MAPE_Forecast=0.0174 MAPE_Test=0.0121 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.018 SMAPE_Forecast=0.0173 SMAPE_Test=0.0121 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.018 MAPE_Forecast=0.0174 MAPE_Test=0.0122 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.018 SMAPE_Forecast=0.0174 SMAPE_Test=0.0122 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0252 MASE_Forecast=0.0248 MASE_Test=0.0263 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07989849853732847 L1_Forecast=0.07860989368042928 L1_Test=0.06414746537239628 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09982593054682697 L2_Forecast=0.09804344569647 L2_Test=0.08047657715069793 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07974029314534574 L1_Forecast=0.0785321039028717 L1_Test=0.0639597501080064 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10007733848586725 L2_Forecast=0.0981572063280222 L2_Test=0.07985530660970212 INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.042336938156116 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_Hour 0.0030733457294287447 {0: -3.9205093728686653, 1: -2.2918060320399545, 2: -1.4430526395707624, 3: 2.725041627333467, 4: 3.549178383274623, 5: 2.3335367452325135, 6: -3.52690980948847, 7: 4.414688421777947, 8: -2.6743069240623267, 9: -0.19034286059738292, 10: -4.782173619898836, 11: -2.7129595358958056, 12: -2.261812976703247, 13: 4.393168135000041, 14: 0.6471518767607152, 15: 1.8934320834645266, 16: -3.0910050185009927, 17: 0.2357867077965552, 18: -1.8577890525822398, 19: -1.4207121916583185, 20: 3.1450813821518873, 21: 2.719160522848914, 22: 1.4654824832773419, 23: 2.7488170026903918} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END GENERATING_RANDOM_DATASET Signal_4000_H_0_constant_24_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0121 0.0263 -1 None _Signal ... 0.0121 0.0263 -2 None _Signal ... 0.0121 0.0263 -4 None _Signal ... 0.0125 0.0270 -3 None _Signal ... 0.0121 0.0263 - -[5 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0121 0.0263 -1 None _Signal ... 0.0121 0.0263 -2 None _Signal ... 0.0121 0.0263 -3 None _Signal ... 0.0121 0.0263 -4 None _Signal ... 0.0125 0.0270 +0 None _Signal ... 0.0122 0.0263 +1 None _Signal ... 0.0122 0.0263 +2 None _Signal ... 0.0121 0.0261 +3 None _Signal ... 0.0121 0.0261 +4 None _Signal ... 0.0123 0.0262 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_minute.log b/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_minute.log index 2cc395897..55ad144e2 100644 --- a/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_minute.log +++ b/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_minute.log @@ -1,844 +1,973 @@ INFO:pyaf.std:START_TRAINING 'Signal' -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_AR 68 0.0243 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_NoAR 4 0.6149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Minute_AR 68 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Minute_NoAR 4 0.4491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 68 0.0386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 4 0.4823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Minute_residue_AR(64) 68 0.0848 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 4 0.0682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.5026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 100 0.0265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 36 0.6724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Minute_residue_AR(64) 100 0.143 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 36 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0206 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 84 0.0386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 20 0.4822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Minute_residue_AR(64) 84 0.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Minute_residue_NoAR 20 0.0682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.5026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 84 0.0384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 20 0.481 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Minute_residue_AR(64) 84 0.0873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Minute_residue_NoAR 20 0.0672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0168 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0236 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.5014 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 3.1163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 5140.8958 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Minute_residue_AR(64) 100 140.4611 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 36 111.726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 3.3458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 6.8482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.3945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 29.873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 6756.1441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Minute_residue_AR(64) 132 189.263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 68 445.3032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 4.0354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 3.2734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 7.8058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 3.0029 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 5139.7975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Minute_residue_AR(64) 116 143.2381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Minute_residue_NoAR 52 110.5978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 3.3968 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.5631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 6.9989 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.4778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 2.9267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 5134.1633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Minute_residue_AR(64) 116 143.0617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Minute_residue_NoAR 52 105.3154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 3.3982 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 6.443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 6.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 6.6748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Minute_residue_AR(64) 100 24396473.0978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 36 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.7953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Minute_residue_AR(64) 132 24396473.0978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 68 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.7953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Minute_residue_AR(64) 116 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Minute_residue_NoAR 52 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 24396472.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Minute_residue_AR(64) 116 24396472.6293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Minute_residue_NoAR 52 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64) 100 0.6326 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR 36 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Minute_residue_AR(64) 100 1.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 36 0.023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.3937 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Hour_residue_AR(64) 132 0.049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Hour_residue_NoAR 68 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Minute_residue_AR(64) 132 0.172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 68 0.0805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_AR(64) 116 0.0436 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Hour_residue_NoAR 52 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Minute_residue_AR(64) 116 0.0488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Minute_residue_NoAR 52 0.454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64) 116 0.056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Hour_residue_NoAR 52 0.4758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Minute_residue_AR(64) 116 0.1103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Minute_residue_NoAR 52 0.4616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.4758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_AR 68 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Hour_NoAR 4 0.6153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Minute_AR 68 0.1724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Minute_NoAR 4 0.4321 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_AR 72 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_AR 64 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 0.6173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_AR 100 0.0264 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_NoAR 36 0.7842 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Minute_AR 100 0.0223 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Minute_NoAR 36 0.5478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_AR 100 0.0274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Hour_NoAR 36 0.8107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Minute_AR 100 0.1916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Minute_NoAR 36 0.5434 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0207 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0243 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_AR 96 0.0237 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_NoAR 32 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_AR 84 0.0243 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_NoAR 20 0.6149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Minute_AR 84 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Minute_NoAR 20 0.4491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_AR 84 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Hour_NoAR 20 0.6153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Minute_AR 84 0.1723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Minute_NoAR 20 0.432 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_AR 88 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_AR 80 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_NoAR 16 0.6173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_AR 84 0.0243 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_NoAR 20 0.6141 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Minute_AR 84 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Minute_NoAR 20 0.4484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_AR 84 0.0266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Hour_NoAR 20 0.6145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Minute_AR 84 0.1719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Minute_NoAR 20 0.4314 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_AR 80 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_NoAR 16 0.6165 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_AR 100 11.6505 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_NoAR 36 0.7394 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Minute_AR 100 4.4026 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Minute_NoAR 36 0.4381 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 3.2372 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.1991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_AR 100 3.3411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Hour_NoAR 36 4943.4232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Minute_AR 100 136.4718 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Minute_NoAR 36 107.5452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 3.3274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.0881 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_AR 96 6.9983 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_NoAR 32 0.458 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_AR 132 2.0722 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_NoAR 68 0.9411 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Minute_AR 132 1.9648 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Minute_NoAR 68 0.5949 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 4.1867 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_AR 132 31.0933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Hour_NoAR 68 6495.6385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Minute_AR 132 184.2688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Minute_NoAR 68 428.1516 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 3.6397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 3.1158 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_AR 128 7.5123 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_AR 116 13.5408 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_NoAR 52 0.5927 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Minute_AR 116 3.3271 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Minute_NoAR 52 0.9651 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 2.5127 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 0.432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_AR 116 4.9488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Hour_NoAR 52 4942.9548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Minute_AR 116 97.4692 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Minute_NoAR 52 107.0456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 2.5942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 1.0679 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_AR 112 4.4567 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_NoAR 48 0.8655 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_AR 116 23.8006 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_NoAR 52 2.2612 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Minute_AR 116 3.9829 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Minute_NoAR 52 2.9652 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 2.3993 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 2.4343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_AR 116 13.5559 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Hour_NoAR 52 4940.5403 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Minute_AR 116 127.4476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Minute_NoAR 52 104.9606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 2.3594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 2.766 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_AR 112 5.4989 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_NoAR 48 2.8441 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Hour_AR 100 26067285.4233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Hour_AR 100 26067285.2065 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Hour_NoAR 36 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Minute_AR 100 0.7813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Minute_AR 100 26067285.2568 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Minute_NoAR 36 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 26067285.4545 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 26067285.6044 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_NoAR 40 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_AR 96 26067285.1893 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_NoAR 32 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_AR 132 26067285.3823 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_NoAR 68 26067285.4714 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Minute_AR 132 26067285.413 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Minute_NoAR 68 26067285.4141 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 26067285.3964 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 26067285.399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_AR 132 26067285.4607 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Hour_NoAR 68 0.7813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Minute_AR 132 26067285.3817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Minute_NoAR 68 26067285.4406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 26067285.6431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.7813 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_AR 128 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_AR 116 26067284.6895 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_AR 116 26067285.4111 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Hour_NoAR 52 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Minute_AR 116 20979257.8974 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Minute_NoAR 52 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 26067285.4104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Minute_AR 116 26067285.3924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Minute_NoAR 52 26067286.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 26067285.3462 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_NoAR 56 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_AR 112 26067286.0661 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_NoAR 48 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_AR 116 26067285.9919 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_NoAR 52 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Minute_AR 116 26067285.3877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_AR 116 26067285.4016 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Hour_NoAR 52 26067286.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Minute_AR 116 26067285.4283 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Minute_NoAR 52 26067286.1927 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 26067285.3905 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 26067285.4712 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_NoAR 56 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_AR 112 26067285.0189 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_NoAR 48 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_AR 100 6.6562 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_NoAR 36 4.9268 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Minute_AR 100 1.0096 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Minute_NoAR 36 2.6448 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0421 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.6869 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_AR 100 6.6553 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Hour_NoAR 36 4.9266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Minute_AR 100 1.0102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Minute_NoAR 36 2.5887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.9035 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_AR 96 0.0307 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_AR 132 0.0335 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_NoAR 68 0.7832 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Minute_AR 132 0.0277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Minute_NoAR 68 0.5478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_AR 132 0.0365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Hour_NoAR 68 0.7824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Minute_AR 132 0.2217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Minute_NoAR 68 0.5469 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0246 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0243 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_AR 128 0.0308 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_AR 116 0.0373 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_NoAR 52 0.6181 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Minute_AR 116 0.027 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Minute_NoAR 52 0.449 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0246 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_AR 116 0.0363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Hour_NoAR 52 0.6179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Minute_AR 116 0.0471 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Minute_NoAR 52 0.446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_None_AR 120 0.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_None_NoAR 56 0.6171 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_AR 112 0.0292 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_NoAR 48 0.6171 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_AR 116 0.0869 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_NoAR 52 0.6121 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Minute_AR 116 0.0532 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Minute_NoAR 52 0.438 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.0518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_AR 116 0.0865 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Hour_NoAR 52 0.6088 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Minute_AR 116 0.1377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Minute_NoAR 52 0.4458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0315 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.5987 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_AR 112 0.0297 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 0.5966 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 81.75752019882202 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 65.25186109542847 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-23T04:59:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=39988 Min=1.0 Max=10.897580959128534 Mean=6.140492375892452 StdDev=2.7988298627673656 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.897580959128534 Mean=6.140492375892452 StdDev=2.7988298627673656 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0175 MAPE_Test=0.0174 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.0174 SMAPE_Test=0.0173 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0255 MASE_Forecast=0.0252 MASE_Test=0.0366 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08045301833662358 L1_Forecast=0.07946557052934489 L1_Test=0.08811779996857634 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10078936173627877 L2_Forecast=0.09970028244744923 L2_Test=0.10062817922654986 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0175 MAPE_Test=0.0173 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.0174 SMAPE_Test=0.0172 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0255 MASE_Forecast=0.0252 MASE_Test=0.0364 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.0804348467171545 L1_Forecast=0.07948584226519684 L1_Test=0.08767596212085278 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10081207145485091 L2_Forecast=0.09971875121220657 L2_Test=0.10001242074031202 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.140462622354592 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 24 0.02263612957852823 {0: -3.9400060922641456, 1: -2.278158753022309, 2: -1.4362191046096884, 3: 2.72154194832852, 4: 3.555228542486237, 5: 2.3133176380447065, 6: -3.5200555446631014, 7: 4.39450688822107, 8: -2.6936526790187036, 9: -0.18775334547582467, 10: -4.774475484995788, 11: -2.695137639608304, 12: -2.2749431439086734, 13: 4.393595554394465, 14: 0.6423782837336147, 15: 1.8952557869710658, 16: -3.1000666485284656, 17: 0.22660077964108272, 18: -1.8589229456517278, 19: -1.4393011510930362, 20: 3.1373537989988822, 21: 2.722522700949633, 22: 1.472946552209819, 23: 2.7271353537859895} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END GENERATING_RANDOM_DATASET Signal_40000_min_0_constant_24_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0174 0.0366 -1 None _Signal ... 0.0174 0.0366 -2 None _Signal ... 0.0171 0.0366 -16 None _Signal ... 0.0253 0.0488 -6 None CumSum_Signal ... 0.0164 0.0356 - -[5 rows x 20 columns] Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0174 0.0366 -1 None _Signal ... 0.0174 0.0366 -2 None _Signal ... 0.0171 0.0366 -3 None _Signal ... 0.0170 0.0358 -4 None _Signal ... 0.0170 0.0358 +0 None _Signal ... 0.0173 0.0364 +1 None _Signal ... 0.0173 0.0364 +2 None _Signal ... 0.0170 0.0364 +3 None _Signal ... 0.0170 0.0357 +4 None _Signal ... 0.0169 0.0357 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_second.log b/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_second.log index 96d3f3d01..17419f33e 100644 --- a/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_second.log +++ b/tests/references/bugs_issue_70_test_artificial_keep_all_seasonals_second.log @@ -1,652 +1,749 @@ INFO:pyaf.std:START_TRAINING 'Signal' -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice. +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. return _methods._mean(a, axis=axis, dtype=dtype, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:216: RuntimeWarning: Degrees of freedom <= 0 for slice +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:185: RuntimeWarning: invalid value encountered in true_divide +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide arrmean = um.true_divide( -/home/antoine/.local/lib/python3.8/site-packages/numpy/core/_methods.py:209: RuntimeWarning: invalid value encountered in double_scalars +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars ret = ret.dtype.type(ret / rcount) -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Second_AR 68 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Second_NoAR 4 0.4491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Second_residue_AR(64) 68 0.0848 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_Seasonal_Second_residue_NoAR 4 0.0682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.0173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.5026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Second_residue_AR(64) 100 0.143 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_Seasonal_Second_residue_NoAR 36 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0206 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Second_residue_AR(64) 84 0.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_Seasonal_Second_residue_NoAR 20 0.0682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.0173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.5026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Second_residue_AR(64) 84 0.0873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_Seasonal_Second_residue_NoAR 20 0.0672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0168 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0236 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.5014 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Second_residue_AR(64) 100 140.4611 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_Seasonal_Second_residue_NoAR 36 111.726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 3.3458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 6.8482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.3945 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Second_residue_AR(64) 132 189.263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_Seasonal_Second_residue_NoAR 68 445.3032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 4.0354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 3.2734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 7.8058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Second_residue_AR(64) 116 143.2381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_Seasonal_Second_residue_NoAR 52 110.5978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 3.3968 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.5631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 6.9989 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.4778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Second_residue_AR(64) 116 143.0617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_Seasonal_Second_residue_NoAR 52 105.3154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 3.3982 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 6.443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 6.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 6.6748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Second_residue_AR(64) 100 24396473.0978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_Seasonal_Second_residue_NoAR 36 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Second_residue_AR(64) 132 24396473.0978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_Seasonal_Second_residue_NoAR 68 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.7953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Second_residue_AR(64) 116 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_Seasonal_Second_residue_NoAR 52 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Second_residue_AR(64) 116 24396472.6293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_Seasonal_Second_residue_NoAR 52 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 24396473.6267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 24396472.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Second_residue_AR(64) 100 1.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_Seasonal_Second_residue_NoAR 36 0.023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.3937 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Second_residue_AR(64) 132 0.172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_Seasonal_Second_residue_NoAR 68 0.0805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.6557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Second_residue_AR(64) 116 0.0488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_Seasonal_Second_residue_NoAR 52 0.454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Second_residue_AR(64) 116 0.1103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_Seasonal_Second_residue_NoAR 52 0.4616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.4758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Second_AR 68 0.1724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Seasonal_Second_NoAR 4 0.4321 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_AR 72 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_Cycle_NoAR 8 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_AR 64 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_ConstantTrend_NoCycle_NoAR 0 0.6173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Second_AR 100 0.0223 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Second_NoAR 36 0.5478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Second_AR 100 0.1916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Seasonal_Second_NoAR 36 0.5434 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_AR 104 0.0207 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_Cycle_NoAR 40 0.0243 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_AR 96 0.0237 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_Lag1Trend_NoCycle_NoAR 32 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Second_AR 84 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Second_NoAR 20 0.4491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Second_AR 84 0.1723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Seasonal_Second_NoAR 20 0.432 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_AR 88 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_AR 80 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_LinearTrend_NoCycle_NoAR 16 0.6173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Second_AR 84 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Second_NoAR 20 0.4484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Second_AR 84 0.1719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Seasonal_Second_NoAR 20 0.4314 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_AR 88 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_Cycle_NoAR 24 0.0175 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_AR 80 0.0222 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Signal NoTransf_PolyTrend_NoCycle_NoAR 16 0.6165 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Second_AR 100 4.4026 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Second_NoAR 36 0.4381 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 3.2372 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.1991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Second_AR 100 136.4718 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Seasonal_Second_NoAR 36 107.5452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_AR 104 3.3274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_Cycle_NoAR 40 0.0881 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_AR 96 6.9983 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_ConstantTrend_NoCycle_NoAR 32 0.458 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Second_AR 132 1.9648 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Second_NoAR 68 0.5949 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 4.1867 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 0.049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Second_AR 132 184.2688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Seasonal_Second_NoAR 68 428.1516 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_AR 136 3.6397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_Cycle_NoAR 72 3.1158 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_AR 128 7.5123 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Second_AR 116 3.3271 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Second_NoAR 52 0.9651 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 2.5127 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 0.432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Second_AR 116 97.4692 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Seasonal_Second_NoAR 52 107.0456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_AR 120 2.5942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_Cycle_NoAR 56 1.0679 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_AR 112 4.4567 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_LinearTrend_NoCycle_NoAR 48 0.8655 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Second_AR 116 3.9829 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Second_NoAR 52 2.9652 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 2.3993 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 2.4343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Second_AR 116 127.4476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Seasonal_Second_NoAR 52 104.9606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_AR 120 2.3594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_Cycle_NoAR 56 2.766 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_AR 112 5.4989 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Signal Difference_PolyTrend_NoCycle_NoAR 48 2.8441 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Second_AR 100 0.7813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Second_AR 100 26067285.2568 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Seasonal_Second_NoAR 36 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 26067285.4545 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_AR 104 26067285.6044 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_Cycle_NoAR 40 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_AR 96 26067285.1893 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_ConstantTrend_NoCycle_NoAR 32 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Second_AR 132 26067285.413 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Second_NoAR 68 26067285.4141 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 26067285.3964 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 26067285.399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Second_AR 132 26067285.3817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Seasonal_Second_NoAR 68 26067285.4406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_AR 136 26067285.6431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.7813 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_AR 128 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Second_AR 116 20979257.8974 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Second_NoAR 52 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 26067285.4104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Second_AR 116 26067285.3924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Seasonal_Second_NoAR 52 26067286.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_AR 120 26067285.3462 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_Cycle_NoAR 56 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_AR 112 26067286.0661 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_LinearTrend_NoCycle_NoAR 48 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Second_AR 116 26067285.3877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Second_AR 116 26067285.4283 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Seasonal_Second_NoAR 52 26067286.1927 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 26067285.3905 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_AR 120 26067285.4712 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_Cycle_NoAR 56 26067284.6301 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_AR 112 26067285.0189 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Signal RelativeDifference_PolyTrend_NoCycle_NoAR 48 26067284.6301 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Second_AR 100 1.0096 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Second_NoAR 36 2.6448 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0421 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.6869 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Second_AR 100 1.0102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Seasonal_Second_NoAR 36 2.5887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_AR 104 0.0366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_Cycle_NoAR 40 1.9035 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_AR 96 0.0307 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Second_AR 132 0.0277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Second_NoAR 68 0.5478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Second_AR 132 0.2217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Seasonal_Second_NoAR 68 0.5469 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_AR 136 0.0246 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_Cycle_NoAR 72 0.0243 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_AR 128 0.0308 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_Lag1Trend_NoCycle_NoAR 64 0.7848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Second_AR 116 0.027 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Second_NoAR 52 0.449 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_AR 120 0.0246 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_NoAR 56 0.0176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Second_AR 116 0.0471 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Seasonal_Second_NoAR 52 0.446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_None_AR 120 0.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_Cycle_None_NoAR 56 0.6171 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_AR 112 0.0292 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_LinearTrend_NoCycle_NoAR 48 0.6171 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Second_AR 116 0.0532 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Second_NoAR 52 0.438 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.0518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Second_AR 116 0.1377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Seasonal_Second_NoAR 52 0.4458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_AR 120 0.0315 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_Cycle_NoAR 56 0.5987 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_AR 112 0.0297 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Signal Integration_PolyTrend_NoCycle_NoAR 48 0.5966 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 67.02700209617615 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 46.96113967895508 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-01T08:52:59.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=39988 Min=1.0 Max=10.897580959128534 Mean=6.140492375892452 StdDev=2.7988298627673656 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.897580959128534 Mean=6.140492375892452 StdDev=2.7988298627673656 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0175 MAPE_Test=0.0174 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.0174 SMAPE_Test=0.0173 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0255 MASE_Forecast=0.0252 MASE_Test=0.0366 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08045301833662358 L1_Forecast=0.07946557052934489 L1_Test=0.08811779996857634 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10078936173627877 L2_Forecast=0.09970028244744923 L2_Test=0.10062817922654986 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0175 MAPE_Test=0.0173 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.0174 SMAPE_Test=0.0172 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0255 MASE_Forecast=0.0252 MASE_Test=0.0364 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.0804348467171545 L1_Forecast=0.07948584226519684 L1_Test=0.08767596212085278 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10081207145485091 L2_Forecast=0.09971875121220657 L2_Test=0.10001242074031202 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.140462622354592 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 24 0.02263612957852823 {0: -3.9400060922641456, 1: -2.278158753022309, 2: -1.4362191046096884, 3: 2.72154194832852, 4: 3.555228542486237, 5: 2.3133176380447065, 6: -3.5200555446631014, 7: 4.39450688822107, 8: -2.6936526790187036, 9: -0.18775334547582467, 10: -4.774475484995788, 11: -2.695137639608304, 12: -2.2749431439086734, 13: 4.393595554394465, 14: 0.6423782837336147, 15: 1.8952557869710658, 16: -3.1000666485284656, 17: 0.22660077964108272, 18: -1.8589229456517278, 19: -1.4393011510930362, 20: 3.1373537989988822, 21: 2.722522700949633, 22: 1.472946552209819, 23: 2.7271353537859895} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END GENERATING_RANDOM_DATASET Signal_40000_S_0_constant_24_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 - Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0174 0.0366 -1 None _Signal ... 0.0174 0.0366 -2 None _Signal ... 0.0171 0.0366 -16 None _Signal ... 0.0253 0.0488 -6 None CumSum_Signal ... 0.0164 0.0356 - -[5 rows x 20 columns] Split Transformation ... TestMAPE TestMASE -0 None _Signal ... 0.0174 0.0366 -1 None _Signal ... 0.0174 0.0366 -2 None _Signal ... 0.0171 0.0366 -3 None _Signal ... 0.0170 0.0358 -4 None _Signal ... 0.0170 0.0358 +0 None _Signal ... 0.0173 0.0364 +1 None _Signal ... 0.0173 0.0364 +2 None _Signal ... 0.0170 0.0364 +3 None _Signal ... 0.0170 0.0357 +4 None _Signal ... 0.0169 0.0357 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_70_test_ozone_filter_seasonals.log b/tests/references/bugs_issue_70_test_ozone_filter_seasonals.log index bc8f8cf1e..8b18a7c0a 100644 --- a/tests/references/bugs_issue_70_test_ozone_filter_seasonals.log +++ b/tests/references/bugs_issue_70_test_ozone_filter_seasonals.log @@ -1,101 +1,197 @@ INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1988 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1988 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.4619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.9761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 360.8514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 3289621.0774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.1949 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2164 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2164 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.1959 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.2137 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_AR 62 0.2137 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.1595 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1992 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_AR 62 0.1992 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.1657 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.316 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.316 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.2424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 0.362 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.5176 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_AR 110 0.2245 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.4322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.18 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.5882 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_AR 94 0.5882 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.7301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 0.3766 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.4489 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.4489 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.5794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.7377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 0.4357 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 5433.1627 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_AR 78 5433.1627 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 16702.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 13195.3873 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 84073.9442 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 84073.9442 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 58064.6971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 667.4806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 1338.131 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 19806.389 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_AR 94 19806.389 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 168397.0695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 113192.3779 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 1858329.0734 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 1858329.0734 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 16375730.725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 3613815.678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 24525015.6551 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3697 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3697 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.338 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.3178 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.3178 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 0.305 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3238 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_AR 94 0.4118 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 0.4065 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3193 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_AR 94 0.3625 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 12.569859743118286 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 13.207495927810669 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -107,20 +203,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013038 L1_Test=0.42992050508902374 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507155 L2_Test=0.5519432695595545 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033716 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533314 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225448 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389378 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.1409304557420878 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046362 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481855 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102227 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045818 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947771 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END Month Ozone Time 0 1955-01 2.7 1955-01-01 @@ -129,15 +234,10 @@ INFO:pyaf.std:AR_MODEL_DETAIL_END 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.174 0.9094 -1 None _Ozone ... 0.343 1.6728 - -[2 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.1740 0.9094 -1 None _Ozone ... 0.3430 1.6728 -2 None _Ozone ... 0.2567 1.2245 -3 None _Ozone ... 0.2567 1.2245 -4 None Diff_Ozone ... 0.2262 1.0525 +1 None _Ozone ... 0.1740 0.9094 +2 None _Ozone ... 0.3430 1.6728 +3 None _Ozone ... 0.3430 1.6728 +4 None _Ozone ... 0.2209 1.0918 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_70_test_ozone_keep_all_seasonals.log b/tests/references/bugs_issue_70_test_ozone_keep_all_seasonals.log index be5f8a2b5..f7378d5ed 100644 --- a/tests/references/bugs_issue_70_test_ozone_keep_all_seasonals.log +++ b/tests/references/bugs_issue_70_test_ozone_keep_all_seasonals.log @@ -1,101 +1,197 @@ INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1988 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1988 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.4619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.9761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 360.8514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 3289621.0774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.1949 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2164 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2164 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.1959 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.2137 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_AR 62 0.2137 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.1595 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1992 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_AR 62 0.1992 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.1657 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.316 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.316 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.2424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 0.362 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.5176 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_AR 110 0.2245 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.4322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.18 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.5882 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_AR 94 0.5882 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.7301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 0.3766 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.4489 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.4489 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.5794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.7377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 0.4357 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 5433.1627 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_AR 78 5433.1627 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 16702.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 13195.3873 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 84073.9442 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 84073.9442 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 58064.6971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 667.4806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 1338.131 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 19806.389 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_AR 94 19806.389 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 168397.0695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 113192.3779 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 1858329.0734 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 1858329.0734 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 16375730.725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 3613815.678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 24525015.6551 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3697 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3697 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.338 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.3178 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.3178 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 0.305 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3238 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_AR 94 0.4118 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 0.4065 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3193 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_AR 94 0.3625 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 15.454696655273438 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 13.705569982528687 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -107,20 +203,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013038 L1_Test=0.42992050508902374 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507155 L2_Test=0.5519432695595545 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033716 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533314 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225448 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389378 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.1409304557420878 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046362 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481855 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102227 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045818 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947771 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END Month Ozone Time 0 1955-01 2.7 1955-01-01 @@ -129,15 +234,10 @@ INFO:pyaf.std:AR_MODEL_DETAIL_END 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.174 0.9094 -1 None _Ozone ... 0.343 1.6728 - -[2 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.1740 0.9094 -1 None _Ozone ... 0.3430 1.6728 -2 None _Ozone ... 0.2567 1.2245 -3 None _Ozone ... 0.2567 1.2245 -4 None Diff_Ozone ... 0.2262 1.0525 +1 None _Ozone ... 0.1740 0.9094 +2 None _Ozone ... 0.3430 1.6728 +3 None _Ozone ... 0.3430 1.6728 +4 None _Ozone ... 0.2209 1.0918 [5 rows x 20 columns] diff --git a/tests/references/bugs_issue_72_test_artificial_32_exp_linear_0__100.log b/tests/references/bugs_issue_72_test_artificial_32_exp_linear_0__100.log index e65e7e917..a4c44ea62 100644 --- a/tests/references/bugs_issue_72_test_artificial_32_exp_linear_0__100.log +++ b/tests/references/bugs_issue_72_test_artificial_32_exp_linear_0__100.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_32_D_0_linear_0_exp_0.0_100 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 5.631014823913574 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 4.994814395904541 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-24T00:00:00.000000 TimeDelta= Horizon=1 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=31 Min=0.3485473933773712 Max=0.38263933949626505 Mean=0.36599495856843456 StdDev=0.007368735349292028 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=0.3485473933773712 Max=0.38263933949626505 Mean=0.36599495856843456 StdDev=0.007368735349292028 @@ -16,13 +16,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5539 MASE_Forecast=0.8397 MASE_Test=None INFO:pyaf.std:MODEL_L1 L1_Fit=0.005453511692656512 L1_Forecast=0.006906249278916023 L1_Test=0.0006340499581108383 INFO:pyaf.std:MODEL_L2 L2_Fit=0.007192214897913679 L2_Forecast=0.008883347268888694 L2_Test=0.0006340499581108383 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.36497721256474436 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_0.01_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.0810694694519043 -Forecast Columns Index(['Date', 'GeneratedTrend', 'GeneratedCycle', 'GeneratedAR', 'Noise', - 'Signal', 'Signal_32_D_0_linear_0_exp_0.0_100', 'orig_Signal', - 'Signal_0.01', '_Signal_0.01', 'row_number', 'Date_Normalized', +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.025418519973754883 +Forecast Columns Index(['Date', 'Signal_0.01', '_Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', '_Signal_0.01_ConstantTrend_residue_zeroCycle', '_Signal_0.01_ConstantTrend_residue_zeroCycle_residue', @@ -54,31 +61,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 1, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-01-31 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 1, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-01-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31 }, - "Training_Signal_Length": 31 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.006906249278916023", - "MAPE": "0.0184", - "MASE": "0.8397", - "RMSE": "0.008883347268888694" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.006906249278916023", + "MAPE": "0.0184", + "MASE": "0.8397", + "RMSE": "0.008883347268888694" + } } } diff --git a/tests/references/bugs_issue_72_test_artificial_32_sqrt_poly_7__20.log b/tests/references/bugs_issue_72_test_artificial_32_sqrt_poly_7__20.log index 4ef5034ff..e914d11d1 100644 --- a/tests/references/bugs_issue_72_test_artificial_32_sqrt_poly_7__20.log +++ b/tests/references/bugs_issue_72_test_artificial_32_sqrt_poly_7__20.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_32_D_0_poly_7_sqrt_0.0_20 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 4.583336114883423 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 3.681199789047241 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-24T00:00:00.000000 TimeDelta= Horizon=1 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=31 Min=0.9896998116695764 Max=2.8701619600408215 Mean=1.9928884994745 StdDev=0.5820934496096675 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=0.9896998116695764 Max=2.8701619600408215 Mean=1.9928884994745 StdDev=0.5820934496096675 @@ -14,21 +14,28 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1149 MAPE_Forecast=0.0577 MAPE_Test=0.0354 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1006 SMAPE_Forecast=0.0587 SMAPE_Test=0.0348 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2187 MASE_Forecast=0.1345 MASE_Test=None INFO:pyaf.std:MODEL_L1 L1_Fit=0.18310862953939636 L1_Forecast=0.1136317903327202 L1_Test=0.069535809444365 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.2751124254085912 L2_Forecast=0.13294890312434357 L2_Test=0.069535809444365 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.2751124254085912 L2_Forecast=0.1329489031243436 L2_Test=0.069535809444365 INFO:pyaf.std:MODEL_COMPLEXITY 6 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.9798363634420466 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_0.01_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag7 0.5345375617171175 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.3427825397765596 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.24910288924719498 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.1630385146694386 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag1 0.07446098820165097 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag3 0.06597447204199061 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag7 0.5345375617171176 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.34278253977655937 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.2491028892471947 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.16303851466943808 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag1 0.0744609882016513 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_0.01_ConstantTrend_residue_zeroCycle_residue_Lag3 0.06597447204199092 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.11760377883911133 -Forecast Columns Index(['Date', 'GeneratedTrend', 'GeneratedCycle', 'GeneratedAR', 'Noise', - 'Signal', 'Signal_32_D_0_poly_7_sqrt_0.0_20', 'orig_Signal', - 'Signal_0.01', '_Signal_0.01', 'row_number', 'Date_Normalized', +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.050615549087524414 +Forecast Columns Index(['Date', 'Signal_0.01', '_Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', '_Signal_0.01_ConstantTrend_residue_zeroCycle', '_Signal_0.01_ConstantTrend_residue_zeroCycle_residue', @@ -54,37 +61,39 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 1.4 KB Forecasts - [[Timestamp('2000-02-01 00:00:00') nan 2.4259745141774536 - 2.16539466405374 2.686554364301167]] + [[Timestamp('2000-02-01 00:00:00') nan 2.425974514177454 + 2.1653946640537405 2.6865543643011676]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 1, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-01-31 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 1, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-01-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31 }, - "Training_Signal_Length": 31 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_zeroCycle_residue_AR(7)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "6", - "MAE": "0.1136317903327202", - "MAPE": "0.0577", - "MASE": "0.1345", - "RMSE": "0.13294890312434357" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_zeroCycle_residue_AR(7)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "6", + "MAE": "0.1136317903327202", + "MAPE": "0.0577", + "MASE": "0.1345", + "RMSE": "0.1329489031243436" + } } } diff --git a/tests/references/bugs_issue_73_issue_73_1_fast_mode.log b/tests/references/bugs_issue_73_issue_73_1_fast_mode.log index f45cb7c43..3f35b3df0 100644 --- a/tests/references/bugs_issue_73_issue_73_1_fast_mode.log +++ b/tests/references/bugs_issue_73_issue_73_1_fast_mode.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'signal' 10.349132061004639 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['signal']' 10.27983546257019 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1.0 TimeMax=10.5089285714286 TimeDelta=0.002976190476190485 Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='signal' Length=4032 Min=18640 Max=38777 Mean=29617.136160714286 StdDev=5566.669346535028 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_signal' Min=18640 Max=38777 Mean=29617.136160714286 StdDev=5566.669346535028 @@ -11,18 +11,27 @@ INFO:pyaf.std:AUTOREG_DETAIL '_signal_ConstantTrend_residue_zeroCycle_residue_AR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0062 MAPE_Forecast=0.0059 MAPE_Test=0.007 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0062 SMAPE_Forecast=0.0059 SMAPE_Test=0.007 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.274 MASE_Forecast=0.2618 MASE_Test=0.3054 -INFO:pyaf.std:MODEL_L1 L1_Fit=178.82967540056677 L1_Forecast=168.91272417381532 L1_Test=181.83942768239447 -INFO:pyaf.std:MODEL_L2 L2_Fit=248.80653028475902 L2_Forecast=235.9142553120004 L2_Test=260.8745401099059 +INFO:pyaf.std:MODEL_L1 L1_Fit=178.82967540056677 L1_Forecast=168.9127241738158 L1_Test=181.83942768239135 +INFO:pyaf.std:MODEL_L2 L2_Fit=248.80653028475905 L2_Forecast=235.91425531200113 L2_Test=260.87454010990564 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 29641.51533166458 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _signal_ConstantTrend_residue_zeroCycle_residue_Lag1 1.6827491457525248 -INFO:pyaf.std:AR_MODEL_COEFF 2 _signal_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.9158762194946228 -INFO:pyaf.std:AR_MODEL_COEFF 3 _signal_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.5095627354222947 -INFO:pyaf.std:AR_MODEL_COEFF 4 _signal_ConstantTrend_residue_zeroCycle_residue_Lag48 0.4382282897310544 -INFO:pyaf.std:AR_MODEL_COEFF 5 _signal_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.40622174175367176 -INFO:pyaf.std:AR_MODEL_COEFF 6 _signal_ConstantTrend_residue_zeroCycle_residue_Lag4 0.27008314559648866 -INFO:pyaf.std:AR_MODEL_COEFF 7 _signal_ConstantTrend_residue_zeroCycle_residue_Lag51 0.23470266066913256 -INFO:pyaf.std:AR_MODEL_COEFF 8 _signal_ConstantTrend_residue_zeroCycle_residue_Lag50 0.21116475684183 -INFO:pyaf.std:AR_MODEL_COEFF 9 _signal_ConstantTrend_residue_zeroCycle_residue_Lag46 0.148150473567102 -INFO:pyaf.std:AR_MODEL_COEFF 10 _signal_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.11629849077292626 +INFO:pyaf.std:AR_MODEL_COEFF 1 _signal_ConstantTrend_residue_zeroCycle_residue_Lag1 1.6827491457525394 +INFO:pyaf.std:AR_MODEL_COEFF 2 _signal_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.9158762194946254 +INFO:pyaf.std:AR_MODEL_COEFF 3 _signal_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.5095627354223132 +INFO:pyaf.std:AR_MODEL_COEFF 4 _signal_ConstantTrend_residue_zeroCycle_residue_Lag48 0.438228289731061 +INFO:pyaf.std:AR_MODEL_COEFF 5 _signal_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.40622174175366954 +INFO:pyaf.std:AR_MODEL_COEFF 6 _signal_ConstantTrend_residue_zeroCycle_residue_Lag4 0.27008314559648827 +INFO:pyaf.std:AR_MODEL_COEFF 7 _signal_ConstantTrend_residue_zeroCycle_residue_Lag51 0.23470266066913714 +INFO:pyaf.std:AR_MODEL_COEFF 8 _signal_ConstantTrend_residue_zeroCycle_residue_Lag50 0.2111647568418291 +INFO:pyaf.std:AR_MODEL_COEFF 9 _signal_ConstantTrend_residue_zeroCycle_residue_Lag46 0.14815047356711777 +INFO:pyaf.std:AR_MODEL_COEFF 10 _signal_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.11629849077292367 INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/bugs_issue_73_issue_73_1_fast_mode_2.log b/tests/references/bugs_issue_73_issue_73_1_fast_mode_2.log index ab1ed98ca..34f13cdd0 100644 --- a/tests/references/bugs_issue_73_issue_73_1_fast_mode_2.log +++ b/tests/references/bugs_issue_73_issue_73_1_fast_mode_2.log @@ -1,18 +1,37 @@ INFO:pyaf.std:START_TRAINING 'signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'signal' 17.019779920578003 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['signal']' 21.023800373077393 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1.0 TimeMax=10.5089285714286 TimeDelta=0.002976190476190485 Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='signal' Length=4032 Min=18640 Max=38777 Mean=29617.136160714286 StdDev=5566.669346535028 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_signal' Min=18640 Max=38777 Mean=29617.136160714286 StdDev=5566.669346535028 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_signal_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [Lag1Trend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_signal_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_signal_Lag1Trend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_signal_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.004 MAPE_Forecast=0.0073 MAPE_Test=0.0094 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.004 SMAPE_Forecast=0.0073 SMAPE_Test=0.0094 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1767 MASE_Forecast=0.3347 MASE_Test=0.4187 -INFO:pyaf.std:MODEL_L1 L1_Fit=115.34164233069116 L1_Forecast=215.96366666666663 L1_Test=249.32716049382745 -INFO:pyaf.std:MODEL_L2 L2_Fit=172.35780156763812 L2_Forecast=361.8300040155104 L2_Test=409.23578699239073 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:BEST_DECOMPOSITION '_signal_ConstantTrend_residue_zeroCycle_residue_AR(64)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_signal_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_signal_ConstantTrend_residue_zeroCycle_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0062 MAPE_Forecast=0.0059 MAPE_Test=0.007 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0062 SMAPE_Forecast=0.0059 SMAPE_Test=0.007 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.274 MASE_Forecast=0.2618 MASE_Test=0.3054 +INFO:pyaf.std:MODEL_L1 L1_Fit=178.82967540056677 L1_Forecast=168.9127241738158 L1_Test=181.83942768239135 +INFO:pyaf.std:MODEL_L2 L2_Fit=248.80653028475905 L2_Forecast=235.91425531200113 L2_Test=260.87454010990564 +INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 29641.51533166458 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _signal_ConstantTrend_residue_zeroCycle_residue_Lag1 1.6827491457525394 +INFO:pyaf.std:AR_MODEL_COEFF 2 _signal_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.9158762194946254 +INFO:pyaf.std:AR_MODEL_COEFF 3 _signal_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.5095627354223132 +INFO:pyaf.std:AR_MODEL_COEFF 4 _signal_ConstantTrend_residue_zeroCycle_residue_Lag48 0.438228289731061 +INFO:pyaf.std:AR_MODEL_COEFF 5 _signal_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.40622174175366954 +INFO:pyaf.std:AR_MODEL_COEFF 6 _signal_ConstantTrend_residue_zeroCycle_residue_Lag4 0.27008314559648827 +INFO:pyaf.std:AR_MODEL_COEFF 7 _signal_ConstantTrend_residue_zeroCycle_residue_Lag51 0.23470266066913714 +INFO:pyaf.std:AR_MODEL_COEFF 8 _signal_ConstantTrend_residue_zeroCycle_residue_Lag50 0.2111647568418291 +INFO:pyaf.std:AR_MODEL_COEFF 9 _signal_ConstantTrend_residue_zeroCycle_residue_Lag46 0.14815047356711777 +INFO:pyaf.std:AR_MODEL_COEFF 10 _signal_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.11629849077292367 INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/bugs_issue_75_issue_75_plot.log b/tests/references/bugs_issue_75_issue_75_plot.log index 91766430f..eceb8ddd8 100644 --- a/tests/references/bugs_issue_75_issue_75_plot.log +++ b/tests/references/bugs_issue_75_issue_75_plot.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 5.785415410995483 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.781870365142822 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,39 +17,48 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013038 L1_Test=0.42992050508902374 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507155 L2_Test=0.5519432695595545 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033716 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533314 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225448 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389378 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.1409304557420878 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046362 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481855 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102227 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045818 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947771 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 29.991007328033447 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 16.348297119140625 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1595 0.1740 -1 None _Ozone ... 0.1657 0.3430 -2 None _Ozone ... 0.1796 0.2567 -3 None _Ozone ... 0.1796 0.2567 -4 None Diff_Ozone ... 0.1800 0.2262 +1 None _Ozone ... 0.1595 0.1740 +2 None _Ozone ... 0.1657 0.3430 +3 None _Ozone ... 0.1657 0.3430 +4 None _Ozone ... 0.1765 0.2209 [5 rows x 8 columns] OK diff --git a/tests/references/bugs_issue_75_issue_75_residues_Are_empty.log b/tests/references/bugs_issue_75_issue_75_residues_Are_empty.log index e1e5e248a..af4f09f3d 100644 --- a/tests/references/bugs_issue_75_issue_75_residues_Are_empty.log +++ b/tests/references/bugs_issue_75_issue_75_residues_Are_empty.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 5.007667064666748 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 6.0173256397247314 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,29 +17,38 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013038 L1_Test=0.42992050508902374 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507155 L2_Test=0.5519432695595545 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033716 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533314 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225448 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389378 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.1409304557420878 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046362 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481855 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102227 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045818 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947771 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.9162662029266357 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.0423336029052734 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1595 0.1740 -1 None _Ozone ... 0.1657 0.3430 -2 None _Ozone ... 0.1796 0.2567 -3 None _Ozone ... 0.1796 0.2567 -4 None Diff_Ozone ... 0.1800 0.2262 +1 None _Ozone ... 0.1595 0.1740 +2 None _Ozone ... 0.1657 0.3430 +3 None _Ozone ... 0.1657 0.3430 +4 None _Ozone ... 0.1765 0.2209 [5 rows x 8 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -83,31 +92,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013038", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507155" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/bugs_issue_76_test_ozone_unicode.log b/tests/references/bugs_issue_76_test_ozone_unicode.log index 71b6ecabe..48317350f 100644 --- a/tests/references/bugs_issue_76_test_ozone_unicode.log +++ b/tests/references/bugs_issue_76_test_ozone_unicode.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING '臭氧' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '臭氧' 9.597908973693848 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['臭氧']' 5.748170852661133 INFO:pyaf.std:TIME_DETAIL TimeVariable='月' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='臭氧' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_臭氧' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,31 +17,40 @@ INFO:pyaf.std:AUTOREG_DETAIL '_臭氧_LinearTrend_residue_zeroCycle_residue_AR(5 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013038 L1_Test=0.42992050508902374 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507155 L2_Test=0.5519432695595545 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _臭氧_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033716 -INFO:pyaf.std:AR_MODEL_COEFF 2 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533314 -INFO:pyaf.std:AR_MODEL_COEFF 3 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225448 -INFO:pyaf.std:AR_MODEL_COEFF 4 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389378 -INFO:pyaf.std:AR_MODEL_COEFF 5 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag12 0.1409304557420878 -INFO:pyaf.std:AR_MODEL_COEFF 6 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046362 -INFO:pyaf.std:AR_MODEL_COEFF 7 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481855 -INFO:pyaf.std:AR_MODEL_COEFF 8 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102227 -INFO:pyaf.std:AR_MODEL_COEFF 9 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045818 -INFO:pyaf.std:AR_MODEL_COEFF 10 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947771 +INFO:pyaf.std:AR_MODEL_COEFF 1 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _臭氧_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager /home/antoine/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:211: RuntimeWarning: Glyph 26376 missing from current font. font.set_text(s, 0.0, flags=flags) @@ -115,15 +124,15 @@ INFO:matplotlib.font_manager:generated new fontManager font.set_text(s, 0, flags=flags) /home/antoine/.local/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:180: RuntimeWarning: Glyph 27687 missing from current font. font.set_text(s, 0, flags=flags) -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 26.42086672782898 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.886199951171875 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 17.016093492507935 +INFO:pyaf.std:START_FORECASTING '['臭氧']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['臭氧']' 0.8674359321594238 Split Transformation ... ForecastMAPE TestMAPE 0 None _臭氧 ... 0.1595 0.1740 -1 None _臭氧 ... 0.1657 0.3430 -2 None _臭氧 ... 0.1796 0.2567 -3 None _臭氧 ... 0.1796 0.2567 -4 None Diff_臭氧 ... 0.1800 0.2262 +1 None _臭氧 ... 0.1595 0.1740 +2 None _臭氧 ... 0.1657 0.3430 +3 None _臭氧 ... 0.1657 0.3430 +4 None _臭氧 ... 0.1765 0.2209 [5 rows x 8 columns] Forecast Columns Index(['月', '臭氧', 'row_number', '月_Normalized', '_臭氧', '_臭氧_LinearTrend', @@ -192,31 +201,33 @@ Forecasts { - "Dataset": { - "Signal": "\u81ed\u6c27", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "\u6708" + "\u81ed\u6c27": { + "Dataset": { + "Signal": "\u81ed\u6c27", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "\u6708" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_\u81ed\u6c27_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_\u81ed\u6c27_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013038", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507155" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/bugs_issue_79_test_ozone_missing_signal.log b/tests/references/bugs_issue_79_test_ozone_missing_signal.log index 49aa7b44e..ec392f9ef 100644 --- a/tests/references/bugs_issue_79_test_ozone_missing_signal.log +++ b/tests/references/bugs_issue_79_test_ozone_missing_signal.log @@ -1,49 +1,58 @@ INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 5.356258392333984 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.00237774848938 INFO:pyaf.std:TIME_DETAIL TimeVariable='Month' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1968-07-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=216 Min=1.2 Max=7.7 Mean=3.746296296296296 StdDev=1.3542978727193387 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_Ozone' Min=-2.7 Max=3.5 Mean=0.008333333333333321 StdDev=0.9641390165374891 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_Ozone_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.221 MAPE_Forecast=0.1862 MAPE_Test=0.25 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2194 SMAPE_Forecast=0.174 SMAPE_Test=0.3197 -INFO:pyaf.std:MODEL_MASE MASE_Fit=1.0337 MASE_Forecast=0.7306 MASE_Test=2.8479 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.813588391737611 L1_Forecast=0.5077962850329415 L1_Test=0.8025913439451932 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.0541828632882473 L2_Forecast=0.5791630897663923 L2_Test=1.1031214313356847 -INFO:pyaf.std:MODEL_COMPLEXITY 52 +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [ConstantTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL 'Diff_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2294 MAPE_Forecast=0.1738 MAPE_Test=0.1713 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2732 SMAPE_Forecast=0.1768 SMAPE_Test=0.2132 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.2823 MASE_Forecast=0.7256 MASE_Test=2.1512 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.0092024539877318 L1_Forecast=0.5042682926829273 L1_Test=0.6062500000000022 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.3163246041653325 L2_Forecast=0.6099905037245641 L2_Test=0.9284002818468658 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 2.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.017791411042944787 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear 0.0822085889570553 {1: -0.06779141104294473, 2: 0.3322085889570552, 3: 0.18220858895705538, 4: 0.5822085889570553, 5: 0.18220858895705494, 6: 0.6822085889570554, 7: 0.532208588957055, 8: -0.21779141104294497, 9: -0.3177914110429455, 10: -0.29279141104294426, 11: -1.4177914110429446, 12: -0.4177914110429447} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.77530264854431 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.3101224899291992 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 18.435232400894165 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.34194350242614746 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_Ozone ... 0.1862 0.2500 -1 None Diff_Ozone ... 0.1862 0.2500 -2 None _Ozone ... 0.2040 0.1890 -3 None _Ozone ... 0.2040 0.1890 +0 None Diff_Ozone ... 0.1738 0.1713 +1 None _Ozone ... 0.1927 0.2019 +2 None _Ozone ... 0.1974 0.1492 +3 None _Ozone ... 0.2083 0.1731 4 None _Ozone ... 0.2083 0.1731 [5 rows x 8 columns] Forecast Columns Index(['Month', 'Ozone', 'row_number', 'Month_Normalized', 'Diff_Ozone', - 'Diff_Ozone_LinearTrend', 'Diff_Ozone_LinearTrend_residue', - 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', - 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', - 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', - 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + 'Diff_Ozone_ConstantTrend', 'Diff_Ozone_ConstantTrend_residue', + 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear', + 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue', + 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', 'Diff_Ozone_Trend', 'Diff_Ozone_Trend_residue', 'Diff_Ozone_Cycle', 'Diff_Ozone_Cycle_residue', 'Diff_Ozone_AR', 'Diff_Ozone_AR_residue', 'Diff_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -63,47 +72,49 @@ memory usage: 5.5 KB None Forecasts Month Ozone Ozone_Forecast -216 1973-01-01 NaN 0.738698 -217 1973-02-01 NaN 1.010435 -218 1973-03-01 NaN 1.596460 -219 1973-04-01 NaN 2.108675 -220 1973-05-01 NaN 2.206605 -221 1973-06-01 NaN 3.061678 -222 1973-07-01 NaN 3.538179 -223 1973-08-01 NaN 3.421589 -224 1973-09-01 NaN 3.003076 -225 1973-10-01 NaN 2.846102 -226 1973-11-01 NaN 1.492974 -227 1973-12-01 NaN 0.824462 +216 1973-01-01 NaN 1.500 +217 1973-02-01 NaN 1.850 +218 1973-03-01 NaN 2.050 +219 1973-04-01 NaN 2.650 +220 1973-05-01 NaN 2.850 +221 1973-06-01 NaN 3.550 +222 1973-07-01 NaN 4.100 +223 1973-08-01 NaN 3.900 +224 1973-09-01 NaN 3.600 +225 1973-10-01 NaN 3.325 +226 1973-11-01 NaN 1.925 +227 1973-12-01 NaN 1.525 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1972-12-01 00:00:00" - ], - "TimeVariable": "Month" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1972-12-01 00:00:00" + ], + "TimeVariable": "Month" + }, + "Training_Signal_Length": 216 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "Difference", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 216 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "Difference", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "52", - "MAE": "0.5077962850329415", - "MAPE": "0.1862", - "MASE": "0.7306", - "RMSE": "0.5791630897663923" + "Model_Performance": { + "COMPLEXITY": "36", + "MAE": "0.5042682926829273", + "MAPE": "0.1738", + "MASE": "0.7256", + "RMSE": "0.6099905037245641" + } } } @@ -112,7 +123,7 @@ Forecasts -{"Month":{"204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z","216":"1973-01-01T00:00:00.000Z","217":"1973-02-01T00:00:00.000Z","218":"1973-03-01T00:00:00.000Z","219":"1973-04-01T00:00:00.000Z","220":"1973-05-01T00:00:00.000Z","221":"1973-06-01T00:00:00.000Z","222":"1973-07-01T00:00:00.000Z","223":"1973-08-01T00:00:00.000Z","224":"1973-09-01T00:00:00.000Z","225":"1973-10-01T00:00:00.000Z","226":"1973-11-01T00:00:00.000Z","227":"1973-12-01T00:00:00.000Z"},"Ozone":{"204":1.5,"205":2.0,"206":3.1,"207":3.0,"208":3.5,"209":3.4,"210":4.0,"211":3.8,"212":3.8,"213":3.8,"214":3.8,"215":3.8,"216":null,"217":null,"218":null,"219":null,"220":null,"221":null,"222":null,"223":null,"224":null,"225":null,"226":null,"227":null},"Ozone_Forecast":{"204":1.0590446516,"205":1.3334516326,"206":1.9221391174,"207":2.4370170783,"208":2.5376093249,"209":3.3953444287,"210":3.8745081039,"211":3.7605811152,"212":3.3447310496,"213":3.1904194455,"214":1.8399539953,"215":1.1741039296,"216":0.7386981589,"217":1.0104352454,"218":1.5964601304,"219":2.1086754915,"220":2.2066051384,"221":3.0616776424,"222":3.5381787179,"223":3.4215891294,"224":3.003076464,"225":2.8461022602,"226":1.4929742101,"227":0.8244615448}} +{"Month":{"204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z","216":"1973-01-01T00:00:00.000Z","217":"1973-02-01T00:00:00.000Z","218":"1973-03-01T00:00:00.000Z","219":"1973-04-01T00:00:00.000Z","220":"1973-05-01T00:00:00.000Z","221":"1973-06-01T00:00:00.000Z","222":"1973-07-01T00:00:00.000Z","223":"1973-08-01T00:00:00.000Z","224":"1973-09-01T00:00:00.000Z","225":"1973-10-01T00:00:00.000Z","226":"1973-11-01T00:00:00.000Z","227":"1973-12-01T00:00:00.000Z"},"Ozone":{"204":1.5,"205":2.0,"206":3.1,"207":3.0,"208":3.5,"209":3.4,"210":4.0,"211":3.8,"212":3.8,"213":3.8,"214":3.8,"215":3.8,"216":null,"217":null,"218":null,"219":null,"220":null,"221":null,"222":null,"223":null,"224":null,"225":null,"226":null,"227":null},"Ozone_Forecast":{"204":1.525,"205":1.875,"206":2.075,"207":2.675,"208":2.875,"209":3.575,"210":4.125,"211":3.925,"212":3.625,"213":3.35,"214":1.95,"215":1.55,"216":1.5,"217":1.85,"218":2.05,"219":2.65,"220":2.85,"221":3.55,"222":4.1,"223":3.9,"224":3.6,"225":3.325,"226":1.925,"227":1.525}} diff --git a/tests/references/bugs_issue_80_test_ozone_missing_time.log b/tests/references/bugs_issue_80_test_ozone_missing_time.log index 88709b993..21847f6b4 100644 --- a/tests/references/bugs_issue_80_test_ozone_missing_time.log +++ b/tests/references/bugs_issue_80_test_ozone_missing_time.log @@ -1,59 +1,68 @@ INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 5.541418552398682 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 14.859541893005371 INFO:pyaf.std:TIME_DETAIL TimeVariable='Month' TimeMin=1955-02-01T00:00:00.000000 TimeMax=1968-07-01T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=216 Min=1.2 Max=8.7 Mean=3.7766203703703702 StdDev=1.484338661926163 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.7766203703703702 StdDev=1.484338661926163 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=216 Min=1.2 Max=8.7 Mean=3.772685185185185 StdDev=1.4881769275050256 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.772685185185185 StdDev=1.4881769275050256 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(54)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(54)' [LinearTrend + Seasonal_DayOfMonth + AR] INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(54)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1598 MAPE_Forecast=0.186 MAPE_Test=0.1313 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1517 SMAPE_Forecast=0.2017 SMAPE_Test=0.145 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6779 MASE_Forecast=0.752 MASE_Test=0.6078 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.5942403498354327 L1_Forecast=0.5226228629154416 L1_Test=0.30944145403651185 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.7919292427045692 L2_Forecast=0.6336082944222472 L2_Test=0.376262868192761 -INFO:pyaf.std:MODEL_COMPLEXITY 56 +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth' [Seasonal_DayOfMonth] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(54)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.171 MAPE_Forecast=0.1664 MAPE_Test=0.141 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1627 SMAPE_Forecast=0.1819 SMAPE_Test=0.159 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7118 MASE_Forecast=0.6731 MASE_Test=0.6439 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6239562593338415 L1_Forecast=0.4677835668058019 L1_Test=0.3277876704917479 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8086648233754375 L2_Forecast=0.5894736747213507 L2_Test=0.3912965475880301 +INFO:pyaf.std:MODEL_COMPLEXITY 60 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.988095612787992, array([-1.87140309])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_DayOfMonth 0.011624442501209309 {1: -0.026060867961259948, 31: -0.8700614245878462, 2: 1.2046407016614031} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.40681860680131854 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19188476939500132 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.18257876105732074 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.16053140695540347 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.1432449084584471 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.14290544703187524 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.13895098904035436 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag52 -0.12920108603376418 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.1280401125536796 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.11867614926141734 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag1 0.3502732457842136 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag10 0.16965848170117298 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag30 0.16075347077023477 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag14 -0.15111456562741163 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag7 -0.14595357782118615 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag12 0.14353872934108375 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag32 -0.14226945604011282 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag39 -0.1384941599576876 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag20 -0.13425229855597118 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_Lag36 0.12788096675091423 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.544835567474365 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.449143648147583 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 17.876869916915894 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.9931764602661133 Split Transformation ... ForecastMAPE TestMAPE -0 None _Ozone ... 0.1860 0.1313 -1 None Diff_Ozone ... 0.1894 0.1383 -2 None _Ozone ... 0.1991 0.4539 -3 None _Ozone ... 0.2005 0.2544 -4 None _Ozone ... 0.2035 0.1894 +0 None _Ozone ... 0.1664 0.1410 +1 None Diff_Ozone ... 0.1768 0.4176 +2 None Diff_Ozone ... 0.1777 0.5038 +3 None _Ozone ... 0.1806 0.1324 +4 None _Ozone ... 0.1826 0.1348 [5 rows x 8 columns] Forecast Columns Index(['Month', 'Ozone', 'row_number', 'Month_Normalized', '_Ozone', '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', - '_Ozone_LinearTrend_residue_zeroCycle', - '_Ozone_LinearTrend_residue_zeroCycle_residue', - '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(54)', - '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(54)_residue', + '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth', + '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue', + '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(54)', + '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(54)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -73,47 +82,49 @@ memory usage: 5.5 KB None Forecasts Month Ozone Ozone_Forecast -216 1972-10-31 06:00:00 NaN 1.171593 -217 1972-11-30 12:00:00 NaN 1.607934 -218 1972-12-30 18:00:00 NaN 2.108468 -219 1973-01-30 00:00:00 NaN 2.555887 -220 1973-03-01 06:00:00 NaN 3.047812 -221 1973-03-31 12:00:00 NaN 3.178621 -222 1973-04-30 18:00:00 NaN 3.187568 -223 1973-05-31 00:00:00 NaN 3.473392 -224 1973-06-30 06:00:00 NaN 2.893446 -225 1973-07-30 12:00:00 NaN 2.285322 -226 1973-08-29 18:00:00 NaN 1.070389 -227 1973-09-29 00:00:00 NaN 0.631172 +216 1972-10-31 06:00:00 NaN 0.207441 +217 1972-11-30 12:00:00 NaN 1.603629 +218 1972-12-30 18:00:00 NaN 2.096575 +219 1973-01-30 00:00:00 NaN 2.593671 +220 1973-03-01 06:00:00 NaN 2.821292 +221 1973-03-31 12:00:00 NaN 2.237640 +222 1973-04-30 18:00:00 NaN 3.111671 +223 1973-05-31 00:00:00 NaN 2.519242 +224 1973-06-30 06:00:00 NaN 2.813193 +225 1973-07-30 12:00:00 NaN 2.258778 +226 1973-08-29 18:00:00 NaN 1.001502 +227 1973-09-29 00:00:00 NaN 0.549184 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-02-01 00:00:00", - "1972-10-01 00:00:00" - ], - "TimeVariable": "Month" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-02-01 00:00:00", + "1972-10-01 00:00:00" + ], + "TimeVariable": "Month" + }, + "Training_Signal_Length": 216 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(54)", + "Cycle": "Seasonal_DayOfMonth", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 216 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(54)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "56", - "MAE": "0.5226228629154416", - "MAPE": "0.186", - "MASE": "0.752", - "RMSE": "0.6336082944222472" + "Model_Performance": { + "COMPLEXITY": "60", + "MAE": "0.4677835668058019", + "MAPE": "0.1664", + "MASE": "0.6731", + "RMSE": "0.5894736747213507" + } } } @@ -122,7 +133,7 @@ Forecasts -{"Month":{"204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-10-01T00:00:00.000Z","215":"1972-10-01T00:00:00.000Z","216":"1972-10-31T06:00:00.000Z","217":"1972-11-30T12:00:00.000Z","218":"1972-12-30T18:00:00.000Z","219":"1973-01-30T00:00:00.000Z","220":"1973-03-01T06:00:00.000Z","221":"1973-03-31T12:00:00.000Z","222":"1973-04-30T18:00:00.000Z","223":"1973-05-31T00:00:00.000Z","224":"1973-06-30T06:00:00.000Z","225":"1973-07-30T12:00:00.000Z","226":"1973-08-29T18:00:00.000Z","227":"1973-09-29T00:00:00.000Z"},"Ozone":{"204":1.5,"205":2.0,"206":3.1,"207":3.0,"208":3.5,"209":3.4,"210":4.0,"211":3.8,"212":3.1,"213":2.1,"214":1.6,"215":1.3,"216":null,"217":null,"218":null,"219":null,"220":null,"221":null,"222":null,"223":null,"224":null,"225":null,"226":null,"227":null},"Ozone_Forecast":{"204":0.9899614492,"205":1.6646046799,"206":2.2575682811,"207":2.7155283254,"208":3.2251441047,"209":3.4191750779,"210":3.4816061958,"211":3.6176533631,"212":3.1699121349,"213":2.3412454914,"214":1.3785314304,"215":1.0864374261,"216":1.1715932689,"217":1.6079344128,"218":2.108468289,"219":2.5558867003,"220":3.0478120715,"221":3.178620916,"222":3.1875676435,"223":3.473392084,"224":2.8934456971,"225":2.2853216955,"226":1.0703887578,"227":0.6311721549}} +{"Month":{"204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-10-01T00:00:00.000Z","215":"1972-10-01T00:00:00.000Z","216":"1972-10-31T06:00:00.000Z","217":"1972-11-30T12:00:00.000Z","218":"1972-12-30T18:00:00.000Z","219":"1973-01-30T00:00:00.000Z","220":"1973-03-01T06:00:00.000Z","221":"1973-03-31T12:00:00.000Z","222":"1973-04-30T18:00:00.000Z","223":"1973-05-31T00:00:00.000Z","224":"1973-06-30T06:00:00.000Z","225":"1973-07-30T12:00:00.000Z","226":"1973-08-29T18:00:00.000Z","227":"1973-09-29T00:00:00.000Z"},"Ozone":{"204":1.5,"205":2.0,"206":3.1,"207":3.0,"208":3.5,"209":3.4,"210":4.0,"211":3.8,"212":3.1,"213":2.1,"214":1.6,"215":1.3,"216":null,"217":null,"218":null,"219":null,"220":null,"221":null,"222":null,"223":null,"224":null,"225":null,"226":null,"227":null},"Ozone_Forecast":{"204":0.893145034,"205":1.7493968922,"206":2.2995489263,"207":2.5160796241,"208":3.1382691723,"209":3.3726550282,"210":3.5817355734,"211":3.5581587159,"212":3.1774845926,"213":2.2385291527,"214":1.3727991452,"215":1.000773588,"216":0.207440642,"217":1.6036294202,"218":2.09657492,"219":2.5936706039,"220":2.821292396,"221":2.2376399369,"222":3.1116712089,"223":2.5192422403,"224":2.8131934716,"225":2.2587778078,"226":1.0015021828,"227":0.5491841621}} diff --git a/tests/references/bugs_issue_82_issue_82_long_cycles.log b/tests/references/bugs_issue_82_issue_82_long_cycles.log index d8cb93b05..fecb37a17 100644 --- a/tests/references/bugs_issue_82_issue_82_long_cycles.log +++ b/tests/references/bugs_issue_82_issue_82_long_cycles.log @@ -2,7 +2,7 @@ INFO:pyaf.std:START_TRAINING 'Signal' TEST_CYCLES_START 2 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 29.540634155273438 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 59.287524700164795 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-02T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=1.8491743129800016 Mean=1.465622874891886 StdDev=0.09943794145139832 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.8491743129800016 Mean=1.465622874891886 StdDev=0.09943794145139832 @@ -17,10 +17,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7069 MASE_Forecast=0.7113 MASE_Test=0.6606 INFO:pyaf.std:MODEL_L1 L1_Fit=0.0788026742868748 L1_Forecast=0.08061744963149645 L1_Test=0.06906760489553276 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09905795697203003 L2_Forecast=0.10094976147793763 L2_Test=0.0891003145657369 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4657142713807132 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.5580532550811768 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 3.443312168121338 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -54,31 +63,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.08061744963149645", - "MAPE": "0.0556", - "MASE": "0.7113", - "RMSE": "0.10094976147793763" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.08061744963149645", + "MAPE": "0.0556", + "MASE": "0.7113", + "RMSE": "0.10094976147793763" + } } } @@ -95,7 +106,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 6 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_6_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 37.62551665306091 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 49.015036821365356 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T19:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=8.333821360571514 Mean=4.6514340347620795 StdDev=2.1536083159682464 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.333821360571514 Mean=4.6514340347620795 StdDev=2.1536083159682464 @@ -104,16 +115,25 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_Hour_r INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour' [Seasonal_Hour] INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0242 MAPE_Forecast=0.0243 MAPE_Test=0.0297 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0241 SMAPE_Forecast=0.0242 SMAPE_Test=0.0296 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0241 MASE_Forecast=0.024 MASE_Test=0.0237 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08017385895829468 L1_Forecast=0.0800841636633722 L1_Test=0.0789298116593309 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10037169161740217 L2_Forecast=0.10034821933850727 L2_Test=0.09050151793877745 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0241 MAPE_Forecast=0.0243 MAPE_Test=0.03 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.024 SMAPE_Forecast=0.0242 SMAPE_Test=0.0299 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.024 MASE_Forecast=0.024 MASE_Test=0.0238 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.08014185956783082 L1_Forecast=0.08013380214010248 L1_Test=0.07923594759930998 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10041282577628087 L2_Forecast=0.10041392569948236 L2_Test=0.09087476936115621 INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.651309216902765 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_Hour 0.0005642027933792093 {0: 0.00112145507781225, 1: -3.3318496867926393, 2: -0.0018319620987155005, 3: 3.340988602011187, 4: -1.6749864737829494, 5: 1.6657038775346944, 6: 0.0022965144751019295, 7: -3.327901785222622, 8: -0.0025588018864155515, 9: 3.3283914344642094, 10: -1.666704273052028, 11: 1.6633980485526036, 12: 0.005034145528322931, 13: -3.3389129989187687, 14: -0.005518464672188195, 15: 3.335847325764391, 16: -1.6693022437105134, 17: 1.6671641840878966, 18: 0.0038605214739781957, 19: -3.326517819482879, 20: 0.0049681482250516495, 21: 3.327643434132467, 22: -1.6661372996024961, 23: 1.6586381732888729} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 4.90747594833374 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 3.76892352104187 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -139,47 +159,49 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.1 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 4.653405461010779] - [Timestamp('2003-08-25 21:00:00') nan 7.984028325237326] - [Timestamp('2003-08-25 22:00:00') nan 2.98533398180741] - [Timestamp('2003-08-25 23:00:00') nan 6.3149379222122395] - [Timestamp('2003-08-26 00:00:00') nan 4.6506634205867385] - [Timestamp('2003-08-26 01:00:00') nan 1.3170180001147713] - [Timestamp('2003-08-26 02:00:00') nan 4.649844652508211] - [Timestamp('2003-08-26 03:00:00') nan 7.987823896560785] - [Timestamp('2003-08-26 04:00:00') nan 2.980441699923875] - [Timestamp('2003-08-26 05:00:00') nan 6.317921298813156] - [Timestamp('2003-08-26 06:00:00') nan 4.651567705019989] - [Timestamp('2003-08-26 07:00:00') nan 1.3252349293702048]] + [[Timestamp('2003-08-25 20:00:00') nan 4.656277365127817] + [Timestamp('2003-08-25 21:00:00') nan 7.978952651035232] + [Timestamp('2003-08-25 22:00:00') nan 2.9851719173002693] + [Timestamp('2003-08-25 23:00:00') nan 6.309947390191638] + [Timestamp('2003-08-26 00:00:00') nan 4.652430671980578] + [Timestamp('2003-08-26 01:00:00') nan 1.3194595301101262] + [Timestamp('2003-08-26 02:00:00') nan 4.64947725480405] + [Timestamp('2003-08-26 03:00:00') nan 7.992297818913952] + [Timestamp('2003-08-26 04:00:00') nan 2.976322743119816] + [Timestamp('2003-08-26 05:00:00') nan 6.31701309443746] + [Timestamp('2003-08-26 06:00:00') nan 4.653605731377867] + [Timestamp('2003-08-26 07:00:00') nan 1.3234074316801436]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR", + "Cycle": "Seasonal_Hour", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR", - "Cycle": "Seasonal_Hour", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "0.0800841636633722", - "MAPE": "0.0243", - "MASE": "0.024", - "RMSE": "0.10034821933850727" + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.08013380214010248", + "MAPE": "0.0243", + "MASE": "0.024", + "RMSE": "0.10041392569948236" + } } } @@ -188,7 +210,7 @@ Forecasts -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null},"Signal_Forecast":{"31988":4.653405461,"31989":7.9840283252,"31990":2.9853339818,"31991":6.3149379222,"31992":4.6506634206,"31993":1.3170180001,"31994":4.6498446525,"31995":7.9878238966,"31996":2.9804416999,"31997":6.3179212988,"31998":4.651567705,"31999":1.3252349294}} +{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null},"Signal_Forecast":{"31988":4.6562773651,"31989":7.978952651,"31990":2.9851719173,"31991":6.3099473902,"31992":4.652430672,"31993":1.3194595301,"31994":4.6494772548,"31995":7.9922978189,"31996":2.9763227431,"31997":6.3170130944,"31998":4.6536057314,"31999":1.3234074317}} @@ -196,32 +218,41 @@ TEST_CYCLES_END 6 TEST_CYCLES_START 10 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_10_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 35.65880584716797 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 79.98473644256592 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T13:00:00.000000 TimeDelta= Horizon=20 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=8.700169238172657 Mean=4.751653298073094 StdDev=2.247677208865058 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.700169238172657 Mean=4.751653298073094 StdDev=2.247677208865058 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0228 MAPE_Forecast=0.0233 MAPE_Test=0.0221 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0227 SMAPE_Forecast=0.0232 SMAPE_Test=0.0218 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0263 MASE_Forecast=0.0269 MASE_Test=0.0243 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.078789925042696 L1_Forecast=0.08066801728649253 L1_Test=0.07576694082457418 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09903340370074636 L2_Forecast=0.10100139614354746 L2_Test=0.09544243176063513 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07878495938794243 L1_Forecast=0.08064743691160367 L1_Test=0.07585865642956542 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09903920885077316 L2_Forecast=0.10097896423433593 L2_Test=0.0954013346310342 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.751863564238395 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 10 0.1146752547368104 {0: -2.4013563201199983, 1: 1.602211122589586, 2: 3.599158544297013, 3: -2.398889422062649, 4: -0.40125388302339715, 5: -3.4021823361080994, 6: -1.4036668502047256, 7: 1.5970111896443058, 8: 0.6021353433222743, 9: 2.6017161805354796} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 5.851982831954956 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 5.272850036621094 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -240,55 +271,57 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.3 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 5.3558670995493065] - [Timestamp('2003-08-25 21:00:00') nan 7.351919707979566] - [Timestamp('2003-08-25 22:00:00') nan 2.35085274066433] - [Timestamp('2003-08-25 23:00:00') nan 6.3551729256245535] - [Timestamp('2003-08-26 00:00:00') nan 8.351547764631942] - [Timestamp('2003-08-26 01:00:00') nan 2.353923101918168] - [Timestamp('2003-08-26 02:00:00') nan 4.3502614448506804] - [Timestamp('2003-08-26 03:00:00') nan 1.3507617134694518] - [Timestamp('2003-08-26 04:00:00') nan 3.348091682713169] - [Timestamp('2003-08-26 05:00:00') nan 6.350079446795888] - [Timestamp('2003-08-26 06:00:00') nan 5.3558670995493065] - [Timestamp('2003-08-26 07:00:00') nan 7.351919707979566] - [Timestamp('2003-08-26 08:00:00') nan 2.35085274066433] - [Timestamp('2003-08-26 09:00:00') nan 6.3551729256245535] - [Timestamp('2003-08-26 10:00:00') nan 8.351547764631942] - [Timestamp('2003-08-26 11:00:00') nan 2.353923101918168] - [Timestamp('2003-08-26 12:00:00') nan 4.3502614448506804] - [Timestamp('2003-08-26 13:00:00') nan 1.3507617134694518] - [Timestamp('2003-08-26 14:00:00') nan 3.348091682713169] - [Timestamp('2003-08-26 15:00:00') nan 6.350079446795888]] + [[Timestamp('2003-08-25 20:00:00') nan 5.353998907560669] + [Timestamp('2003-08-25 21:00:00') nan 7.353579744773874] + [Timestamp('2003-08-25 22:00:00') nan 2.3505072441183965] + [Timestamp('2003-08-25 23:00:00') nan 6.354074686827981] + [Timestamp('2003-08-26 00:00:00') nan 8.351022108535407] + [Timestamp('2003-08-26 01:00:00') nan 2.352974142175746] + [Timestamp('2003-08-26 02:00:00') nan 4.350609681214998] + [Timestamp('2003-08-26 03:00:00') nan 1.3496812281302955] + [Timestamp('2003-08-26 04:00:00') nan 3.3481967140336693] + [Timestamp('2003-08-26 05:00:00') nan 6.348874753882701] + [Timestamp('2003-08-26 06:00:00') nan 5.353998907560669] + [Timestamp('2003-08-26 07:00:00') nan 7.353579744773874] + [Timestamp('2003-08-26 08:00:00') nan 2.3505072441183965] + [Timestamp('2003-08-26 09:00:00') nan 6.354074686827981] + [Timestamp('2003-08-26 10:00:00') nan 8.351022108535407] + [Timestamp('2003-08-26 11:00:00') nan 2.352974142175746] + [Timestamp('2003-08-26 12:00:00') nan 4.350609681214998] + [Timestamp('2003-08-26 13:00:00') nan 1.3496812281302955] + [Timestamp('2003-08-26 14:00:00') nan 3.3481967140336693] + [Timestamp('2003-08-26 15:00:00') nan 6.348874753882701]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 20, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 20, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08066801728649253", - "MAPE": "0.0233", - "MASE": "0.0269", - "RMSE": "0.10100139614354746" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08064743691160367", + "MAPE": "0.0233", + "MASE": "0.0269", + "RMSE": "0.10097896423433593" + } } } @@ -297,7 +330,7 @@ Forecasts -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null},"Signal_Forecast":{"31988":5.3558670995,"31989":7.351919708,"31990":2.3508527407,"31991":6.3551729256,"31992":8.3515477646,"31993":2.3539231019,"31994":4.3502614449,"31995":1.3507617135,"31996":3.3480916827,"31997":6.3500794468,"31998":5.3558670995,"31999":7.351919708,"32000":2.3508527407,"32001":6.3551729256,"32002":8.3515477646,"32003":2.3539231019,"32004":4.3502614449,"32005":1.3507617135,"32006":3.3480916827,"32007":6.3500794468}} +{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null},"Signal_Forecast":{"31988":5.3539989076,"31989":7.3535797448,"31990":2.3505072441,"31991":6.3540746868,"31992":8.3510221085,"31993":2.3529741422,"31994":4.3506096812,"31995":1.3496812281,"31996":3.348196714,"31997":6.3488747539,"31998":5.3539989076,"31999":7.3535797448,"32000":2.3505072441,"32001":6.3540746868,"32002":8.3510221085,"32003":2.3529741422,"32004":4.3506096812,"32005":1.3496812281,"32006":3.348196714,"32007":6.3488747539}} @@ -305,32 +338,41 @@ TEST_CYCLES_END 10 TEST_CYCLES_START 14 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_14_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 47.12329816818237 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 109.88680005073547 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T07:00:00.000000 TimeDelta= Horizon=28 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=9.498336323016646 Mean=4.914127790036362 StdDev=2.21938241641426 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=9.498336323016646 Mean=4.914127790036362 StdDev=2.21938241641426 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR' [ConstantTrend + Seasonal_HourOfWeek + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0215 MAPE_Forecast=0.0222 MAPE_Test=0.0173 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0214 SMAPE_Forecast=0.0221 SMAPE_Test=0.0173 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0366 MASE_Forecast=0.0375 MASE_Test=0.0299 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07879207580928814 L1_Forecast=0.08070664717092943 L1_Test=0.06470131886925524 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09904274122031652 L2_Forecast=0.10102433007959907 L2_Test=0.08592266975028022 -INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek' [Seasonal_HourOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0214 MAPE_Forecast=0.0223 MAPE_Test=0.0174 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0213 SMAPE_Forecast=0.0222 SMAPE_Test=0.0174 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0365 MASE_Forecast=0.0377 MASE_Test=0.0294 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07841817742208272 L1_Forecast=0.08106350300805767 L1_Test=0.06378458189608335 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09893948792096707 L2_Forecast=0.10150581080497878 L2_Test=0.08388505168971785 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.914135611511643 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_HourOfWeek 0.2803087205458108 {120: -2.2043226744117304, 121: 0.6811989989677008, 122: 2.076812016777396, 123: -2.1895067019277303, 124: -0.7581014090823253, 125: -2.9048673349272356, 126: -1.4888704063349651, 127: 0.6558952394735433, 128: -0.04747066194316574, 129: 1.371763365906613, 130: 1.372428391741951, 131: 4.230131916785178, 132: 2.7975700345455907, 133: -3.612094833182529, 134: -2.2045484315243122, 135: 0.6603556870262643, 136: 2.0880442640684267, 137: -2.1813010858712087, 138: -0.7562016112006322, 139: -2.898346622458507, 140: -1.4752902710229727, 141: 0.6744120673341296, 142: -0.07005427391410102, 143: 1.376065445011709, 144: 1.3775696733304406, 145: 4.244732633751619, 146: 2.8065676462488147, 147: -3.6484829792228153, 148: -2.207375638323446, 149: 0.6679071022340146, 150: 2.077310047714339, 151: -2.184248400864179, 152: -0.7573658159721592, 153: -2.891881653595087, 154: -1.4835864341506961, 155: 0.6559970545577212, 156: -0.056859930483175614, 157: 1.368607525431845, 158: 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-0.051256044046614324, 73: 1.3751959182707867, 74: 1.3821643252884939, 75: 4.23155095792093, 76: 2.801102344844081, 77: -3.6248187581111653, 78: -2.1721258837820727, 79: 0.6779791879306818, 80: 2.093320828614375, 81: -2.1915551319257975, 82: -0.7774697156441679, 83: -2.9077790328355966, 84: -1.4763367262592495, 85: 0.6575645546797291, 86: -0.04211937809267852, 87: 1.3890107480197043, 88: 1.3767872640223677, 89: 4.234557309017047, 90: 2.7902654206444, 91: -3.637796673170218, 92: -2.2001742876233, 93: 0.6411969651836023, 94: 2.0927896046160708, 95: -2.184306631376426, 96: -0.7569814703634821, 97: -2.9239088324756732, 98: -1.4829451118549277, 99: 0.6595603923143787, 100: -0.05588015822674963, 101: 1.3696104976748265, 102: 1.3901883476120611, 103: 4.235757582331614, 104: 2.8123260220901, 105: -3.6112582925739054, 106: -2.1894570659376518, 107: 0.660043537939007, 108: 2.111040517144278, 109: -2.203334305739854, 110: -0.7560786762214908, 111: -2.8973948824432947, 112: -1.4731831641748683, 113: 0.6466818309486579, 114: -0.059040163255775546, 115: 1.389416846410355, 116: 1.3853591101027876, 117: 4.234362471007696, 118: 2.8041453118689033, 119: -3.6233961401698798} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 6.9026243686676025 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 6.036162614822388 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek', + '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue', + '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR', + '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -349,63 +391,65 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.5 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 7.716779541001199] - [Timestamp('2003-08-25 21:00:00') nan 1.2918727971268784] - [Timestamp('2003-08-25 22:00:00') nan 2.719435238730687] - [Timestamp('2003-08-25 23:00:00') nan 5.576697909984164] - [Timestamp('2003-08-26 00:00:00') nan 7.0064048844872175] - [Timestamp('2003-08-26 01:00:00') nan 2.7232909276901958] - [Timestamp('2003-08-26 02:00:00') nan 4.150654951891211] - [Timestamp('2003-08-26 03:00:00') nan 2.0077658443535538] - [Timestamp('2003-08-26 04:00:00') nan 3.436589194947015] - [Timestamp('2003-08-26 05:00:00') nan 5.576229214923491] - [Timestamp('2003-08-26 06:00:00') nan 4.863228480739073] - [Timestamp('2003-08-26 07:00:00') nan 6.292000843663519] - [Timestamp('2003-08-26 08:00:00') nan 6.288438291685888] - [Timestamp('2003-08-26 09:00:00') nan 9.149403492230894] - [Timestamp('2003-08-26 10:00:00') nan 7.716779541001199] - [Timestamp('2003-08-26 11:00:00') nan 1.2918727971268784] - [Timestamp('2003-08-26 12:00:00') nan 2.719435238730687] - [Timestamp('2003-08-26 13:00:00') nan 5.576697909984164] - [Timestamp('2003-08-26 14:00:00') nan 7.0064048844872175] - [Timestamp('2003-08-26 15:00:00') nan 2.7232909276901958] - [Timestamp('2003-08-26 16:00:00') nan 4.150654951891211] - [Timestamp('2003-08-26 17:00:00') nan 2.0077658443535538] - [Timestamp('2003-08-26 18:00:00') nan 3.436589194947015] - [Timestamp('2003-08-26 19:00:00') nan 5.576229214923491] - [Timestamp('2003-08-26 20:00:00') nan 4.863228480739073] - [Timestamp('2003-08-26 21:00:00') nan 6.292000843663519] - [Timestamp('2003-08-26 22:00:00') nan 6.288438291685888] - [Timestamp('2003-08-26 23:00:00') nan 9.149403492230894]] + [[Timestamp('2003-08-25 20:00:00') nan 7.717147828648914] + [Timestamp('2003-08-25 21:00:00') nan 1.2933139104814266] + [Timestamp('2003-08-25 22:00:00') nan 2.7236232159522675] + [Timestamp('2003-08-25 23:00:00') nan 5.591697124222082] + [Timestamp('2003-08-26 00:00:00') nan 7.008624582516546] + [Timestamp('2003-08-26 01:00:00') nan 2.714348496482085] + [Timestamp('2003-08-26 02:00:00') nan 4.151050185084388] + [Timestamp('2003-08-26 03:00:00') nan 2.00184785125818] + [Timestamp('2003-08-26 04:00:00') nan 3.431419672994254] + [Timestamp('2003-08-26 05:00:00') nan 5.568502443278032] + [Timestamp('2003-08-26 06:00:00') nan 4.864240099171656] + [Timestamp('2003-08-26 07:00:00') nan 6.288243687752381] + [Timestamp('2003-08-26 08:00:00') nan 6.29614579502398] + [Timestamp('2003-08-26 09:00:00') nan 9.15684187142951] + [Timestamp('2003-08-26 10:00:00') nan 7.71828125847931] + [Timestamp('2003-08-26 11:00:00') nan 1.2884162444152008] + [Timestamp('2003-08-26 12:00:00') nan 2.7137216400608395] + [Timestamp('2003-08-26 13:00:00') nan 5.576918865475979] + [Timestamp('2003-08-26 14:00:00') nan 7.008096744787299] + [Timestamp('2003-08-26 15:00:00') nan 2.725705095531281] + [Timestamp('2003-08-26 16:00:00') nan 4.1442788461224165] + [Timestamp('2003-08-26 17:00:00') nan 2.014860219949029] + [Timestamp('2003-08-26 18:00:00') nan 3.4552919877871044] + [Timestamp('2003-08-26 19:00:00') nan 5.579804774081413] + [Timestamp('2003-08-26 20:00:00') nan 4.842070680562014] + [Timestamp('2003-08-26 21:00:00') nan 6.288141491587831] + [Timestamp('2003-08-26 22:00:00') nan 6.300583287298524] + [Timestamp('2003-08-26 23:00:00') nan 9.15731294483159]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 28, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 28, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR", + "Cycle": "Seasonal_HourOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08070664717092943", - "MAPE": "0.0222", - "MASE": "0.0375", - "RMSE": "0.10102433007959907" + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.08106350300805767", + "MAPE": "0.0223", + "MASE": "0.0377", + "RMSE": "0.10150581080497878" + } } } @@ -414,7 +458,7 @@ Forecasts -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null},"Signal_Forecast":{"31988":7.716779541,"31989":1.2918727971,"31990":2.7194352387,"31991":5.57669791,"31992":7.0064048845,"31993":2.7232909277,"31994":4.1506549519,"31995":2.0077658444,"31996":3.4365891949,"31997":5.5762292149,"31998":4.8632284807,"31999":6.2920008437,"32000":6.2884382917,"32001":9.1494034922,"32002":7.716779541,"32003":1.2918727971,"32004":2.7194352387,"32005":5.57669791,"32006":7.0064048845,"32007":2.7232909277,"32008":4.1506549519,"32009":2.0077658444,"32010":3.4365891949,"32011":5.5762292149,"32012":4.8632284807,"32013":6.2920008437,"32014":6.2884382917,"32015":9.1494034922}} +{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null},"Signal_Forecast":{"31988":7.7171478286,"31989":1.2933139105,"31990":2.723623216,"31991":5.5916971242,"31992":7.0086245825,"31993":2.7143484965,"31994":4.1510501851,"31995":2.0018478513,"31996":3.431419673,"31997":5.5685024433,"31998":4.8642400992,"31999":6.2882436878,"32000":6.296145795,"32001":9.1568418714,"32002":7.7182812585,"32003":1.2884162444,"32004":2.7137216401,"32005":5.5769188655,"32006":7.0080967448,"32007":2.7257050955,"32008":4.1442788461,"32009":2.0148602199,"32010":3.4552919878,"32011":5.5798047741,"32012":4.8420706806,"32013":6.2881414916,"32014":6.3005832873,"32015":9.1573129448}} @@ -422,32 +466,41 @@ TEST_CYCLES_END 14 TEST_CYCLES_START 18 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_18_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 81.50431084632874 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 77.6365077495575 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=10.576427076945718 Mean=5.633355844209139 StdDev=2.5597257798105417 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.576427076945718 Mean=5.633355844209139 StdDev=2.5597257798105417 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0185 MAPE_Forecast=0.0181 MAPE_Test=0.0189 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0185 SMAPE_Forecast=0.0181 SMAPE_Test=0.0184 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.023 MASE_Forecast=0.0226 MASE_Test=0.0202 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07950354912814159 L1_Forecast=0.0782535805622656 L1_Test=0.07113849940016446 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.0999744396283691 L2_Forecast=0.09823029320966337 L2_Test=0.08636308805383552 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0185 MAPE_Forecast=0.0181 MAPE_Test=0.0185 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0185 SMAPE_Forecast=0.0181 SMAPE_Test=0.0181 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.023 MASE_Forecast=0.0227 MASE_Test=0.0198 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07958924340299912 L1_Forecast=0.07832565396296981 L1_Test=0.07002568368804168 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10011698257279802 L2_Forecast=0.09829060464825726 L2_Test=0.0852649926930095 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5.633453794953687 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 18 -0.6718146495871293 {0: -3.1791669103965314, 1: -0.9577375212383066, 2: 0.146711186111816, 3: -2.622064593343656, 4: -1.5152844165964323, 5: 1.8191817883665733, 6: -4.290428403648969, 7: -1.5097390376254975, 8: -0.9561993521071699, 9: 2.935819719171061, 10: 4.599750234741594, 11: -2.0663613648920727, 12: 2.379912394752645, 13: -0.406214935857629, 14: 0.15951855376602087, 15: 4.044687276052934, 16: -2.061640301708385, 17: 3.4861072437956375} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 14.354127883911133 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 7.913002967834473 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -466,71 +519,73 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.7 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 5.788418344125107] - [Timestamp('2003-08-25 21:00:00') nan 3.0101117841588496] - [Timestamp('2003-08-25 22:00:00') nan 4.1092264357885835] - [Timestamp('2003-08-25 23:00:00') nan 7.448158075388571] - [Timestamp('2003-08-26 00:00:00') nan 1.3511247728046154] - [Timestamp('2003-08-26 01:00:00') nan 4.128653701408584] - [Timestamp('2003-08-26 02:00:00') nan 4.669469081029887] - [Timestamp('2003-08-26 03:00:00') nan 8.570324634505875] - [Timestamp('2003-08-26 04:00:00') nan 10.229335798050279] - [Timestamp('2003-08-26 05:00:00') nan 3.5664661715914248] - [Timestamp('2003-08-26 06:00:00') nan 8.016445790025644] - [Timestamp('2003-08-26 07:00:00') nan 5.213866445905249] - [Timestamp('2003-08-26 08:00:00') nan 5.789888275914795] - [Timestamp('2003-08-26 09:00:00') nan 9.67762249640452] - [Timestamp('2003-08-26 10:00:00') nan 3.571184940962243] - [Timestamp('2003-08-26 11:00:00') nan 9.109504241452747] - [Timestamp('2003-08-26 12:00:00') nan 2.45215872629142] - [Timestamp('2003-08-26 13:00:00') nan 4.6732908141459575] - [Timestamp('2003-08-26 14:00:00') nan 5.769652925477868] - [Timestamp('2003-08-26 15:00:00') nan 3.0052983882795914] - [Timestamp('2003-08-26 16:00:00') nan 4.1243901161998995] - [Timestamp('2003-08-26 17:00:00') nan 7.454607491669816] - [Timestamp('2003-08-26 18:00:00') nan 1.3464872328715884] - [Timestamp('2003-08-26 19:00:00') nan 4.120466453406708] - [Timestamp('2003-08-26 20:00:00') nan 4.686654032061969] - [Timestamp('2003-08-26 21:00:00') nan 8.567513688868868] - [Timestamp('2003-08-26 22:00:00') nan 10.229771023702636] - [Timestamp('2003-08-26 23:00:00') nan 3.576885969712121] - [Timestamp('2003-08-27 00:00:00') nan 8.012253232171728] - [Timestamp('2003-08-27 01:00:00') nan 5.223467005597552] - [Timestamp('2003-08-27 02:00:00') nan 5.784752154380072] - [Timestamp('2003-08-27 03:00:00') nan 9.677738708684743] - [Timestamp('2003-08-27 04:00:00') nan 3.571549419113958] - [Timestamp('2003-08-27 05:00:00') nan 9.118073178385938] - [Timestamp('2003-08-27 06:00:00') nan 2.4592149675902384] - [Timestamp('2003-08-27 07:00:00') nan 4.675235422551577]] + [[Timestamp('2003-08-25 20:00:00') nan 5.780164981065504] + [Timestamp('2003-08-25 21:00:00') nan 3.011389201610031] + [Timestamp('2003-08-25 22:00:00') nan 4.118169378357255] + [Timestamp('2003-08-25 23:00:00') nan 7.45263558332026] + [Timestamp('2003-08-26 00:00:00') nan 1.3430253913047183] + [Timestamp('2003-08-26 01:00:00') nan 4.123714757328189] + [Timestamp('2003-08-26 02:00:00') nan 4.677254442846517] + [Timestamp('2003-08-26 03:00:00') nan 8.569273514124749] + [Timestamp('2003-08-26 04:00:00') nan 10.233204029695282] + [Timestamp('2003-08-26 05:00:00') nan 3.5670924300616145] + [Timestamp('2003-08-26 06:00:00') nan 8.013366189706332] + [Timestamp('2003-08-26 07:00:00') nan 5.227238859096058] + [Timestamp('2003-08-26 08:00:00') nan 5.792972348719708] + [Timestamp('2003-08-26 09:00:00') nan 9.678141071006621] + [Timestamp('2003-08-26 10:00:00') nan 3.571813493245302] + [Timestamp('2003-08-26 11:00:00') nan 9.119561038749325] + [Timestamp('2003-08-26 12:00:00') nan 2.4542868845571557] + [Timestamp('2003-08-26 13:00:00') nan 4.675716273715381] + [Timestamp('2003-08-26 14:00:00') nan 5.780164981065504] + [Timestamp('2003-08-26 15:00:00') nan 3.011389201610031] + [Timestamp('2003-08-26 16:00:00') nan 4.118169378357255] + [Timestamp('2003-08-26 17:00:00') nan 7.45263558332026] + [Timestamp('2003-08-26 18:00:00') nan 1.3430253913047183] + [Timestamp('2003-08-26 19:00:00') nan 4.123714757328189] + [Timestamp('2003-08-26 20:00:00') nan 4.677254442846517] + [Timestamp('2003-08-26 21:00:00') nan 8.569273514124749] + [Timestamp('2003-08-26 22:00:00') nan 10.233204029695282] + [Timestamp('2003-08-26 23:00:00') nan 3.5670924300616145] + [Timestamp('2003-08-27 00:00:00') nan 8.013366189706332] + [Timestamp('2003-08-27 01:00:00') nan 5.227238859096058] + [Timestamp('2003-08-27 02:00:00') nan 5.792972348719708] + [Timestamp('2003-08-27 03:00:00') nan 9.678141071006621] + [Timestamp('2003-08-27 04:00:00') nan 3.571813493245302] + [Timestamp('2003-08-27 05:00:00') nan 9.119561038749325] + [Timestamp('2003-08-27 06:00:00') nan 2.4542868845571557] + [Timestamp('2003-08-27 07:00:00') nan 4.675716273715381]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 36, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 36, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.0782535805622656", - "MAPE": "0.0181", - "MASE": "0.0226", - "RMSE": "0.09823029320966337" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.07832565396296981", + "MAPE": "0.0181", + "MASE": "0.0227", + "RMSE": "0.09829060464825726" + } } } @@ -539,7 +594,7 @@ Forecasts -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z","32016":"2003-08-27T00:00:00.000Z","32017":"2003-08-27T01:00:00.000Z","32018":"2003-08-27T02:00:00.000Z","32019":"2003-08-27T03:00:00.000Z","32020":"2003-08-27T04:00:00.000Z","32021":"2003-08-27T05:00:00.000Z","32022":"2003-08-27T06:00:00.000Z","32023":"2003-08-27T07:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null,"32016":null,"32017":null,"32018":null,"32019":null,"32020":null,"32021":null,"32022":null,"32023":null},"Signal_Forecast":{"31988":5.7884183441,"31989":3.0101117842,"31990":4.1092264358,"31991":7.4481580754,"31992":1.3511247728,"31993":4.1286537014,"31994":4.669469081,"31995":8.5703246345,"31996":10.2293357981,"31997":3.5664661716,"31998":8.01644579,"31999":5.2138664459,"32000":5.7898882759,"32001":9.6776224964,"32002":3.571184941,"32003":9.1095042415,"32004":2.4521587263,"32005":4.6732908141,"32006":5.7696529255,"32007":3.0052983883,"32008":4.1243901162,"32009":7.4546074917,"32010":1.3464872329,"32011":4.1204664534,"32012":4.6866540321,"32013":8.5675136889,"32014":10.2297710237,"32015":3.5768859697,"32016":8.0122532322,"32017":5.2234670056,"32018":5.7847521544,"32019":9.6777387087,"32020":3.5715494191,"32021":9.1180731784,"32022":2.4592149676,"32023":4.6752354226}} +{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z","32016":"2003-08-27T00:00:00.000Z","32017":"2003-08-27T01:00:00.000Z","32018":"2003-08-27T02:00:00.000Z","32019":"2003-08-27T03:00:00.000Z","32020":"2003-08-27T04:00:00.000Z","32021":"2003-08-27T05:00:00.000Z","32022":"2003-08-27T06:00:00.000Z","32023":"2003-08-27T07:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null,"32016":null,"32017":null,"32018":null,"32019":null,"32020":null,"32021":null,"32022":null,"32023":null},"Signal_Forecast":{"31988":5.7801649811,"31989":3.0113892016,"31990":4.1181693784,"31991":7.4526355833,"31992":1.3430253913,"31993":4.1237147573,"31994":4.6772544428,"31995":8.5692735141,"31996":10.2332040297,"31997":3.5670924301,"31998":8.0133661897,"31999":5.2272388591,"32000":5.7929723487,"32001":9.678141071,"32002":3.5718134932,"32003":9.1195610387,"32004":2.4542868846,"32005":4.6757162737,"32006":5.7801649811,"32007":3.0113892016,"32008":4.1181693784,"32009":7.4526355833,"32010":1.3430253913,"32011":4.1237147573,"32012":4.6772544428,"32013":8.5692735141,"32014":10.2332040297,"32015":3.5670924301,"32016":8.0133661897,"32017":5.2272388591,"32018":5.7929723487,"32019":9.678141071,"32020":3.5718134932,"32021":9.1195610387,"32022":2.4542868846,"32023":4.6757162737}} @@ -547,31 +602,40 @@ TEST_CYCLES_END 18 TEST_CYCLES_START 22 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_22_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 70.88109087944031 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 90.84341764450073 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-11-30T18:00:00.000000 TimeDelta= Horizon=44 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=10.758520015803596 Mean=6.098222819806447 StdDev=2.810646854893992 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.758520015803596 Mean=6.098222819806447 StdDev=2.810646854893992 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0179 MAPE_Forecast=0.018 MAPE_Test=0.0223 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0179 SMAPE_Forecast=0.0179 SMAPE_Test=0.0222 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0299 MASE_Forecast=0.03 MASE_Test=0.0355 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08037054640188437 L1_Forecast=0.08065873696180399 L1_Test=0.0908536290242984 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10073418401778016 L2_Forecast=0.10083100124303364 L2_Test=0.11556693323710295 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0179 MAPE_Forecast=0.018 MAPE_Test=0.0222 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0178 SMAPE_Forecast=0.0179 SMAPE_Test=0.0221 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0299 MASE_Forecast=0.03 MASE_Test=0.0353 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.08032611138875244 L1_Forecast=0.08075293102223974 L1_Test=0.09032240650280009 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10078902909585007 L2_Forecast=0.10091825923290786 L2_Test=0.1149716067777537 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.0981444673224665 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 22 -0.8289203680368509 {0: -3.8584048930599213, 1: -2.044771209716883, 2: -1.1313973381021545, 3: 3.4104909287428056, 4: 4.3291513599874065, 5: 2.958284496745141, 6: -3.4118851906278436, 7: -2.498326554648907, 8: 0.2208718697573886, 9: -4.774180091357353, 10: -2.499232322795973, 11: -2.051079991143092, 12: 1.1293139040958398, 13: 2.500372795885049, 14: -2.958150138832557, 15: 0.6773172828592138, 16: -1.583588645659888, 17: -1.12902668929003, 18: 3.864863385068819, 19: 3.4013741031993536, 20: 2.0444213681217, 21: 3.411227384830518} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 12.907360792160034 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 6.5678629875183105 Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -590,79 +654,81 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.9 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 2.2382608505038615] - [Timestamp('2003-08-25 21:00:00') nan 4.054464386821573] - [Timestamp('2003-08-25 22:00:00') nan 4.964740805683372] - [Timestamp('2003-08-25 23:00:00') nan 9.509472424714387] - [Timestamp('2003-08-26 00:00:00') nan 10.421657788635786] - [Timestamp('2003-08-26 01:00:00') nan 9.05993047427913] - [Timestamp('2003-08-26 02:00:00') nan 2.688807941098492] - [Timestamp('2003-08-26 03:00:00') nan 3.597045553119314] - [Timestamp('2003-08-26 04:00:00') nan 6.321125684665563] - [Timestamp('2003-08-26 05:00:00') nan 1.3179039753830226] - [Timestamp('2003-08-26 06:00:00') nan 3.5990967615138207] - [Timestamp('2003-08-26 07:00:00') nan 4.050299921933755] - [Timestamp('2003-08-26 08:00:00') nan 7.228097682961997] - [Timestamp('2003-08-26 09:00:00') nan 8.598890858436441] - [Timestamp('2003-08-26 10:00:00') nan 3.1451402885829647] - [Timestamp('2003-08-26 11:00:00') nan 6.779839229691158] - [Timestamp('2003-08-26 12:00:00') nan 4.511119616872964] - [Timestamp('2003-08-26 13:00:00') nan 4.965135346238603] - [Timestamp('2003-08-26 14:00:00') nan 9.960774113308226] - [Timestamp('2003-08-26 15:00:00') nan 9.505338636775136] - [Timestamp('2003-08-26 16:00:00') nan 8.144452038702253] - [Timestamp('2003-08-26 17:00:00') nan 9.506390941503097] - [Timestamp('2003-08-26 18:00:00') nan 2.2382608505038615] - [Timestamp('2003-08-26 19:00:00') nan 4.054464386821573] - [Timestamp('2003-08-26 20:00:00') nan 4.964740805683372] - [Timestamp('2003-08-26 21:00:00') nan 9.509472424714387] - [Timestamp('2003-08-26 22:00:00') nan 10.421657788635786] - [Timestamp('2003-08-26 23:00:00') nan 9.05993047427913] - [Timestamp('2003-08-27 00:00:00') nan 2.688807941098492] - [Timestamp('2003-08-27 01:00:00') nan 3.597045553119314] - [Timestamp('2003-08-27 02:00:00') nan 6.321125684665563] - [Timestamp('2003-08-27 03:00:00') nan 1.3179039753830226] - [Timestamp('2003-08-27 04:00:00') nan 3.5990967615138207] - [Timestamp('2003-08-27 05:00:00') nan 4.050299921933755] - [Timestamp('2003-08-27 06:00:00') nan 7.228097682961997] - [Timestamp('2003-08-27 07:00:00') nan 8.598890858436441] - [Timestamp('2003-08-27 08:00:00') nan 3.1451402885829647] - [Timestamp('2003-08-27 09:00:00') nan 6.779839229691158] - [Timestamp('2003-08-27 10:00:00') nan 4.511119616872964] - [Timestamp('2003-08-27 11:00:00') nan 4.965135346238603] - [Timestamp('2003-08-27 12:00:00') nan 9.960774113308226] - [Timestamp('2003-08-27 13:00:00') nan 9.505338636775136] - [Timestamp('2003-08-27 14:00:00') nan 8.144452038702253] - [Timestamp('2003-08-27 15:00:00') nan 9.506390941503097]] + [[Timestamp('2003-08-25 20:00:00') nan 2.239739574262545] + [Timestamp('2003-08-25 21:00:00') nan 4.053373257605584] + [Timestamp('2003-08-25 22:00:00') nan 4.9667471292203125] + [Timestamp('2003-08-25 23:00:00') nan 9.508635396065273] + [Timestamp('2003-08-26 00:00:00') nan 10.427295827309873] + [Timestamp('2003-08-26 01:00:00') nan 9.056428964067607] + [Timestamp('2003-08-26 02:00:00') nan 2.686259276694623] + [Timestamp('2003-08-26 03:00:00') nan 3.5998179126735597] + [Timestamp('2003-08-26 04:00:00') nan 6.319016337079855] + [Timestamp('2003-08-26 05:00:00') nan 1.3239643759651134] + [Timestamp('2003-08-26 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[Timestamp('2003-08-26 23:00:00') nan 9.056428964067607] + [Timestamp('2003-08-27 00:00:00') nan 2.686259276694623] + [Timestamp('2003-08-27 01:00:00') nan 3.5998179126735597] + [Timestamp('2003-08-27 02:00:00') nan 6.319016337079855] + [Timestamp('2003-08-27 03:00:00') nan 1.3239643759651134] + [Timestamp('2003-08-27 04:00:00') nan 3.5989121445264933] + [Timestamp('2003-08-27 05:00:00') nan 4.047064476179374] + [Timestamp('2003-08-27 06:00:00') nan 7.227458371418306] + [Timestamp('2003-08-27 07:00:00') nan 8.598517263207516] + [Timestamp('2003-08-27 08:00:00') nan 3.1399943284899097] + [Timestamp('2003-08-27 09:00:00') nan 6.77546175018168] + [Timestamp('2003-08-27 10:00:00') nan 4.5145558216625785] + [Timestamp('2003-08-27 11:00:00') nan 4.9691177780324365] + [Timestamp('2003-08-27 12:00:00') nan 9.963007852391286] + [Timestamp('2003-08-27 13:00:00') nan 9.49951857052182] + [Timestamp('2003-08-27 14:00:00') nan 8.142565835444167] + [Timestamp('2003-08-27 15:00:00') nan 9.509371852152984]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 44, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 44, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08065873696180399", - "MAPE": "0.018", - "MASE": "0.03", - "RMSE": "0.10083100124303364" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08075293102223974", + "MAPE": "0.018", + "MASE": "0.03", + "RMSE": "0.10091825923290786" + } } } @@ -671,7 +737,7 @@ Forecasts 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diff --git a/tests/references/bugs_issue_94_issue_94.log b/tests/references/bugs_issue_94_issue_94.log index f00d82f0c..823d88e8b 100644 --- a/tests/references/bugs_issue_94_issue_94.log +++ b/tests/references/bugs_issue_94_issue_94.log @@ -1,51 +1,60 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 6.66628098487854 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 5.2231152057647705 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-01-25T00:00:00.000000 TimeMax=2016-11-01T00:00:00.000000 TimeDelta= Horizon=7 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=360 Min=-0.738918521113308 Max=29.153124490140325 Mean=13.876880092368264 StdDev=6.825680845870594 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=-0.738918521113308 Max=29.153124490140325 Mean=13.876880092368264 StdDev=6.825680845870594 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=360 Min=0.4398558475299983 Max=28.712057410405663 Mean=13.97942360807454 StdDev=6.708957948567105 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=0.4398558475299983 Max=28.712057410405663 Mean=13.97942360807454 StdDev=6.708957948567105 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [ConstantTrend + Seasonal_DayOfWeek + AR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.3419 MAPE_Forecast=0.0899 MAPE_Test=0.0542 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1509 SMAPE_Forecast=0.0939 SMAPE_Test=0.0563 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5328 MASE_Forecast=0.6185 MASE_Test=0.1743 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.180617368933476 L1_Forecast=1.4071786505722512 L1_Test=0.771959980327596 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.7902506647243357 L2_Forecast=1.771874339993418 L2_Test=0.8673710166358553 -INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2005 MAPE_Forecast=0.0754 MAPE_Test=0.1132 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1298 SMAPE_Forecast=0.0769 SMAPE_Test=0.1129 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4862 MASE_Forecast=0.5213 MASE_Test=0.3687 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.0719050996810775 L1_Forecast=1.1909224026515663 L1_Test=1.526619453200319 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.5065542292934542 L2_Forecast=1.4914549101132255 L2_Test=1.8235887091054894 +INFO:pyaf.std:MODEL_COMPLEXITY 68 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12.966785736447527 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_DayOfWeek -0.24650277143335586 {0: -2.2530107142724596, 1: -2.035069062564018, 2: -1.2208738358058708, 3: -0.8234559200919023, 4: 0.664113538758869, 5: 1.6992368766324413, 6: 2.829565147000066} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag21 0.7738540927625062 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5246873703125783 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.3551103152834102 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.34081677859194415 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2595009225896322 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag63 0.25158251287110733 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag28 0.1875667371334016 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag25 0.17872253838018284 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag33 -0.1742706013855944 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.15079887456572 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag1 0.5115291751319303 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag21 0.43142580708127914 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag22 -0.2644482725420876 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag13 -0.25298310779590827 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag42 0.24229211112227855 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag2 0.22646628175420228 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag38 -0.19147839507843975 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag7 -0.16756970128001047 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag34 0.16459931995166704 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag23 -0.15354627076406213 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 29.23277449607849 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.4071638584136963 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.95932388305664 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.9431297779083252 Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - '_Signal_ConstantTrend_residue_zeroCycle', - '_Signal_ConstantTrend_residue_zeroCycle_residue', - '_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)', - '_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)_residue', + '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek', + '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64)', + '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64)_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -56,5 +65,5 @@ Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '2017-01-21T00:00:00.000000000' '2017-01-22T00:00:00.000000000' '2017-01-23T00:00:00.000000000' '2017-01-24T00:00:00.000000000' '2017-01-25T00:00:00.000000000'] -[ 8.97490162 10.02838141 10.67064208 11.32039254 13.77889213 15.1670234 - 15.37103464] +[ 9.42903143 10.8281931 9.83790778 11.20939992 12.67746026 13.31901525 + 13.26762694] diff --git a/tests/references/bugs_run_benchmark_M1Comp_MNB71_18.log b/tests/references/bugs_run_benchmark_M1Comp_MNB71_18.log index 80a7a5daf..01f6bfc23 100644 --- a/tests/references/bugs_run_benchmark_M1Comp_MNB71_18.log +++ b/tests/references/bugs_run_benchmark_M1Comp_MNB71_18.log @@ -19,7 +19,7 @@ Data columns (total 2 columns): dtypes: float64(1), int64(1) memory usage: 2.8+ KB None -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'MNB71' 4.143050193786621 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['MNB71']' 2.690119504928589 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=0 TimeMax=81 TimeDelta=1 Horizon=18 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='MNB71' Length=121 Min=772.85 Max=881.66 Mean=843.0081818181818 StdDev=19.67635800809718 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_MNB71' Min=772.85 Max=881.66 Mean=843.0081818181818 StdDev=19.67635800809718 @@ -34,30 +34,39 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.1353 MASE_Forecast=1.2722 MASE_Test=1.5664 INFO:pyaf.std:MODEL_L1 L1_Fit=12.435963712076159 L1_Forecast=23.42995934959352 L1_Test=17.64001355013549 INFO:pyaf.std:MODEL_L2 L2_Fit=15.528034324573348 L2_Forecast=30.01182440245457 L2_Test=21.62576519037121 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 844.4701219512194 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _MNB71_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5196394920349121 +INFO:pyaf.std:START_FORECASTING '['MNB71']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['MNB71']' 0.5950026512145996 Split Transformation ... ForecastMAPE TestMAPE -0 None _MNB71 ... 2.030000e-02 1.390000e-02 -1 None _MNB71 ... 2.080000e-02 1.640000e-02 -2 None _MNB71 ... 2.210000e-02 1.940000e-02 -3 None _MNB71 ... 2.250000e-02 1.300000e-02 -4 None CumSum_MNB71 ... 2.250000e-02 1.300000e-02 +0 None _MNB71 ... 2.080000e-02 1.640000e-02 +1 None _MNB71 ... 2.080000e-02 1.810000e-02 +2 None CumSum_MNB71 ... 2.220000e-02 1.330000e-02 +3 None _MNB71 ... 2.230000e-02 1.350000e-02 +4 None _MNB71 ... 2.250000e-02 1.300000e-02 .. ... ... ... ... ... -59 None RelDiff_MNB71 ... 1.296901e+06 8.405692e+06 -60 None RelDiff_MNB71 ... 1.304037e+06 8.405692e+06 -61 None RelDiff_MNB71 ... 1.305705e+06 8.405692e+06 +59 None RelDiff_MNB71 ... 1.304037e+06 8.405692e+06 +60 None RelDiff_MNB71 ... 1.306489e+06 8.405692e+06 +61 None RelDiff_MNB71 ... 1.318573e+06 8.405692e+06 62 None RelDiff_MNB71 ... 1.328310e+06 8.405692e+06 63 None RelDiff_MNB71 ... 1.343878e+06 8.405692e+06 [64 rows x 8 columns] Split Transformation ... ForecastMAPE TestMAPE -0 None _MNB71 ... 0.0203 0.0139 -1 None _MNB71 ... 0.0208 0.0164 -2 None _MNB71 ... 0.0221 0.0194 -3 None _MNB71 ... 0.0225 0.0130 -4 None CumSum_MNB71 ... 0.0225 0.0130 +0 None _MNB71 ... 0.0208 0.0164 +1 None _MNB71 ... 0.0208 0.0181 +2 None CumSum_MNB71 ... 0.0222 0.0133 +3 None _MNB71 ... 0.0223 0.0135 +4 None _MNB71 ... 0.0225 0.0130 [5 rows x 8 columns] 18 0 823.89 @@ -101,7 +110,7 @@ Name: MNB71_Forecast, dtype: float64 FORECAST_DETAIL_ACTUAL M1_COMP MNB71 [823.89, 862.06, 854.67, 849.02, 840.59, 855.8, 837.77, 834.7, 846.68, 831.63, 848.74, 833.83, 855.49, 862.28, 874.14, 846.93, 891.1, 888.38] FORECAST_DETAIL_PREDICTED M1_COMP MNB71 [844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194, 844.4701219512194] BENCHMARK_PERF_DETAIL_SIGNAL_HORIZON M1_COMP MNB71 121 18 -BENCHMARK_PERF_DETAIL_BENCH_TIME_IN_SECONDS PYAF_SYSTEM_DEPENDENT_ M1_COMP MNB71 4.779381513595581 +BENCHMARK_PERF_DETAIL_BENCH_TIME_IN_SECONDS PYAF_SYSTEM_DEPENDENT_ M1_COMP MNB71 3.3827013969421387 BENCHMARK_PERF_DETAIL_BEST_MODEL M1_COMP MNB71 ConstantTrend + NoCycle + NoAR BENCHMARK_PERF_DETAIL_PERF_COUNT M1_COMP MNB71 18 BENCHMARK_PERF_DETAIL_PERF_MAPE_SMAPE_MASE M1_COMP MNB71 0.0171 0.0173 0.9327 diff --git a/tests/references/bugs_run_benchmark_M4Comp_ECONOMICS_ECON1151_18.log b/tests/references/bugs_run_benchmark_M4Comp_ECONOMICS_ECON1151_18.log index 8557933d0..5bb22343b 100644 --- a/tests/references/bugs_run_benchmark_M4Comp_ECONOMICS_ECON1151_18.log +++ b/tests/references/bugs_run_benchmark_M4Comp_ECONOMICS_ECON1151_18.log @@ -23,7 +23,7 @@ Data columns (total 2 columns): dtypes: float64(1), int64(1) memory usage: 1.0 KB None -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ECON1151' 4.93635892868042 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ECON1151']' 4.188233375549316 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=0 TimeMax=43 TimeDelta=1 Horizon=18 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ECON1151' Length=44 Min=10106446.0 Max=10326213.0 Mean=10233197.045454545 StdDev=60995.96677536615 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_ECON1151' Min=10106446.0 Max=10326213.0 Mean=10233197.045454545 StdDev=60995.96677536615 @@ -38,10 +38,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.8687 MASE_Forecast=1.8687 MASE_Test=1.8687 INFO:pyaf.std:MODEL_L1 L1_Fit=50364.26446281001 L1_Forecast=50364.26446281001 L1_Test=50364.26446281001 INFO:pyaf.std:MODEL_L2 L2_Fit=60995.96677536615 L2_Forecast=60995.96677536615 L2_Test=60995.96677536615 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 10233197.045454545 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _ECON1151_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.1073548793792725 +INFO:pyaf.std:START_FORECASTING '['ECON1151']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ECON1151']' 0.914431095123291 Split Transformation ... ForecastMAPE TestMAPE 0 None _ECON1151 ... 0.0016 0.0016 1 None _ECON1151 ... 0.0016 0.0016 @@ -105,7 +114,7 @@ Name: ECON1151_Forecast, dtype: float64 FORECAST_DETAIL_ACTUAL ECON1151 ECON1151 [10262695.0, 10269501.0, 10214939.0, 10229278.0, 10295400.0, 10269948.0, 10291118.0, 10305980.0, 10263024.0, 10253247.0, 10252520.0, 10262676.0, 10331265.0, 10343289.0, 10301749.0, 10309781.0, 10347691.0, 10338706.0] FORECAST_DETAIL_PREDICTED ECON1151 ECON1151 [10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545, 10233197.045454545] BENCHMARK_PERF_DETAIL_SIGNAL_HORIZON ECON1151 ECON1151 44 18 -BENCHMARK_PERF_DETAIL_BENCH_TIME_IN_SECONDS PYAF_SYSTEM_DEPENDENT_ ECON1151 ECON1151 6.198461294174194 +BENCHMARK_PERF_DETAIL_BENCH_TIME_IN_SECONDS PYAF_SYSTEM_DEPENDENT_ ECON1151 ECON1151 5.289479970932007 BENCHMARK_PERF_DETAIL_BEST_MODEL ECON1151 ECON1151 ConstantTrend + NoCycle + NoAR BENCHMARK_PERF_DETAIL_PERF_COUNT ECON1151 ECON1151 18 BENCHMARK_PERF_DETAIL_PERF_MAPE_SMAPE_MASE ECON1151 ECON1151 0.0053 0.0054 2.105 diff --git a/tests/references/bugs_run_benchmark_Yahoo_nysecomp_VRS.log b/tests/references/bugs_run_benchmark_Yahoo_nysecomp_VRS.log index 9ddb014c1..4e57714f6 100644 --- a/tests/references/bugs_run_benchmark_Yahoo_nysecomp_VRS.log +++ b/tests/references/bugs_run_benchmark_Yahoo_nysecomp_VRS.log @@ -1573,7 +1573,7 @@ Data columns (total 2 columns): dtypes: datetime64[ns](1), float64(1) memory usage: 120.0 bytes None -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VRS' 1.64656662940979 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['VRS']' 1.327857255935669 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-07-20T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=1 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='VRS' Length=5 Min=11.53 Max=11.8 Mean=11.644 StdDev=0.11074294559925753 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_VRS' Min=11.53 Max=11.8 Mean=11.644 StdDev=0.11074294559925753 @@ -1588,10 +1588,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.5526 MASE_Forecast=1.5526 MASE_Test=1.5526 INFO:pyaf.std:MODEL_L1 L1_Fit=0.10480000000000053 L1_Forecast=0.10480000000000053 L1_Test=0.10480000000000053 INFO:pyaf.std:MODEL_L2 L2_Fit=0.11074294559925753 L2_Forecast=0.11074294559925753 L2_Test=0.11074294559925753 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 11.644 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _VRS_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.06781673431396484 +INFO:pyaf.std:START_FORECASTING '['VRS']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['VRS']' 0.04795575141906738 Split Transformation ... ForecastMAPE TestMAPE 0 None Diff_VRS ... 0.0030 0.0030 1 None Diff_VRS ... 0.0030 0.0030 @@ -1642,7 +1651,7 @@ Name: VRS_Forecast, dtype: float64 FORECAST_DETAIL_ACTUAL Yahoo_Stock_Price_VRS VRS [11.55] FORECAST_DETAIL_PREDICTED Yahoo_Stock_Price_VRS VRS [11.644] BENCHMARK_PERF_DETAIL_SIGNAL_HORIZON Yahoo_Stock_Price_VRS VRS 5 1 -BENCHMARK_PERF_DETAIL_BENCH_TIME_IN_SECONDS PYAF_SYSTEM_DEPENDENT_ Yahoo_Stock_Price_VRS VRS 1.855614423751831 +BENCHMARK_PERF_DETAIL_BENCH_TIME_IN_SECONDS PYAF_SYSTEM_DEPENDENT_ Yahoo_Stock_Price_VRS VRS 1.490490436553955 BENCHMARK_PERF_DETAIL_BEST_MODEL Yahoo_Stock_Price_VRS VRS ConstantTrend + NoCycle + NoAR BENCHMARK_PERF_DETAIL_PERF_COUNT Yahoo_Stock_Price_VRS VRS 1 BENCHMARK_PERF_DETAIL_PERF_MAPE_SMAPE_MASE Yahoo_Stock_Price_VRS VRS 0.0081 0.0081 None diff --git a/tests/references/bugs_test_artificial_bug_1.log b/tests/references/bugs_test_artificial_bug_1.log index 2c7e32fc9..74bb25fa7 100644 --- a/tests/references/bugs_test_artificial_bug_1.log +++ b/tests/references/bugs_test_artificial_bug_1.log @@ -1,38 +1,46 @@ INFO:pyaf.std:START_TRAINING 'Signal_0.01' GENERATING_RANDOM_DATASET Signal_200_D_0_linear_48_exp_4.0_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_0.01' 7.158754587173462 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_0.01']' 5.318541526794434 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-05-29T00:00:00.000000 TimeDelta= Horizon=6 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_0.01' Length=194 Min=-0.030051272969503103 Max=0.3790431865671391 Mean=0.0037588225755744212 StdDev=0.03152183563553583 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Signal_0.01' Min=-0.03458639563011951 Max=0.8047266172901858 Mean=0.5490647319866144 StdDev=0.24941213189630643 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' -INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_Signal_0.01_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'CumSum_Signal_0.01_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'CumSum_Signal_0.01_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_Signal_0.01_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=10.8027 MAPE_Forecast=1.0 MAPE_Test=1.0 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=2.0 SMAPE_Forecast=2.0 SMAPE_Test=2.0 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7912 MASE_Forecast=0.6027 MASE_Test=0.6188 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.016429470633415266 L1_Forecast=0.008805361456829334 L1_Test=0.008001206814759042 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.05360206412246399 L2_Forecast=0.010839991946644947 L2_Test=0.009714089392457147 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_0.01' Min=-0.030051272969503103 Max=0.3790431865671391 Mean=0.0037588225755744212 StdDev=0.03152183563553583 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_0.01_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [ConstantTrend + Seasonal_WeekOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_0.01_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_0.01_ConstantTrend_residue_Seasonal_WeekOfYear' [Seasonal_WeekOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_0.01_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.3358 MAPE_Forecast=0.9378 MAPE_Test=1.0374 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.2542 SMAPE_Forecast=1.5955 SMAPE_Test=1.8731 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5889 MASE_Forecast=0.6301 MASE_Test=0.6336 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.012228371601526438 L1_Forecast=0.009206860426789386 L1_Test=0.008192618596285658 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.035001856825545505 L2_Forecast=0.011934980157735916 L2_Test=0.009799127888827128 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.004773151918961902 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_0.01_ConstantTrend_residue_Seasonal_WeekOfYear -0.0041989165743820545 {52: -0.003529115739236197, 1: -0.008936233241325494, 2: -0.006903408196589401, 3: 0.0009751926932189681, 4: -0.010815593464080647, 5: -6.119049768871752e-05, 6: -0.005555603101060069, 7: -0.00654711175886354, 8: -0.00633156345047464, 9: -0.0023752791193937424, 10: -0.002365302340723449, 11: -0.0020948527822473744, 12: -0.003511740371246553, 13: 0.0074777215238977955, 14: -0.003997056666728091, 15: -0.005723954824018522, 16: -0.002723090315232298, 17: -0.005292681442534026, 18: -0.01216511291277573, 19: -0.014166622159726042, 20: -0.004407430447453156, 21: -0.0009490464454970592, 22: -0.011904669170694524} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.45119333267211914 -Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', - 'CumSum_Signal_0.01', 'CumSum_Signal_0.01_ConstantTrend', - 'CumSum_Signal_0.01_ConstantTrend_residue', - 'CumSum_Signal_0.01_ConstantTrend_residue_zeroCycle', - 'CumSum_Signal_0.01_ConstantTrend_residue_zeroCycle_residue', - 'CumSum_Signal_0.01_ConstantTrend_residue_zeroCycle_residue_NoAR', - 'CumSum_Signal_0.01_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', - 'CumSum_Signal_0.01_Trend', 'CumSum_Signal_0.01_Trend_residue', - 'CumSum_Signal_0.01_Cycle', 'CumSum_Signal_0.01_Cycle_residue', - 'CumSum_Signal_0.01_AR', 'CumSum_Signal_0.01_AR_residue', - 'CumSum_Signal_0.01_TransformedForecast', 'Signal_0.01_Forecast', - 'CumSum_Signal_0.01_TransformedResidue', 'Signal_0.01_Residue', - 'Signal_0.01_Forecast_Lower_Bound', 'Signal_0.01_Forecast_Upper_Bound'], +INFO:pyaf.std:START_FORECASTING '['Signal_0.01']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_0.01']' 0.13729023933410645 +Forecast Columns Index(['Date', 'Signal_0.01', 'row_number', 'Date_Normalized', '_Signal_0.01', + '_Signal_0.01_ConstantTrend', '_Signal_0.01_ConstantTrend_residue', + '_Signal_0.01_ConstantTrend_residue_Seasonal_WeekOfYear', + '_Signal_0.01_ConstantTrend_residue_Seasonal_WeekOfYear_residue', + '_Signal_0.01_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR', + '_Signal_0.01_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR_residue', + '_Signal_0.01_Trend', '_Signal_0.01_Trend_residue', + '_Signal_0.01_Cycle', '_Signal_0.01_Cycle_residue', '_Signal_0.01_AR', + '_Signal_0.01_AR_residue', '_Signal_0.01_TransformedForecast', + 'Signal_0.01_Forecast', '_Signal_0.01_TransformedResidue', + 'Signal_0.01_Residue', 'Signal_0.01_Forecast_Lower_Bound', + 'Signal_0.01_Forecast_Upper_Bound'], dtype='object') RangeIndex: 200 entries, 0 to 199 @@ -47,47 +55,49 @@ Data columns (total 5 columns): dtypes: datetime64[ns](1), float64(4) memory usage: 7.9 KB Forecasts - [[Timestamp('2000-07-13 00:00:00') nan 0.0 -0.021246384215424095 - 0.021246384215424095] - [Timestamp('2000-07-14 00:00:00') nan 0.0 -0.021246384215424095 - 0.021246384215424095] - [Timestamp('2000-07-15 00:00:00') nan 0.0 -0.021246384215424095 - 0.021246384215424095] - [Timestamp('2000-07-16 00:00:00') nan 0.0 -0.021246384215424095 - 0.021246384215424095] - [Timestamp('2000-07-17 00:00:00') nan 0.0 -0.021246384215424095 - 0.021246384215424095] - [Timestamp('2000-07-18 00:00:00') nan 0.0 -0.021246384215424095 - 0.021246384215424095]] + [[Timestamp('2000-07-13 00:00:00') nan 0.0005742353445798475 + -0.022818325764582547 0.02396679645374224] + [Timestamp('2000-07-14 00:00:00') nan 0.0005742353445798475 + -0.022818325764582547 0.02396679645374224] + [Timestamp('2000-07-15 00:00:00') nan 0.0005742353445798475 + -0.022818325764582547 0.02396679645374224] + [Timestamp('2000-07-16 00:00:00') nan 0.0005742353445798475 + -0.022818325764582547 0.02396679645374224] + [Timestamp('2000-07-17 00:00:00') nan 0.0005742353445798475 + -0.022818325764582547 0.02396679645374224] + [Timestamp('2000-07-18 00:00:00') nan 0.0005742353445798475 + -0.022818325764582547 0.02396679645374224]] { - "Dataset": { - "Signal": "Signal_0.01", - "Time": { - "Horizon": 6, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-07-12 00:00:00" - ], - "TimeVariable": "Date" + "Signal_0.01": { + "Dataset": { + "Signal": "Signal_0.01", + "Time": { + "Horizon": 6, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-07-12 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 194 }, - "Training_Signal_Length": 194 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Signal_0.01_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "Integration", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "0.008805361456829334", - "MAPE": "1.0", - "MASE": "0.6027", - "RMSE": "0.010839991946644947" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_0.01_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR", + "Cycle": "Seasonal_WeekOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.009206860426789386", + "MAPE": "0.9378", + "MASE": "0.6301", + "RMSE": "0.011934980157735916" + } } } @@ -96,7 +106,7 @@ Forecasts -{"Date":{"188":"2000-07-07T00:00:00.000Z","189":"2000-07-08T00:00:00.000Z","190":"2000-07-09T00:00:00.000Z","191":"2000-07-10T00:00:00.000Z","192":"2000-07-11T00:00:00.000Z","193":"2000-07-12T00:00:00.000Z","194":"2000-07-13T00:00:00.000Z","195":"2000-07-14T00:00:00.000Z","196":"2000-07-15T00:00:00.000Z","197":"2000-07-16T00:00:00.000Z","198":"2000-07-17T00:00:00.000Z","199":"2000-07-18T00:00:00.000Z"},"Signal_0.01":{"188":-0.0029397299,"189":-0.0055880024,"190":0.017482737,"191":0.0030476687,"192":-0.0134648388,"193":-0.0054842641,"194":null,"195":null,"196":null,"197":null,"198":null,"199":null},"Signal_0.01_Forecast":{"188":0.0,"189":0.0,"190":0.0,"191":0.0,"192":0.0,"193":0.0,"194":0.0,"195":0.0,"196":0.0,"197":0.0,"198":0.0,"199":0.0},"Signal_0.01_Forecast_Lower_Bound":{"188":null,"189":null,"190":null,"191":null,"192":null,"193":null,"194":-0.0212463842,"195":-0.0212463842,"196":-0.0212463842,"197":-0.0212463842,"198":-0.0212463842,"199":-0.0212463842},"Signal_0.01_Forecast_Upper_Bound":{"188":null,"189":null,"190":null,"191":null,"192":null,"193":null,"194":0.0212463842,"195":0.0212463842,"196":0.0212463842,"197":0.0212463842,"198":0.0212463842,"199":0.0212463842}} +{"Date":{"188":"2000-07-07T00:00:00.000Z","189":"2000-07-08T00:00:00.000Z","190":"2000-07-09T00:00:00.000Z","191":"2000-07-10T00:00:00.000Z","192":"2000-07-11T00:00:00.000Z","193":"2000-07-12T00:00:00.000Z","194":"2000-07-13T00:00:00.000Z","195":"2000-07-14T00:00:00.000Z","196":"2000-07-15T00:00:00.000Z","197":"2000-07-16T00:00:00.000Z","198":"2000-07-17T00:00:00.000Z","199":"2000-07-18T00:00:00.000Z"},"Signal_0.01":{"188":-0.0029397299,"189":-0.0055880024,"190":0.017482737,"191":0.0030476687,"192":-0.0134648388,"193":-0.0054842641,"194":null,"195":null,"196":null,"197":null,"198":null,"199":null},"Signal_0.01_Forecast":{"188":0.0005742353,"189":0.0005742353,"190":0.0005742353,"191":0.0005742353,"192":0.0005742353,"193":0.0005742353,"194":0.0005742353,"195":0.0005742353,"196":0.0005742353,"197":0.0005742353,"198":0.0005742353,"199":0.0005742353},"Signal_0.01_Forecast_Lower_Bound":{"188":null,"189":null,"190":null,"191":null,"192":null,"193":null,"194":-0.0228183258,"195":-0.0228183258,"196":-0.0228183258,"197":-0.0228183258,"198":-0.0228183258,"199":-0.0228183258},"Signal_0.01_Forecast_Upper_Bound":{"188":null,"189":null,"190":null,"191":null,"192":null,"193":null,"194":0.0239667965,"195":0.0239667965,"196":0.0239667965,"197":0.0239667965,"198":0.0239667965,"199":0.0239667965}} diff --git a/tests/references/bugs_test_artificial_bug_2.log b/tests/references/bugs_test_artificial_bug_2.log index 62dcd9501..ce1ce2718 100644 --- a/tests/references/bugs_test_artificial_bug_2.log +++ b/tests/references/bugs_test_artificial_bug_2.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal' GENERATING_RANDOM_DATASET Signal_40_D_0_linear_4_exp_2.0_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 3.784379482269287 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 2.4260094165802 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-28T00:00:00.000000 TimeDelta= Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=38 Min=1.7657102120073388e-06 Max=0.36787944117144233 Mean=0.019575897831234024 StdDev=0.061803237333687915 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Signal' Min=0.0015475103000363044 Max=0.7438841175868927 Mean=0.28813068843628037 StdDev=0.26411610602621793 @@ -16,10 +16,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.607 MASE_Forecast=0.4537 MASE_Test=0.5029 INFO:pyaf.std:MODEL_L1 L1_Fit=0.026852326897330053 L1_Forecast=0.015957339833153502 L1_Test=0.006871036776974278 INFO:pyaf.std:MODEL_L2 L2_Fit=0.07858267686770308 L2_Forecast=0.03426256134651989 L2_Test=0.009689466084927443 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.15247684835759795 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.1820368766784668 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.07519054412841797 Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', 'CumSum_Signal', 'CumSum_Signal_ConstantTrend', 'CumSum_Signal_ConstantTrend_residue', 'CumSum_Signal_ConstantTrend_residue_zeroCycle', @@ -51,31 +60,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 2, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-02-07 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 2, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-02-07 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 38 }, - "Training_Signal_Length": 38 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "Integration", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "0.015957339833153502", - "MAPE": "1.0", - "MASE": "0.4537", - "RMSE": "0.03426256134651989" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "Integration", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "0.015957339833153502", + "MAPE": "1.0", + "MASE": "0.4537", + "RMSE": "0.03426256134651989" + } } } diff --git a/tests/references/bugs_test_random_exogenous.log b/tests/references/bugs_test_random_exogenous.log index b608801cf..a9317a894 100644 --- a/tests/references/bugs_test_random_exogenous.log +++ b/tests/references/bugs_test_random_exogenous.log @@ -1,35 +1,44 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 162.36153388023376 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 146.6550145149231 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-04-29T00:00:00.000000 TimeDelta= Horizon=5 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=155 Min=1.0 Max=8.098847796172965 Mean=4.528034104498642 StdDev=2.0623039863566244 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.098847796172965 Mean=4.528034104498642 StdDev=2.0623039863566244 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0238 MAPE_Forecast=0.0238 MAPE_Test=0.0188 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0238 SMAPE_Forecast=0.0229 SMAPE_Test=0.0191 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0355 MASE_Forecast=0.0289 MASE_Test=0.0731 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08329903901249791 L1_Forecast=0.07262014605762628 L1_Test=0.10056649473482934 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10085236046502419 L2_Forecast=0.09339669151208699 L2_Test=0.12540994183796092 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0234 MAPE_Forecast=0.0229 MAPE_Test=0.0192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0235 SMAPE_Forecast=0.0222 SMAPE_Test=0.0195 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.035 MASE_Forecast=0.028 MASE_Test=0.0723 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.0821585469637092 L1_Forecast=0.07037192599828514 L1_Test=0.09951083231862831 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.1025401472147582 L2_Forecast=0.0921671329317167 L2_Test=0.120297538198616 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.546931469497249 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 12 0.3699275058108773 {0: -2.500646781813473, 1: 0.8378174030122043, 2: 2.4968499159485216, 3: -2.499444584318911, 4: -0.812522375903721, 5: -3.3230283573989254, 6: -1.7173721969774531, 7: 0.8327733061764464, 8: -0.0288482457218322, 9: 1.6609541658000948, 10: 1.644447272460039, 11: 3.35464847678832} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 35.47744798660278 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.35321569442749023 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 16.944348573684692 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.15911173820495605 GENERATING_RANDOM_DATASET Signal_160_D_0_constant_12_None_0.1_1280 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 @@ -48,10 +57,10 @@ dtypes: datetime64[ns](1), float64(4), int64(2) memory usage: 8.6 KB Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -70,40 +79,42 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 3.9 KB None Forecasts - [[Timestamp('2000-06-04 00:00:00') nan 7.886263461149813] - [Timestamp('2000-06-05 00:00:00') nan 2.069104559524298] - [Timestamp('2000-06-06 00:00:00') nan 5.406301613192547] - [Timestamp('2000-06-07 00:00:00') nan 7.0338123319113635] - [Timestamp('2000-06-08 00:00:00') nan 2.054537376090997]] + [[Timestamp('2000-06-04 00:00:00') nan 7.901579946285569] + [Timestamp('2000-06-05 00:00:00') nan 2.0462846876837757] + [Timestamp('2000-06-06 00:00:00') nan 5.384748872509453] + [Timestamp('2000-06-07 00:00:00') nan 7.0437813854457705] + [Timestamp('2000-06-08 00:00:00') nan 2.047486885178338]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 5, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-06-03 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 5, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-06-03 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 155 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 155 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.07262014605762628", - "MAPE": "0.0238", - "MASE": "0.0289", - "RMSE": "0.09339669151208699" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.07037192599828514", + "MAPE": "0.0229", + "MASE": "0.028", + "RMSE": "0.0921671329317167" + } } } @@ -112,7 +123,7 @@ Forecasts -{"Date":{"150":"2000-05-30T00:00:00.000Z","151":"2000-05-31T00:00:00.000Z","152":"2000-06-01T00:00:00.000Z","153":"2000-06-02T00:00:00.000Z","154":"2000-06-03T00:00:00.000Z","155":"2000-06-04T00:00:00.000Z","156":"2000-06-05T00:00:00.000Z","157":"2000-06-06T00:00:00.000Z","158":"2000-06-07T00:00:00.000Z","159":"2000-06-08T00:00:00.000Z"},"Signal":{"150":2.8643781404,"151":5.547541439,"152":4.6430085728,"153":6.3732594278,"154":6.1867792531,"155":null,"156":null,"157":null,"158":null,"159":null},"Signal_Forecast":{"150":2.8377598296,"151":5.3704193129,"152":4.5418209542,"153":6.1828318887,"154":6.1793023741,"155":7.8862634611,"156":2.0691045595,"157":5.4063016132,"158":7.0338123319,"159":2.0545373761}} +{"Date":{"150":"2000-05-30T00:00:00.000Z","151":"2000-05-31T00:00:00.000Z","152":"2000-06-01T00:00:00.000Z","153":"2000-06-02T00:00:00.000Z","154":"2000-06-03T00:00:00.000Z","155":"2000-06-04T00:00:00.000Z","156":"2000-06-05T00:00:00.000Z","157":"2000-06-06T00:00:00.000Z","158":"2000-06-07T00:00:00.000Z","159":"2000-06-08T00:00:00.000Z"},"Signal":{"150":2.8643781404,"151":5.547541439,"152":4.6430085728,"153":6.3732594278,"154":6.1867792531,"155":null,"156":null,"157":null,"158":null,"159":null},"Signal_Forecast":{"150":2.8295592725,"151":5.3797047757,"152":4.5180832238,"153":6.2078856353,"154":6.191378742,"155":7.9015799463,"156":2.0462846877,"157":5.3847488725,"158":7.0437813854,"159":2.0474868852}} diff --git a/tests/references/cross_validation_test_air_passengers_cross_valid.log b/tests/references/cross_validation_test_air_passengers_cross_valid.log index 098e9dd99..c70b1836c 100644 --- a/tests/references/cross_validation_test_air_passengers_cross_valid.log +++ b/tests/references/cross_validation_test_air_passengers_cross_valid.log @@ -1,70 +1,69 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'AirPassengers' (0.5, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'AirPassengers' (0.5, 0.1, 0.0) 2.658370018005371 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'AirPassengers' (0.6000000000000001, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'AirPassengers' (0.6000000000000001, 0.1, 0.0) 2.695525646209717 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'AirPassengers' (0.7000000000000001, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'AirPassengers' (0.7000000000000001, 0.1, 0.0) 2.757091999053955 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'AirPassengers' (0.8, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'AirPassengers' (0.8, 0.1, 0.0) 2.748607873916626 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'AirPassengers' (0.9, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'AirPassengers' (0.9, 0.1, 0.0) 2.7497167587280273 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_TIME_IN_SECONDS _AirPassengers 13.623807191848755 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 13.865517854690552 -INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1954.41666666667 TimeDelta=0.08333333333338347 Horizon=12 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.018378973007202 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0438 MAPE_Forecast=0.0386 MAPE_Test=None -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0437 SMAPE_Forecast=0.0391 SMAPE_Test=None -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4861 MASE_Forecast=0.4656 MASE_Test=None -INFO:pyaf.std:MODEL_L1 L1_Fit=7.164469425273994 L1_Forecast=10.786413393505907 L1_Test=None -INFO:pyaf.std:MODEL_L2 L2_Fit=8.942766451419972 L2_Forecast=12.324512306852768 L2_Test=None -INFO:pyaf.std:MODEL_COMPLEXITY 16 +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0349 MAPE_Forecast=0.0217 MAPE_Test=0.0541 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0346 SMAPE_Forecast=0.022 SMAPE_Test=0.0558 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3563 MASE_Forecast=0.2287 MASE_Test=0.495 +INFO:pyaf.std:MODEL_L1 L1_Fit=6.6616086574266005 L1_Forecast=8.471185219451263 L1_Test=22.27708217077866 +INFO:pyaf.std:MODEL_L2 L2_Fit=8.553917012084607 L2_Forecast=11.971762904662638 L2_Test=23.59123159802204 +INFO:pyaf.std:MODEL_COMPLEXITY 40 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 176.57575757575756 +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (114.90523344816275, array([197.60619977])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.834574412746363 -INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7123718250165114 -INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.4018787406298 -INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.3896147658999262 -INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.34965907231433074 -INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.2355216649654528 -INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag15 0.15763860782410244 -INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag22 0.13898102966856776 -INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.12616114892681568 -INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 0.10846582203594723 +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag1 0.7786311442771912 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag12 0.7393757053683319 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag13 -0.5331389013991886 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag25 -0.2876891887152461 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag24 0.24302785675794508 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag33 -0.18406731004063412 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag5 0.17161844385149028 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag29 -0.16896686902773 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16816626466278906 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag16 -0.16495284212978803 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.077415227890015 -INFO:pyaf.std:START_FORECASTING 'AirPassengers' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'AirPassengers' 0.2257215976715088 - Split Transformation ... TestMAPE IC -0 (0.5, 0.1, 0.0) _AirPassengers ... None 0 -1 (0.5, 0.1, 0.0) Diff_AirPassengers ... None 0 -2 (0.5, 0.1, 0.0) Diff_AirPassengers ... None 0 -3 (0.5, 0.1, 0.0) CumSum_AirPassengers ... None 0 -4 (0.5, 0.1, 0.0) CumSum_AirPassengers ... None 0 +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 16.082287311553955 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.5007338523864746 + Split Transformation ... TestMAPE IC +0 None Diff_AirPassengers ... 0.0402 1 +1 None _AirPassengers ... 0.0541 1 +2 None _AirPassengers ... 0.0541 1 +3 None Diff_AirPassengers ... 0.0296 1 +4 None _AirPassengers ... 0.0281 1 [5 rows x 9 columns] Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', - '_AirPassengers', '_AirPassengers_ConstantTrend', - '_AirPassengers_ConstantTrend_residue', - '_AirPassengers_ConstantTrend_residue_zeroCycle', - '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', - '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', - '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', + '_AirPassengers', '_AirPassengers_LinearTrend', + '_AirPassengers_LinearTrend_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)_residue', '_AirPassengers_Trend', '_AirPassengers_Trend_residue', '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', '_AirPassengers_AR', '_AirPassengers_AR_residue', @@ -100,49 +99,51 @@ Forecasts 129 1959.750000 ... NaN 130 1959.833333 ... NaN 131 1959.916667 ... NaN -132 1960.000000 ... 442.789238 -133 1960.083333 ... 422.808818 -134 1960.166667 ... 486.750543 -135 1960.250000 ... 499.056189 -136 1960.333333 ... 600.338129 -137 1960.416667 ... 806.361411 -138 1960.500000 ... 1157.254012 -139 1960.583333 ... 1581.582987 -140 1960.666667 ... 1953.092674 -141 1960.750000 ... 2654.672484 -142 1960.833333 ... 3604.944845 -143 1960.916667 ... 4758.794175 +132 1960.000000 ... 427.657421 +133 1960.083333 ... 399.424006 +134 1960.166667 ... 456.765653 +135 1960.250000 ... 512.347708 +136 1960.333333 ... 753.238798 +137 1960.416667 ... 1208.760036 +138 1960.500000 ... 2050.507020 +139 1960.583333 ... 3376.269807 +140 1960.666667 ... 4967.708813 +141 1960.750000 ... 5799.430189 +142 1960.833333 ... 5352.171549 +143 1960.916667 ... 7721.072427 [24 rows x 5 columns] { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "16", - "MAE": "10.786413393505907", - "MAPE": "0.0386", - "MASE": "0.4656", - "RMSE": "12.324512306852768" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "40", + "MAE": "8.471185219451263", + "MAPE": "0.0217", + "MASE": "0.2287", + "RMSE": "11.971762904662638" + } } } @@ -151,7 +152,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":354.9469969313,"121":332.8604972552,"122":392.5980383163,"123":389.5267157165,"124":400.7194169386,"125":488.0646390606,"126":526.3735128031,"127":550.8961466883,"128":448.139293608,"129":395.0985133281,"130":348.3937628617,"131":377.6799675279,"132":418.6331936292,"133":383.6588130987,"134":436.6839840551,"135":423.9458952184,"136":441.4548124051,"137":509.3634757547,"138":577.0987731586,"139":598.2130693945,"140":480.2004111087,"141":422.6921148294,"142":368.8873038151,"143":409.2363803474},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":394.4771495077,"133":344.5088079526,"134":386.617424793,"135":348.8356018004,"136":282.5714954632,"137":212.3655408066,"138":-3.056465859,"139":-385.1568477769,"140":-992.6918520569,"141":-1809.2882546048,"142":-2867.1702370842,"143":-3940.321414229},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":442.7892377506,"133":422.8088182448,"134":486.7505433173,"135":499.0561886364,"136":600.3381293469,"137":806.3614107027,"138":1157.2540121762,"139":1581.582986566,"140":1953.0926742743,"141":2654.6724842637,"142":3604.9448447144,"143":4758.7941749237}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":336.76433931,"121":322.8158768976,"122":370.1970011391,"123":378.032857026,"124":398.9335945668,"125":490.9916896704,"126":527.6050111738,"127":547.2692898967,"128":447.2569355178,"129":389.1498397716,"130":336.5874463251,"131":365.0545119965,"132":404.1927658782,"133":362.2723575753,"134":407.3659989041,"135":392.3773701384,"136":426.0082038065,"137":494.6325800918,"138":561.1241745773,"139":558.3950905274,"140":437.9865222534,"141":374.1309595368,"142":319.9068836751,"143":355.980727},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":380.728110585,"133":325.1207093518,"134":357.9663450975,"135":272.4070322436,"136":98.7776096737,"137":-219.4948761306,"138":-928.2586710993,"139":-2259.4796258236,"140":-4091.7357686502,"141":-5051.1682697064,"142":-4712.357781742,"143":-7009.1109734824},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":427.6574211713,"133":399.4240057988,"134":456.7656527106,"135":512.3477080331,"136":753.2387979394,"137":1208.7600363143,"138":2050.507020254,"139":3376.2698068785,"140":4967.708813157,"141":5799.43018878,"142":5352.1715490921,"143":7721.0724274824}} diff --git a/tests/references/cross_validation_test_ozone_cross_valid.log b/tests/references/cross_validation_test_ozone_cross_valid.log index 695f7a622..e620db1c5 100644 --- a/tests/references/cross_validation_test_ozone_cross_valid.log +++ b/tests/references/cross_validation_test_ozone_cross_valid.log @@ -1,75 +1,74 @@ INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.5, 0.1, 0.0) Month Ozone Time 0 1955-01 2.7 1955-01-01 1 1955-02 2.0 1955-02-01 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.5, 0.1, 0.0) 3.0824809074401855 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.6000000000000001, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.6000000000000001, 0.1, 0.0) 4.155992746353149 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.7000000000000001, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.7000000000000001, 0.1, 0.0) 4.178727865219116 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.8, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.8, 0.1, 0.0) 4.988113164901733 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.9, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.9, 0.1, 0.0) 6.388055324554443 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_TIME_IN_SECONDS _Ozone 22.80823802947998 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 23.19182515144348 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1968-07-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.846750974655151 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)' [PolyTrend + Cycle_None + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_bestCycle_byMAPE' [Cycle_None] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1581 MAPE_Forecast=0.1382 MAPE_Test=None -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1491 SMAPE_Forecast=0.1376 SMAPE_Test=None -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6784 MASE_Forecast=0.5719 MASE_Test=None -INFO:pyaf.std:MODEL_L1 L1_Fit=0.5946639323035057 L1_Forecast=0.46050213610999613 L1_Test=None -INFO:pyaf.std:MODEL_L2 L2_Fit=0.7992783835359704 L2_Forecast=0.5988849665280183 L2_Test=None -INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 +INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.1221874309598405, array([-2.38954202, -0.38841589, 1.03620791])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Ozone_PolyTrend_residue_bestCycle_byMAPE None -0.036022548290779 {} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag1 0.4016363567221183 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag10 0.1863306116230097 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag12 0.1753142557903944 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag36 0.17375658201440342 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag7 -0.16469315893917696 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag30 0.14800975722350934 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag20 -0.1188727499892588 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag4 0.10707580719819523 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag48 0.10312520062123998 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag35 -0.1029173113587088 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.306308269500732 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3672220706939697 - Split Transformation ... TestMAPE IC -0 (0.5, 0.1, 0.0) _Ozone ... None 0 -1 (0.5, 0.1, 0.0) _Ozone ... None 0 -2 (0.5, 0.1, 0.0) _Ozone ... None 0 -3 (0.5, 0.1, 0.0) _Ozone ... None 0 -4 (0.5, 0.1, 0.0) _Ozone ... None 0 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 17.26258611679077 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5227169990539551 + Split Transformation ... TestMAPE IC +0 None _Ozone ... 0.1740 1 +1 None _Ozone ... 0.1740 1 +2 None _Ozone ... 0.3430 1 +3 None _Ozone ... 0.3430 1 +4 None _Ozone ... 0.2209 0 [5 rows x 9 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', - 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', - '_Ozone_PolyTrend_residue', '_Ozone_PolyTrend_residue_bestCycle_byMAPE', - '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue', - '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)', - '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)_residue', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_zeroCycle', + '_Ozone_LinearTrend_residue_zeroCycle_residue', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -89,47 +88,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 1.184938 -205 1972-02-01 NaN 2.491261 -206 1972-03-01 NaN 3.010337 -207 1972-04-01 NaN 3.587059 -208 1972-05-01 NaN 3.825771 -209 1972-06-01 NaN 4.381998 -210 1972-07-01 NaN 4.433500 -211 1972-08-01 NaN 4.790043 -212 1972-09-01 NaN 4.165712 -213 1972-10-01 NaN 3.423718 -214 1972-11-01 NaN 2.645356 -215 1972-12-01 NaN 2.235572 +204 1972-01-01 NaN 0.611147 +205 1972-02-01 NaN 1.626529 +206 1972-03-01 NaN 1.942209 +207 1972-04-01 NaN 2.369673 +208 1972-05-01 NaN 2.663022 +209 1972-06-01 NaN 3.248702 +210 1972-07-01 NaN 3.220270 +211 1972-08-01 NaN 3.329387 +212 1972-09-01 NaN 2.996846 +213 1972-10-01 NaN 2.118734 +214 1972-11-01 NaN 1.332600 +215 1972-12-01 NaN 0.841575 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)", - "Cycle": "Cycle_None", - "Signal_Transoformation": "NoTransf", - "Trend": "PolyTrend" - }, - "Model_Performance": { - "COMPLEXITY": "64", - "MAE": "0.46050213610999613", - "MAPE": "0.1382", - "MASE": "0.5719", - "RMSE": "0.5988849665280183" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } @@ -138,7 +139,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.5885638245,"193":2.3763922007,"194":3.1784235327,"195":2.5454211461,"196":3.7391670935,"197":3.4095003778,"198":4.8168231096,"199":3.8658794356,"200":3.6105365666,"201":2.998160286,"202":2.2220259921,"203":1.5897393659,"204":1.1849376732,"205":2.4912613567,"206":3.0103373659,"207":3.5870593834,"208":3.8257708791,"209":4.3819982103,"210":4.4334998784,"211":4.7900429376,"212":4.1657122589,"213":3.4237182368,"214":2.64535624,"215":2.2355719979}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0201234776,"193":1.9477080322,"194":2.8071877532,"195":1.9810299731,"196":3.2711119481,"197":3.0180455521,"198":4.2391631176,"199":3.363140279,"200":2.9406052694,"201":2.3736934134,"202":1.5680019494,"203":1.0535599086,"204":0.6111465642,"205":1.6265294603,"206":1.9422085586,"207":2.3696733129,"208":2.6630222018,"209":3.2487020879,"210":3.220269503,"211":3.32938729,"212":2.996846273,"213":2.1187344328,"214":1.3326002972,"215":0.8415747446}} diff --git a/tests/references/cross_validation_test_ozone_custom_split.log b/tests/references/cross_validation_test_ozone_custom_split.log index ae350d36f..7464ec708 100644 --- a/tests/references/cross_validation_test_ozone_custom_split.log +++ b/tests/references/cross_validation_test_ozone_custom_split.log @@ -5,124 +5,264 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Cycle_None_AR 18 0.2751 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.4583 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_NoCycle_AR 10 0.2751 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.4583 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Cycle_None_AR 50 0.295 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Cycle_None_NoAR 40 0.1989 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_NoCycle_AR 42 0.295 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.1989 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Cycle_AR 34 0.2618 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.4028 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_LinearTrend_NoCycle_AR 26 0.2442 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.4071 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Cycle_None_AR 34 0.5201 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.8672 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_PolyTrend_NoCycle_AR 26 0.5201 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.8672 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Cycle_None_AR 50 1.0796 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Cycle_None_NoAR 40 0.42 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_NoCycle_AR 42 1.0796 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.42 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 82 0.5117 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.1989 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_NoCycle_AR 74 0.5117 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.1989 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Cycle_None_AR 66 2.1818 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 1.7667 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_NoCycle_AR 58 2.1818 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 1.7667 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Cycle_None_AR 66 2.4078 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Cycle_None_NoAR 56 1.8937 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_NoCycle_AR 58 2.4078 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 1.8937 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 50 73.5944 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 337.6127 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 42 73.5944 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 337.6127 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_None_AR 82 0.7321 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.1989 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 74 0.7321 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.1989 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 66 3.4172 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 8.9936 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 58 3.4172 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 8.9936 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_None_AR 66 505.7789 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_None_NoAR 56 523.8571 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 58 505.7789 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 523.8571 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Cycle_AR 50 0.2728 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.8222 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 42 0.2273 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Cycle_AR 82 0.2712 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2351 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 74 0.3348 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.1989 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Cycle_AR 66 0.2643 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.5945 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_NoCycle_AR 58 0.2176 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.3219 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 66 0.3303 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 2.7889 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_NoCycle_AR 58 0.3303 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.2, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 2.7889 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 3.3966917991638184 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1958-04-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.4619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.9761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 360.8514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 3289621.0774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.1949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.2424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 0.362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.4322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.7301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.5794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.7377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 0.4357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 16702.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 58064.6971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 667.4806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 1338.131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 168397.0695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 16375730.725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 3613815.678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 24525015.6551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 0.305 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.884079694747925 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2337 MAPE_Forecast=0.1989 MAPE_Test=None -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2189 SMAPE_Forecast=0.1942 SMAPE_Test=None -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.975 MASE_Forecast=0.9782 MASE_Test=None -INFO:pyaf.std:MODEL_L1 L1_Fit=1.0875 L1_Forecast=0.7549999999999999 L1_Test=None -INFO:pyaf.std:MODEL_L2 L2_Fit=1.440919845098956 L2_Forecast=1.030048542545447 L2_Test=None -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 +INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LAG1_TREND Lag1Trend 2.7 +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.132140398025513 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.1506803035736084 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 17.954551458358765 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.537421703338623 INFO:pyaf.std:START_TRAINING 'Ozone' - Split Transformation ... TestMAPE TestMASE -0 (0.2, 0.2, 0.0) _Ozone ... None None -1 (0.2, 0.2, 0.0) _Ozone ... None None -2 (0.2, 0.2, 0.0) CumSum_Ozone ... None None -3 (0.2, 0.2, 0.0) Diff_Ozone ... None None -4 (0.2, 0.2, 0.0) RelDiff_Ozone ... None None - -[5 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 (0.2, 0.2, 0.0) _Ozone ... None None -1 (0.2, 0.2, 0.0) _Ozone ... None None -2 (0.2, 0.2, 0.0) CumSum_Ozone ... None None -3 (0.2, 0.2, 0.0) Diff_Ozone ... None None -4 (0.2, 0.2, 0.0) RelDiff_Ozone ... None None + Split Transformation ... TestMAPE TestMASE +0 None _Ozone ... 0.1740 0.9094 +1 None _Ozone ... 0.1740 0.9094 +2 None _Ozone ... 0.3430 1.6728 +3 None _Ozone ... 0.3430 1.6728 +4 None _Ozone ... 0.2209 1.0918 [5 rows x 20 columns] eTimeResolution.MONTH Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', - '_Ozone_Lag1Trend', '_Ozone_Lag1Trend_residue', - '_Ozone_Lag1Trend_residue_zeroCycle', - '_Ozone_Lag1Trend_residue_zeroCycle_residue', - '_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR', - '_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_zeroCycle', + '_Ozone_LinearTrend_residue_zeroCycle_residue', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -142,47 +282,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 1.2 -205 1972-02-01 NaN 1.2 -206 1972-03-01 NaN 1.2 -207 1972-04-01 NaN 1.2 -208 1972-05-01 NaN 1.2 -209 1972-06-01 NaN 1.2 -210 1972-07-01 NaN 1.2 -211 1972-08-01 NaN 1.2 -212 1972-09-01 NaN 1.2 -213 1972-10-01 NaN 1.2 -214 1972-11-01 NaN 1.2 -215 1972-12-01 NaN 1.2 +204 1972-01-01 NaN 0.611147 +205 1972-02-01 NaN 1.626529 +206 1972-03-01 NaN 1.942209 +207 1972-04-01 NaN 2.369673 +208 1972-05-01 NaN 2.663022 +209 1972-06-01 NaN 3.248702 +210 1972-07-01 NaN 3.220270 +211 1972-08-01 NaN 3.329387 +212 1972-09-01 NaN 2.996846 +213 1972-10-01 NaN 2.118734 +214 1972-11-01 NaN 1.332600 +215 1972-12-01 NaN 0.841575 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "0.7549999999999999", - "MAPE": "0.1989", - "MASE": "0.9782", - "RMSE": "1.030048542545447" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } @@ -191,7 +333,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.3,"193":1.8,"194":2.0,"195":2.2,"196":3.0,"197":2.4,"198":3.5,"199":3.5,"200":3.3,"201":2.7,"202":2.5,"203":1.6,"204":1.2,"205":1.2,"206":1.2,"207":1.2,"208":1.2,"209":1.2,"210":1.2,"211":1.2,"212":1.2,"213":1.2,"214":1.2,"215":1.2}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0201234776,"193":1.9477080322,"194":2.8071877532,"195":1.9810299731,"196":3.2711119481,"197":3.0180455521,"198":4.2391631176,"199":3.363140279,"200":2.9406052694,"201":2.3736934134,"202":1.5680019494,"203":1.0535599086,"204":0.6111465642,"205":1.6265294603,"206":1.9422085586,"207":2.3696733129,"208":2.6630222018,"209":3.2487020879,"210":3.220269503,"211":3.32938729,"212":2.996846273,"213":2.1187344328,"214":1.3326002972,"215":0.8415747446}} @@ -201,72 +343,200 @@ Forecasts 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Cycle_None_AR 28 0.2906 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.5953 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_NoCycle_AR 20 0.2906 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5953 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Cycle_None_AR 60 0.2381 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Cycle_None_NoAR 40 0.2601 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_NoCycle_AR 52 0.2381 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2601 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Cycle_None_AR 44 0.1965 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3558 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_LinearTrend_NoCycle_AR 36 0.1965 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3558 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Cycle_AR 44 0.3252 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.4747 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_PolyTrend_NoCycle_AR 36 0.3206 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4754 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Cycle_None_AR 60 0.8805 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Cycle_None_NoAR 40 0.5024 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_NoCycle_AR 52 0.8805 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.5024 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 92 0.258 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2601 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_NoCycle_AR 84 0.258 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2601 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Cycle_None_AR 76 1.6159 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.5529 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_NoCycle_AR 68 1.6159 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.5529 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Cycle_None_AR 76 0.7226 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Cycle_None_NoAR 56 0.9021 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_NoCycle_AR 68 0.7226 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.9021 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 60 21.7522 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 18972.566 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 52 21.7522 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 18972.566 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_None_AR 92 2.3144 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.2601 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 84 2.3144 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2601 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 76 12.9027 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 6061.9398 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 68 12.9027 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 6061.9398 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_None_AR 76 93.729 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_None_NoAR 56 70319.758 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 68 93.729 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 70319.758 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Cycle_AR 60 0.3159 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.4837 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 52 0.2964 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Cycle_AR 92 0.3461 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2945 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 84 0.3589 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2601 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 76 0.3018 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4821 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_NoCycle_AR 68 0.3018 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4821 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 76 0.3759 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 1.5403 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_NoCycle_AR 68 0.3759 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.4, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 1.5403 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 3.660947799682617 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1961-09-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.4619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.9761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 360.8514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 3289621.0774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.1949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.2424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 0.362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.4322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.7301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.5794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.7377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 0.4357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 16702.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 58064.6971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 667.4806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 1338.131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 168397.0695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 16375730.725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 3613815.678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 24525015.6551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 0.305 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 3.655287265777588 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' @@ -274,49 +544,44 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_ INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1621 MAPE_Forecast=0.1965 MAPE_Test=None -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1516 SMAPE_Forecast=0.193 SMAPE_Test=None -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7508 MASE_Forecast=0.7861 MASE_Test=None -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6944497955657216 L1_Forecast=0.6168038613298956 L1_Test=None -INFO:pyaf.std:MODEL_L2 L2_Fit=0.926642220358621 L2_Forecast=0.7587248745833638 L2_Test=None -INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 +INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.420956766100799, array([-1.63774749])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.521270531956657 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.2983128047937673 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.22829756766807321 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.21599293977050021 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.20616805648555356 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag41 0.16928706012438155 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag13 -0.15082219866656765 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.12003234668768703 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag23 0.1066961519031369 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag5 -0.09516023569337206 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.026059150695801 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.2567276954650879 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.0941078662872314 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5064253807067871 INFO:pyaf.std:START_TRAINING 'Ozone' - Split Transformation ... TestMAPE TestMASE -0 (0.4, 0.2, 0.0) _Ozone ... None None -1 (0.4, 0.2, 0.0) _Ozone ... None None - -[2 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 (0.4, 0.2, 0.0) _Ozone ... None None -1 (0.4, 0.2, 0.0) _Ozone ... None None -2 (0.4, 0.2, 0.0) _Ozone ... None None -3 (0.4, 0.2, 0.0) _Ozone ... None None -4 (0.4, 0.2, 0.0) Diff_Ozone ... None None + Split Transformation ... TestMAPE TestMASE +0 None _Ozone ... 0.1740 0.9094 +1 None _Ozone ... 0.1740 0.9094 +2 None _Ozone ... 0.3430 1.6728 +3 None _Ozone ... 0.3430 1.6728 +4 None _Ozone ... 0.2209 1.0918 [5 rows x 20 columns] eTimeResolution.MONTH @@ -345,47 +610,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.232341 -205 1972-02-01 NaN 0.819692 -206 1972-03-01 NaN 0.559729 -207 1972-04-01 NaN 1.336053 -208 1972-05-01 NaN 1.050237 -209 1972-06-01 NaN 2.011558 -210 1972-07-01 NaN 1.646154 -211 1972-08-01 NaN 1.842919 -212 1972-09-01 NaN 1.131399 -213 1972-10-01 NaN 1.036558 -214 1972-11-01 NaN 0.377937 -215 1972-12-01 NaN 0.105218 +204 1972-01-01 NaN 0.611147 +205 1972-02-01 NaN 1.626529 +206 1972-03-01 NaN 1.942209 +207 1972-04-01 NaN 2.369673 +208 1972-05-01 NaN 2.663022 +209 1972-06-01 NaN 3.248702 +210 1972-07-01 NaN 3.220270 +211 1972-08-01 NaN 3.329387 +212 1972-09-01 NaN 2.996846 +213 1972-10-01 NaN 2.118734 +214 1972-11-01 NaN 1.332600 +215 1972-12-01 NaN 0.841575 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "36", - "MAE": "0.6168038613298956", - "MAPE": "0.1965", - "MASE": "0.7861", - "RMSE": "0.7587248745833638" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } @@ -394,7 +661,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.8177870311,"193":0.9335066333,"194":1.9656940238,"195":1.5870601531,"196":2.6161552563,"197":2.37585982,"198":3.5305417191,"199":2.5511717876,"200":2.148475864,"201":1.1758001519,"202":1.3746940765,"203":0.8704251887,"204":0.2323413404,"205":0.8196924543,"206":0.5597287829,"207":1.3360533972,"208":1.0502371969,"209":2.0115577582,"210":1.6461542711,"211":1.8429193325,"212":1.1313987102,"213":1.0365575253,"214":0.377936536,"215":0.1052178431}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0201234776,"193":1.9477080322,"194":2.8071877532,"195":1.9810299731,"196":3.2711119481,"197":3.0180455521,"198":4.2391631176,"199":3.363140279,"200":2.9406052694,"201":2.3736934134,"202":1.5680019494,"203":1.0535599086,"204":0.6111465642,"205":1.6265294603,"206":1.9422085586,"207":2.3696733129,"208":2.6630222018,"209":3.2487020879,"210":3.220269503,"211":3.32938729,"212":2.996846273,"213":2.1187344328,"214":1.3326002972,"215":0.8415747446}} @@ -404,104 +671,200 @@ Forecasts 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 34 0.2226 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2179 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Cycle_None_AR 38 0.2564 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.4641 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_NoCycle_AR 30 0.2564 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.4641 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 66 0.2422 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2705 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Cycle_None_AR 70 0.2493 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Cycle_None_NoAR 40 0.2692 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_NoCycle_AR 62 0.2493 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2692 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 50 0.2455 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.2921 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Cycle_None_AR 54 0.202 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3446 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_LinearTrend_NoCycle_AR 46 0.202 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3446 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 50 0.2651 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.3375 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Cycle_None_AR 54 0.224 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.3537 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_PolyTrend_NoCycle_AR 46 0.224 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.3537 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 66 0.2306 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.2845 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Cycle_None_AR 70 0.3303 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Cycle_None_NoAR 40 0.3445 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_NoCycle_AR 62 0.3303 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.3445 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 98 0.4719 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.158 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 102 0.3996 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2692 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_NoCycle_AR 94 0.3996 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2692 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 82 0.5137 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0057 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Cycle_None_AR 86 0.2375 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.61 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_NoCycle_AR 78 0.2375 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.61 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 82 0.356 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 2.6789 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Cycle_None_AR 86 0.6325 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Cycle_None_NoAR 56 0.3468 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_NoCycle_AR 78 0.6325 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.3468 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 66 25.1081 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 4.7154 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 70 117.214 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 40825271.5565 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 62 117.214 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 40825271.5565 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 98 0.6151 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6149 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 102 0.5688 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.4439 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 94 0.6061 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2692 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 82 97.1928 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 92.1578 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_AR 86 2350.0479 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 3490.9233 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 78 282.7209 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 43636851.3725 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 82 12129069.294 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 578800.2178 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_None_AR 86 8251328.373 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_None_NoAR 56 44733479.5454 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 78 8251328.373 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 44733479.5454 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 66 0.3594 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0728 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Cycle_AR 70 0.3577 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.279 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 62 0.3283 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 98 0.3719 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2425 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Cycle_AR 102 0.3719 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2425 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 94 0.3437 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2692 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 82 0.3173 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2951 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 86 0.2934 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.396 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_NoCycle_AR 78 0.2934 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.396 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 82 0.3317 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6781 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 86 0.3163 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7022 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_NoCycle_AR 78 0.3163 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7022 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 5.032236814498901 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1965-02-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.4619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.9761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 360.8514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 3289621.0774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.1949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.2424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 0.362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.4322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.7301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.5794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.7377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 0.4357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 16702.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 58064.6971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 667.4806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 1338.131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 168397.0695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 16375730.725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 3613815.678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 24525015.6551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 0.305 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.216478109359741 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' @@ -509,49 +872,44 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_ INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1693 MAPE_Forecast=0.202 MAPE_Test=None -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.16 SMAPE_Forecast=0.2259 SMAPE_Test=None -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7416 MASE_Forecast=0.8018 MASE_Test=None -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6460293204603748 L1_Forecast=0.7195307724046183 L1_Test=None -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8329387990758682 L2_Forecast=0.9974115322615982 L2_Test=None -INFO:pyaf.std:MODEL_COMPLEXITY 46 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 +INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.30323388262745, array([-2.22581787])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4623916215625944 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.31063770969699933 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.19242010017168115 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.17622521851307849 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.13856090235188 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.13712045520083607 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag8 -0.12073099941422545 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag16 -0.12048124605800353 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag13 -0.11413493982051175 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.1073930722810234 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.070584774017334 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.2970762252807617 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 5.849674940109253 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.8011581897735596 INFO:pyaf.std:START_TRAINING 'Ozone' - Split Transformation ... TestMAPE TestMASE -0 (0.6000000000000001, 0.2, 0.0) _Ozone ... None None -1 (0.6000000000000001, 0.2, 0.0) _Ozone ... None None - -[2 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 (0.6000000000000001, 0.2, 0.0) _Ozone ... None None -1 (0.6000000000000001, 0.2, 0.0) _Ozone ... None None -2 (0.6000000000000001, 0.2, 0.0) _Ozone ... None None -3 (0.6000000000000001, 0.2, 0.0) _Ozone ... None None -4 (0.6000000000000001, 0.2, 0.0) _Ozone ... None None + Split Transformation ... TestMAPE TestMASE +0 None _Ozone ... 0.1740 0.9094 +1 None _Ozone ... 0.1740 0.9094 +2 None _Ozone ... 0.3430 1.6728 +3 None _Ozone ... 0.3430 1.6728 +4 None _Ozone ... 0.2209 1.0918 [5 rows x 20 columns] eTimeResolution.MONTH @@ -580,47 +938,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.268077 -205 1972-02-01 NaN 1.188244 -206 1972-03-01 NaN 1.055857 -207 1972-04-01 NaN 1.642528 -208 1972-05-01 NaN 1.470483 -209 1972-06-01 NaN 2.167399 -210 1972-07-01 NaN 2.138778 -211 1972-08-01 NaN 2.482992 -212 1972-09-01 NaN 1.981476 -213 1972-10-01 NaN 1.525327 -214 1972-11-01 NaN 0.665264 -215 1972-12-01 NaN 0.281100 +204 1972-01-01 NaN 0.611147 +205 1972-02-01 NaN 1.626529 +206 1972-03-01 NaN 1.942209 +207 1972-04-01 NaN 2.369673 +208 1972-05-01 NaN 2.663022 +209 1972-06-01 NaN 3.248702 +210 1972-07-01 NaN 3.220270 +211 1972-08-01 NaN 3.329387 +212 1972-09-01 NaN 2.996846 +213 1972-10-01 NaN 2.118734 +214 1972-11-01 NaN 1.332600 +215 1972-12-01 NaN 0.841575 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "46", - "MAE": "0.7195307724046183", - "MAPE": "0.202", - "MASE": "0.8018", - "RMSE": "0.9974115322615982" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } @@ -629,7 +989,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.6992503649,"193":1.4888662893,"194":2.0985495223,"195":1.3868911271,"196":2.7054296266,"197":2.5177115566,"198":3.6995086593,"199":2.915138346,"200":2.3106458055,"201":1.8412427706,"202":1.5063822453,"203":0.5315104199,"204":0.2680769694,"205":1.1882436777,"206":1.0558569268,"207":1.6425281532,"208":1.4704829135,"209":2.1673992723,"210":2.1387777442,"211":2.4829922255,"212":1.9814757048,"213":1.5253274842,"214":0.6652636808,"215":0.2810996921}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0201234776,"193":1.9477080322,"194":2.8071877532,"195":1.9810299731,"196":3.2711119481,"197":3.0180455521,"198":4.2391631176,"199":3.363140279,"200":2.9406052694,"201":2.3736934134,"202":1.5680019494,"203":1.0535599086,"204":0.6111465642,"205":1.6265294603,"206":1.9422085586,"207":2.3696733129,"208":2.6630222018,"209":3.2487020879,"210":3.220269503,"211":3.32938729,"212":2.996846273,"213":2.1187344328,"214":1.3326002972,"215":0.8415747446}} @@ -639,104 +999,200 @@ Forecasts 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 44 0.2028 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3556 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Cycle_AR 48 0.2337 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5512 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_NoCycle_AR 40 0.2323 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5782 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 76 0.2025 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2133 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Cycle_None_AR 80 0.19 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_Cycle_None_NoAR 40 0.2594 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_NoCycle_AR 72 0.19 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2594 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 60 0.2147 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.2073 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Cycle_AR 64 0.1642 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.3095 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_LinearTrend_NoCycle_AR 56 0.1536 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3146 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 60 0.2308 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2592 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Cycle_None_AR 64 0.1746 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.41 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_PolyTrend_NoCycle_AR 56 0.1746 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.41 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 76 0.3674 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 3.5527 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Cycle_None_AR 80 0.1991 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_Cycle_None_NoAR 40 0.9729 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_NoCycle_AR 72 0.1991 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.9729 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 108 0.2122 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.9989 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 112 0.208 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2594 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_NoCycle_AR 104 0.208 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2594 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 92 0.2437 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 3.4043 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Cycle_None_AR 96 0.189 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.89 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_NoCycle_AR 88 0.189 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.89 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 92 2.6693 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 5.3171 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Cycle_None_AR 96 1.2655 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_Cycle_None_NoAR 56 2.0025 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_NoCycle_AR 88 1.2655 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 2.0025 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 76 7803.6445 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4686 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 80 632.4093 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 56918349.4245 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 72 632.4093 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 56918349.4245 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 108 4190.319 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5446 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_None_AR 112 1.0037 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.2594 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 104 1.0037 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2594 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 92 42307.1899 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 3.7411 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 96 482.3516 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 56918349.4245 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 88 482.3516 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 56918349.4245 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 92 212241.7386 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 67.2742 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_None_AR 96 17841.4926 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_None_NoAR 56 56918349.4245 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 88 17841.4926 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 56918349.4245 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 76 0.4458 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.4869 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Cycle_AR 80 0.3437 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.3564 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 72 0.3242 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 108 0.2601 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2583 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Cycle_AR 112 0.2601 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2583 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 104 0.333 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2594 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 92 0.3491 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.3477 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 96 0.3461 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4632 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_NoCycle_AR 88 0.3461 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4632 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 92 0.4074 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.7191 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 96 0.352 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.816 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_NoCycle_AR 88 0.352 -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.2, 0.0) CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.816 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 6.202826499938965 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1968-07-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.4619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.9761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 360.8514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 3289621.0774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.1949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.2424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 0.362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.4322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.7301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.5794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.7377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 0.4357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 16702.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 58064.6971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 667.4806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 1338.131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 168397.0695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 16375730.725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 3613815.678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 24525015.6551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 0.305 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.969878435134888 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' @@ -744,47 +1200,43 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_ INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1595 MAPE_Forecast=0.1536 MAPE_Test=None -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1506 SMAPE_Forecast=0.1601 SMAPE_Test=None -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6824 MASE_Forecast=0.6692 MASE_Test=None -INFO:pyaf.std:MODEL_L1 L1_Fit=0.598133079360903 L1_Forecast=0.4701386807613671 L1_Test=None -INFO:pyaf.std:MODEL_L2 L2_Fit=0.803765511961128 L2_Forecast=0.5955282811729463 L2_Test=None -INFO:pyaf.std:MODEL_COMPLEXITY 56 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 +INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.9984819530936715, array([-1.88052118])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4098600230696514 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.18675857678963093 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.17447725453500862 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.1716117565197735 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16331486590362887 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.14919908836674062 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag20 -0.11847857304533725 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag4 0.11057011863645645 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.1048900666387087 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag35 -0.10436289782089132 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.109665155410767 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3546929359436035 - Split Transformation ... TestMAPE TestMASE -0 (0.8, 0.2, 0.0) _Ozone ... None None - -[1 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 (0.8, 0.2, 0.0) _Ozone ... None None -1 (0.8, 0.2, 0.0) _Ozone ... None None -2 (0.8, 0.2, 0.0) _Ozone ... None None -3 (0.8, 0.2, 0.0) _Ozone ... None None -4 (0.8, 0.2, 0.0) Diff_Ozone ... None None +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 7.40251612663269 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.1760997772216797 + Split Transformation ... TestMAPE TestMASE +0 None _Ozone ... 0.1740 0.9094 +1 None _Ozone ... 0.1740 0.9094 +2 None _Ozone ... 0.3430 1.6728 +3 None _Ozone ... 0.3430 1.6728 +4 None _Ozone ... 0.2209 1.0918 [5 rows x 20 columns] eTimeResolution.MONTH @@ -813,47 +1265,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.630228 -205 1972-02-01 NaN 1.715126 -206 1972-03-01 NaN 2.094236 -207 1972-04-01 NaN 2.593447 -208 1972-05-01 NaN 2.711910 -209 1972-06-01 NaN 3.231872 -210 1972-07-01 NaN 3.223992 -211 1972-08-01 NaN 3.622499 -212 1972-09-01 NaN 3.048997 -213 1972-10-01 NaN 2.348002 -214 1972-11-01 NaN 1.491648 -215 1972-12-01 NaN 1.029668 +204 1972-01-01 NaN 0.611147 +205 1972-02-01 NaN 1.626529 +206 1972-03-01 NaN 1.942209 +207 1972-04-01 NaN 2.369673 +208 1972-05-01 NaN 2.663022 +209 1972-06-01 NaN 3.248702 +210 1972-07-01 NaN 3.220270 +211 1972-08-01 NaN 3.329387 +212 1972-09-01 NaN 2.996846 +213 1972-10-01 NaN 2.118734 +214 1972-11-01 NaN 1.332600 +215 1972-12-01 NaN 0.841575 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "56", - "MAE": "0.4701386807613671", - "MAPE": "0.1536", - "MASE": "0.6692", - "RMSE": "0.5955282811729463" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } @@ -862,7 +1316,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.1936290488,"193":1.9652801069,"194":2.7710493496,"195":2.1132841078,"196":3.3137858962,"197":2.9529351246,"198":4.3624912205,"199":3.3817291746,"200":3.10289664,"201":2.4811328438,"202":1.6810151461,"203":1.0653791331,"204":0.6302278277,"205":1.7151257289,"206":2.0942359393,"207":2.5934473858,"208":2.711910371,"209":3.2318722159,"210":3.2239924947,"211":3.6224994091,"212":3.0489972433,"213":2.3480015514,"214":1.4916478749,"215":1.0296679146}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0201234776,"193":1.9477080322,"194":2.8071877532,"195":1.9810299731,"196":3.2711119481,"197":3.0180455521,"198":4.2391631176,"199":3.363140279,"200":2.9406052694,"201":2.3736934134,"202":1.5680019494,"203":1.0535599086,"204":0.6111465642,"205":1.6265294603,"206":1.9422085586,"207":2.3696733129,"208":2.6630222018,"209":3.2487020879,"210":3.220269503,"211":3.32938729,"212":2.996846273,"213":2.1187344328,"214":1.3326002972,"215":0.8415747446}} diff --git a/tests/references/croston_croston_test_1_SBA_linear_trend.log b/tests/references/croston_croston_test_1_SBA_linear_trend.log index e316cd000..94090bed9 100644 --- a/tests/references/croston_croston_test_1_SBA_linear_trend.log +++ b/tests/references/croston_croston_test_1_SBA_linear_trend.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 3.7400832176208496 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 6.841228485107422 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-01-25T00:00:00.000000 TimeMax=2016-11-05T00:00:00.000000 TimeDelta= Horizon=7 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=365 Min=0.0027397260273972603 Max=1.1839455211826702 Mean=0.5040123444529377 StdDev=0.2901181557641455 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=0.0027397260273972603 Max=1.1839455211826702 Mean=0.5040123444529377 StdDev=0.2901181557641455 @@ -11,14 +11,14 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_LinearTrend_residue_zeroCycle_residue_CROS INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.9747 MAPE_Forecast=0.0049 MAPE_Test=0.0026 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1257 SMAPE_Forecast=0.0051 SMAPE_Test=0.0026 INFO:pyaf.std:MODEL_MASE MASE_Fit=1.4134 MASE_Forecast=0.6793 MASE_Test=0.9465 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02021275935189369 L1_Forecast=0.004706720464166015 L1_Test=0.0025932080659925505 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.06030894604580932 L2_Forecast=0.018501837854203902 L2_Test=0.0025971533479613377 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.020212759351893688 L1_Forecast=0.004706720464166004 L1_Test=0.0025932080659925028 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.06030894604580932 L2_Forecast=0.018501837854203902 L2_Test=0.0025971533479612905 INFO:pyaf.std:MODEL_COMPLEXITY 18 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.02302527998568371, array([0.74744996])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.023025279985683655, array([0.74744996])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_LinearTrend_residue_zeroCycle 0.0 {} @@ -28,9 +28,19 @@ INFO:pyaf.std:CROSTON_ALPHA 0.1 INFO:pyaf.std:CROSTON_METHOD SBA INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 6.580574989318848 -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 2.0066659450531006 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 19.224148273468018 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 4.47317361831665 Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_LinearTrend', '_Signal_LinearTrend_residue', '_Signal_LinearTrend_residue_zeroCycle', diff --git a/tests/references/croston_croston_test_1_SBJ_linear_trend.log b/tests/references/croston_croston_test_1_SBJ_linear_trend.log index d00815d6f..696dd16e0 100644 --- a/tests/references/croston_croston_test_1_SBJ_linear_trend.log +++ b/tests/references/croston_croston_test_1_SBJ_linear_trend.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 3.749507188796997 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 6.995051383972168 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-01-25T00:00:00.000000 TimeMax=2016-11-05T00:00:00.000000 TimeDelta= Horizon=7 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=365 Min=0.0027397260273972603 Max=1.1839455211826702 Mean=0.5040123444529377 StdDev=0.2901181557641455 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=0.0027397260273972603 Max=1.1839455211826702 Mean=0.5040123444529377 StdDev=0.2901181557641455 @@ -11,14 +11,14 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_LinearTrend_residue_zeroCycle_residue_CROS INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.9721 MAPE_Forecast=0.005 MAPE_Test=0.0027 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1255 SMAPE_Forecast=0.0051 SMAPE_Test=0.0027 INFO:pyaf.std:MODEL_MASE MASE_Fit=1.4113 MASE_Forecast=0.6825 MASE_Test=0.9865 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.020182073431876158 L1_Forecast=0.004729179937757529 L1_Test=0.0027027948897990284 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.06023407622692955 L2_Forecast=0.018511141374359313 L2_Test=0.0027065936695407408 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.020182073431876165 L1_Forecast=0.004729179937757521 L1_Test=0.002702794889798981 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.060234076226929556 L2_Forecast=0.018511141374359313 L2_Test=0.002706593669540692 INFO:pyaf.std:MODEL_COMPLEXITY 18 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.02302527998568371, array([0.74744996])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.023025279985683655, array([0.74744996])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_LinearTrend_residue_zeroCycle 0.0 {} @@ -28,9 +28,19 @@ INFO:pyaf.std:CROSTON_ALPHA 0.1 INFO:pyaf.std:CROSTON_METHOD SBJ INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 6.559646129608154 -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 1.9983747005462646 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 19.600623607635498 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 4.581467390060425 Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_LinearTrend', '_Signal_LinearTrend_residue', '_Signal_LinearTrend_residue_zeroCycle', diff --git a/tests/references/croston_croston_test_1_legacy_linear_trend.log b/tests/references/croston_croston_test_1_legacy_linear_trend.log index 56cd17783..3c13f5e10 100644 --- a/tests/references/croston_croston_test_1_legacy_linear_trend.log +++ b/tests/references/croston_croston_test_1_legacy_linear_trend.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 3.7159454822540283 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 7.595531225204468 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-01-25T00:00:00.000000 TimeMax=2016-11-05T00:00:00.000000 TimeDelta= Horizon=7 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=365 Min=0.0027397260273972603 Max=1.1839455211826702 Mean=0.5040123444529377 StdDev=0.2901181557641455 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=0.0027397260273972603 Max=1.1839455211826702 Mean=0.5040123444529377 StdDev=0.2901181557641455 @@ -11,14 +11,14 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_LinearTrend_residue_zeroCycle_residue_CROS INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.0259 MAPE_Forecast=0.005 MAPE_Test=0.0005 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1282 SMAPE_Forecast=0.0051 SMAPE_Test=0.0005 INFO:pyaf.std:MODEL_MASE MASE_Fit=1.4574 MASE_Forecast=0.6808 MASE_Test=0.1865 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.020840688387714128 L1_Forecast=0.0047175666396369375 L1_Test=0.0005110584136698135 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.06177210192575246 L2_Forecast=0.018437787897295516 L2_Test=0.0005294543633223304 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02084068838771413 L1_Forecast=0.004717566639636935 L1_Test=0.0005110584136697659 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.06177210192575246 L2_Forecast=0.018437787897295513 L2_Test=0.0005294543633222937 INFO:pyaf.std:MODEL_COMPLEXITY 18 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.02302527998568371, array([0.74744996])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.023025279985683655, array([0.74744996])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_LinearTrend_residue_zeroCycle 0.0 {} @@ -28,9 +28,19 @@ INFO:pyaf.std:CROSTON_ALPHA 0.1 INFO:pyaf.std:CROSTON_METHOD None INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 6.536631107330322 -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 2.0222713947296143 +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 18.80357313156128 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 4.3604090213775635 Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_LinearTrend', '_Signal_LinearTrend_residue', '_Signal_LinearTrend_residue_zeroCycle', diff --git a/tests/references/exog_test_ozone_exogenous.log b/tests/references/exog_test_ozone_exogenous.log index fc1ef67a8..bf2bc766d 100644 --- a/tests/references/exog_test_ozone_exogenous.log +++ b/tests/references/exog_test_ozone_exogenous.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 1955 3 AS P_S 3.6 1955-03-01 3 1955-04 1955 4 AT P_U 5.0 1955-04-01 4 1955-05 1955 5 AU P_V 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 18.12391185760498 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 12.721349000930786 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,8 +17,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6135978624272591 L1_Forecast=0.5265316758013037 L1_Test=0.42992050508902385 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507152 L2_Test=0.551943269559555 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -30,21 +30,31 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033754 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533328 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225498 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.1612820579538937 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046368 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481863 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102252 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045825 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947768 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.301309108734131 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.32889485359191895 +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 18.95945143699646 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.8774373531341553 Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', '_Ozone_LinearTrend_residue_zeroCycle', @@ -69,47 +79,49 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 5.2 KB None Forecasts - [[Timestamp('1972-01-01 00:00:00') nan 0.611146564169524] - [Timestamp('1972-02-01 00:00:00') nan 1.626529460286908] - [Timestamp('1972-03-01 00:00:00') nan 1.9422085585983484] - [Timestamp('1972-04-01 00:00:00') nan 2.369673312889974] - [Timestamp('1972-05-01 00:00:00') nan 2.6630222017572533] - [Timestamp('1972-06-01 00:00:00') nan 3.2487020878736232] - [Timestamp('1972-07-01 00:00:00') nan 3.2202695030432946] - [Timestamp('1972-08-01 00:00:00') nan 3.3293872899828663] - [Timestamp('1972-09-01 00:00:00') nan 2.9968462730263554] - [Timestamp('1972-10-01 00:00:00') nan 2.1187344328436213] - [Timestamp('1972-11-01 00:00:00') nan 1.3326002972043864] - [Timestamp('1972-12-01 00:00:00') nan 0.8415747446287962]] + [[Timestamp('1972-01-01 00:00:00') nan 0.6111465641695266] + [Timestamp('1972-02-01 00:00:00') nan 1.6265294602869103] + [Timestamp('1972-03-01 00:00:00') nan 1.9422085585983508] + [Timestamp('1972-04-01 00:00:00') nan 2.369673312889975] + [Timestamp('1972-05-01 00:00:00') nan 2.6630222017572525] + [Timestamp('1972-06-01 00:00:00') nan 3.248702087873622] + [Timestamp('1972-07-01 00:00:00') nan 3.2202695030432924] + [Timestamp('1972-08-01 00:00:00') nan 3.329387289982864] + [Timestamp('1972-09-01 00:00:00') nan 2.9968462730263523] + [Timestamp('1972-10-01 00:00:00') nan 2.1187344328436204] + [Timestamp('1972-11-01 00:00:00') nan 1.3326002972043869] + [Timestamp('1972-12-01 00:00:00') nan 0.8415747446287978]] { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013037", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507152" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/exog_test_ozone_exogenous_with_categorical.log b/tests/references/exog_test_ozone_exogenous_with_categorical.log index 936f9dda9..8aa5a337a 100644 --- a/tests/references/exog_test_ozone_exogenous_with_categorical.log +++ b/tests/references/exog_test_ozone_exogenous_with_categorical.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone2' 2 1955-03 1955 3 AS P_S 3.6 1955-03-01 3.6 3 1955-04 1955 4 AT P_U 5.0 1955-04-01 5.0 4 1955-05 1955 5 AU P_V 6.5 1955-05-01 6.5 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone2' 2.1902248859405518 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone2']' 2.1448113918304443 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1959-03-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone2' Length=51 Min=0.0 Max=26.099999999999998 Mean=5.7529411764705864 StdDev=5.086288871735056 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='RelDiff_Ozone2' Min=-0.9999999681707318 Max=36781608.195402294 Mean=1171963.1590098052 StdDev=5559717.681827764 @@ -31,21 +31,31 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES RelDiff_Ozone2_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Exog4=P_S_Lag1 4660418.708409504 +INFO:pyaf.std:AR_MODEL_COEFF 1 Exog4=P_S_Lag1 4660418.708409503 INFO:pyaf.std:AR_MODEL_COEFF 2 Exog4=P_S_Lag5 3880307.295613807 -INFO:pyaf.std:AR_MODEL_COEFF 3 Exog4=P_U_Lag2 2971362.472427069 -INFO:pyaf.std:AR_MODEL_COEFF 4 Exog4=P_T_Lag4 2310728.753743561 -INFO:pyaf.std:AR_MODEL_COEFF 5 Exog4=P_U_Lag10 1873570.1369204647 -INFO:pyaf.std:AR_MODEL_COEFF 6 Exog4=P_U_Lag3 -1404113.0996582045 -INFO:pyaf.std:AR_MODEL_COEFF 7 Exog4=P_T_Lag5 -1224950.891523218 -INFO:pyaf.std:AR_MODEL_COEFF 8 Exog4=P_U_Lag1 -447988.34764394455 -INFO:pyaf.std:AR_MODEL_COEFF 9 Exog4=P_U_Lag9 441781.7927186434 -INFO:pyaf.std:AR_MODEL_COEFF 10 Exog4=P_U_Lag4 -365013.34041281475 +INFO:pyaf.std:AR_MODEL_COEFF 3 Exog4=P_U_Lag2 2971362.4724270706 +INFO:pyaf.std:AR_MODEL_COEFF 4 Exog4=P_T_Lag4 2310728.753743562 +INFO:pyaf.std:AR_MODEL_COEFF 5 Exog4=P_U_Lag10 1873570.136920467 +INFO:pyaf.std:AR_MODEL_COEFF 6 Exog4=P_U_Lag3 -1404113.0996582021 +INFO:pyaf.std:AR_MODEL_COEFF 7 Exog4=P_T_Lag5 -1224950.891523217 +INFO:pyaf.std:AR_MODEL_COEFF 8 Exog4=P_U_Lag1 -447988.34764394077 +INFO:pyaf.std:AR_MODEL_COEFF 9 Exog4=P_U_Lag9 441781.7927186412 +INFO:pyaf.std:AR_MODEL_COEFF 10 Exog4=P_U_Lag4 -365013.340412811 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.405914306640625 -INFO:pyaf.std:START_FORECASTING 'Ozone2' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone2' 0.35083818435668945 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 15.72371792793274 +INFO:pyaf.std:START_FORECASTING '['Ozone2']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone2']' 0.8061108589172363 INFO:pyaf.std:START_TRAINING 'Ozone2' Forecast Columns Index(['Time', 'Ozone2', 'row_number', 'Time_Normalized', 'RelDiff_Ozone2', 'RelDiff_Ozone2_ConstantTrend', 'RelDiff_Ozone2_ConstantTrend_residue', @@ -89,31 +99,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone2", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1959-03-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone2": { + "Dataset": { + "Signal": "Ozone2", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1959-03-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 51 }, - "Training_Signal_Length": 51 - }, - "Model": { - "AR_Model": "ARX", - "Best_Decomposition": "RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(12)", - "Cycle": "NoCycle", - "Signal_Transoformation": "RelativeDifference", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "44", - "MAE": "5.7529411764705864", - "MAPE": "0.902", - "MASE": "1.5021", - "RMSE": "7.67897562612792" + "Model": { + "AR_Model": "ARX", + "Best_Decomposition": "RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(12)", + "Cycle": "NoCycle", + "Signal_Transoformation": "RelativeDifference", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "44", + "MAE": "5.7529411764705864", + "MAPE": "0.902", + "MASE": "1.5021", + "RMSE": "7.67897562612792" + } } } @@ -126,7 +138,7 @@ Forecasts -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone2' 2.0667121410369873 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone2']' 2.2751309871673584 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1959-03-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone2' Length=51 Min=0.0 Max=26.099999999999998 Mean=5.7529411764705864 StdDev=5.086288871735056 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='RelDiff_Ozone2' Min=-0.9999999681707318 Max=36781608.195402294 Mean=1171963.1590098052 StdDev=5559717.681827764 @@ -152,21 +164,21 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES RelDiff_Ozone2_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Exog4=P_S_Lag1 4660418.708409504 +INFO:pyaf.std:AR_MODEL_COEFF 1 Exog4=P_S_Lag1 4660418.708409503 INFO:pyaf.std:AR_MODEL_COEFF 2 Exog4=P_S_Lag5 3880307.295613807 -INFO:pyaf.std:AR_MODEL_COEFF 3 Exog4=P_U_Lag2 2971362.472427069 -INFO:pyaf.std:AR_MODEL_COEFF 4 Exog4=P_T_Lag4 2310728.753743561 -INFO:pyaf.std:AR_MODEL_COEFF 5 Exog4=P_U_Lag10 1873570.1369204647 -INFO:pyaf.std:AR_MODEL_COEFF 6 Exog4=P_U_Lag3 -1404113.0996582045 -INFO:pyaf.std:AR_MODEL_COEFF 7 Exog4=P_T_Lag5 -1224950.891523218 -INFO:pyaf.std:AR_MODEL_COEFF 8 Exog4=P_U_Lag1 -447988.34764394455 -INFO:pyaf.std:AR_MODEL_COEFF 9 Exog4=P_U_Lag9 441781.7927186434 -INFO:pyaf.std:AR_MODEL_COEFF 10 Exog4=P_U_Lag4 -365013.34041281475 +INFO:pyaf.std:AR_MODEL_COEFF 3 Exog4=P_U_Lag2 2971362.4724270706 +INFO:pyaf.std:AR_MODEL_COEFF 4 Exog4=P_T_Lag4 2310728.753743562 +INFO:pyaf.std:AR_MODEL_COEFF 5 Exog4=P_U_Lag10 1873570.136920467 +INFO:pyaf.std:AR_MODEL_COEFF 6 Exog4=P_U_Lag3 -1404113.0996582021 +INFO:pyaf.std:AR_MODEL_COEFF 7 Exog4=P_T_Lag5 -1224950.891523217 +INFO:pyaf.std:AR_MODEL_COEFF 8 Exog4=P_U_Lag1 -447988.34764394077 +INFO:pyaf.std:AR_MODEL_COEFF 9 Exog4=P_U_Lag9 441781.7927186412 +INFO:pyaf.std:AR_MODEL_COEFF 10 Exog4=P_U_Lag4 -365013.340412811 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.1734843254089355 -INFO:pyaf.std:START_FORECASTING 'Ozone2' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone2' 0.3496437072753906 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 7.637729644775391 +INFO:pyaf.std:START_FORECASTING '['Ozone2']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone2']' 0.8125710487365723 INFO:pyaf.std:START_TRAINING 'Ozone2' Forecast Columns Index(['Time', 'Ozone2', 'row_number', 'Time_Normalized', 'RelDiff_Ozone2', 'RelDiff_Ozone2_ConstantTrend', 'RelDiff_Ozone2_ConstantTrend_residue', @@ -210,31 +222,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone2", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1959-03-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone2": { + "Dataset": { + "Signal": "Ozone2", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1959-03-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 51 + }, + "Model": { + "AR_Model": "ARX", + "Best_Decomposition": "RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(12)", + "Cycle": "NoCycle", + "Signal_Transoformation": "RelativeDifference", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 51 - }, - "Model": { - "AR_Model": "ARX", - "Best_Decomposition": "RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(12)", - "Cycle": "NoCycle", - "Signal_Transoformation": "RelativeDifference", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "44", - "MAE": "5.7529411764705864", - "MAPE": "0.902", - "MASE": "1.5021", - "RMSE": "7.67897562612792" + "Model_Performance": { + "COMPLEXITY": "44", + "MAE": "5.7529411764705864", + "MAPE": "0.902", + "MASE": "1.5021", + "RMSE": "7.67897562612792" + } } } @@ -247,7 +261,7 @@ Forecasts -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone2' 4.404573678970337 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone2']' 4.881984710693359 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1960-12-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone2' Length=102 Min=0.0 Max=26.099999999999998 Mean=5.548039215686275 StdDev=4.275597203835099 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone2' Min=0.0 Max=26.099999999999998 Mean=5.548039215686275 StdDev=4.275597203835099 @@ -260,8 +274,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(25 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=3644101649.3845 MAPE_Forecast=0.3368 MAPE_Test=0.285 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3953 SMAPE_Forecast=0.3242 SMAPE_Test=0.337 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4788 MASE_Forecast=0.7555 MASE_Test=0.5945 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.7504733195812596 L1_Forecast=1.6797892595416455 L1_Test=1.826675658993765 -INFO:pyaf.std:MODEL_L2 L2_Fit=2.4713461939361285 L2_Forecast=2.3717534046265407 L2_Test=2.34823520360096 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.7504733195812596 L1_Forecast=1.6797892595416457 L1_Test=1.826675658993765 +INFO:pyaf.std:MODEL_L2 L2_Fit=2.471346193936128 L2_Forecast=2.3717534046265416 L2_Test=2.34823520360096 INFO:pyaf.std:MODEL_COMPLEXITY 34 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -273,21 +287,21 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone2_PolyTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Exog3=AU_Lag4 1.6292730647328129 -INFO:pyaf.std:AR_MODEL_COEFF 2 Exog2=4_Lag5 1.6292730647328109 -INFO:pyaf.std:AR_MODEL_COEFF 3 Exog3=AT_Lag5 1.6292730647328109 -INFO:pyaf.std:AR_MODEL_COEFF 4 Exog2=3_Lag6 1.6292730647328109 -INFO:pyaf.std:AR_MODEL_COEFF 5 Exog3=AS_Lag6 1.6292730647328109 -INFO:pyaf.std:AR_MODEL_COEFF 6 Exog2=2_Lag7 1.6292730647328109 -INFO:pyaf.std:AR_MODEL_COEFF 7 Exog3=AR_Lag7 1.6292730647328109 -INFO:pyaf.std:AR_MODEL_COEFF 8 Exog2=5_Lag4 1.6292730647328038 -INFO:pyaf.std:AR_MODEL_COEFF 9 Exog3=AQ_Lag8 0.45660056565641427 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone2_PolyTrend_residue_zeroCycle_residue_Lag12 0.16502320433323697 +INFO:pyaf.std:AR_MODEL_COEFF 1 Exog2=5_Lag4 1.6292730647328146 +INFO:pyaf.std:AR_MODEL_COEFF 2 Exog3=AU_Lag4 1.6292730647328098 +INFO:pyaf.std:AR_MODEL_COEFF 3 Exog3=AT_Lag5 1.6292730647328086 +INFO:pyaf.std:AR_MODEL_COEFF 4 Exog2=3_Lag6 1.6292730647328086 +INFO:pyaf.std:AR_MODEL_COEFF 5 Exog3=AS_Lag6 1.6292730647328086 +INFO:pyaf.std:AR_MODEL_COEFF 6 Exog2=2_Lag7 1.6292730647328086 +INFO:pyaf.std:AR_MODEL_COEFF 7 Exog3=AR_Lag7 1.6292730647328086 +INFO:pyaf.std:AR_MODEL_COEFF 8 Exog2=4_Lag5 1.629273064732808 +INFO:pyaf.std:AR_MODEL_COEFF 9 Exog3=AQ_Lag8 0.45660056565641205 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone2_PolyTrend_residue_zeroCycle_residue_Lag12 0.16502320433323742 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.397237300872803 -INFO:pyaf.std:START_FORECASTING 'Ozone2' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone2' 0.3703117370605469 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 6.852611780166626 +INFO:pyaf.std:START_FORECASTING '['Ozone2']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone2']' 1.0146572589874268 INFO:pyaf.std:START_TRAINING 'Ozone2' Forecast Columns Index(['Time', 'Ozone2', 'row_number', 'Time_Normalized', '_Ozone2', 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone2_PolyTrend', @@ -316,47 +330,49 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 2.8 KB None Forecasts - [[Timestamp('1963-07-01 00:00:00') nan 4.0608579682043455] - [Timestamp('1963-08-01 00:00:00') nan 3.620314033462324] - [Timestamp('1963-09-01 00:00:00') nan 17.508836416137918] - [Timestamp('1963-10-01 00:00:00') nan 3.448711850062719] - [Timestamp('1963-11-01 00:00:00') nan 3.0566389893224977] - [Timestamp('1963-12-01 00:00:00') nan 3.523288384905925] - [Timestamp('1964-01-01 00:00:00') nan 3.097518222732897] - [Timestamp('1964-02-01 00:00:00') nan 3.034445323087833] - [Timestamp('1964-03-01 00:00:00') nan 2.923320217146667] - [Timestamp('1964-04-01 00:00:00') nan 2.942068598245868] - [Timestamp('1964-05-01 00:00:00') nan 3.5390502082221253] - [Timestamp('1964-06-01 00:00:00') nan 3.6396059474900246]] + [[Timestamp('1963-07-01 00:00:00') nan 4.06085796820435] + [Timestamp('1963-08-01 00:00:00') nan 3.6203140334623276] + [Timestamp('1963-09-01 00:00:00') nan 17.508836416137928] + [Timestamp('1963-10-01 00:00:00') nan 3.448711850062723] + [Timestamp('1963-11-01 00:00:00') nan 3.0566389893225003] + [Timestamp('1963-12-01 00:00:00') nan 3.523288384905929] + [Timestamp('1964-01-01 00:00:00') nan 3.0975182227328992] + [Timestamp('1964-02-01 00:00:00') nan 3.0344453230878354] + [Timestamp('1964-03-01 00:00:00') nan 2.9233202171466686] + [Timestamp('1964-04-01 00:00:00') nan 2.9420685982458705] + [Timestamp('1964-05-01 00:00:00') nan 3.5390502082221307] + [Timestamp('1964-06-01 00:00:00') nan 3.6396059474900304]] { - "Dataset": { - "Signal": "Ozone2", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1963-06-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone2": { + "Dataset": { + "Signal": "Ozone2", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1963-06-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 102 }, - "Training_Signal_Length": 102 - }, - "Model": { - "AR_Model": "ARX", - "Best_Decomposition": "_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(25)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "PolyTrend" - }, - "Model_Performance": { - "COMPLEXITY": "34", - "MAE": "1.6797892595416455", - "MAPE": "0.3368", - "MASE": "0.7555", - "RMSE": "2.3717534046265407" + "Model": { + "AR_Model": "ARX", + "Best_Decomposition": "_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(25)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "34", + "MAE": "1.6797892595416457", + "MAPE": "0.3368", + "MASE": "0.7555", + "RMSE": "2.3717534046265416" + } } } @@ -369,7 +385,7 @@ Forecasts -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone2' 7.5378031730651855 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone2']' 7.077742338180542 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone2' Length=204 Min=0.0 Max=26.099999999999998 Mean=5.529411764705882 StdDev=3.838506864406639 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone2' Min=0.0 Max=26.099999999999998 Mean=5.529411764705882 StdDev=3.838506864406639 @@ -382,8 +398,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=2109152020.0645 MAPE_Forecast=0.3014 MAPE_Test=0.3076 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3346 SMAPE_Forecast=0.2841 SMAPE_Test=0.2663 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5056 MASE_Forecast=0.5276 MASE_Test=0.6554 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.647664584531132 L1_Forecast=1.4994398611500226 L1_Test=1.1677849321896334 -INFO:pyaf.std:MODEL_L2 L2_Fit=2.451342315346558 L2_Forecast=2.057703776386393 L2_Test=1.3080463795624167 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.64766458453113 L1_Forecast=1.4994398611500228 L1_Test=1.1677849321896348 +INFO:pyaf.std:MODEL_L2 L2_Fit=2.4513423153465532 L2_Forecast=2.0577037763863935 L2_Test=1.3080463795624186 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -395,21 +411,21 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone2_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Exog3=AS_Lag6 1.6691989661011695 -INFO:pyaf.std:AR_MODEL_COEFF 2 Exog2=4_Lag5 1.6691989661011675 -INFO:pyaf.std:AR_MODEL_COEFF 3 Exog2=3_Lag6 1.6691989661011661 -INFO:pyaf.std:AR_MODEL_COEFF 4 Exog3=AT_Lag5 1.6691989661011613 -INFO:pyaf.std:AR_MODEL_COEFF 5 Exog3=AU_Lag4 1.6691989661011597 -INFO:pyaf.std:AR_MODEL_COEFF 6 Exog3=AR_Lag7 1.6691989661011561 -INFO:pyaf.std:AR_MODEL_COEFF 7 Exog2=5_Lag4 1.6691989661011484 -INFO:pyaf.std:AR_MODEL_COEFF 8 Exog2=2_Lag7 1.669198966101129 -INFO:pyaf.std:AR_MODEL_COEFF 9 Exog2=4_Lag29 -0.8383550959252106 -INFO:pyaf.std:AR_MODEL_COEFF 10 Exog2=3_Lag30 -0.8383550959251898 +INFO:pyaf.std:AR_MODEL_COEFF 1 Exog2=3_Lag6 1.6691989661011786 +INFO:pyaf.std:AR_MODEL_COEFF 2 Exog2=4_Lag5 1.6691989661011652 +INFO:pyaf.std:AR_MODEL_COEFF 3 Exog2=2_Lag7 1.6691989661011597 +INFO:pyaf.std:AR_MODEL_COEFF 4 Exog3=AR_Lag7 1.6691989661011553 +INFO:pyaf.std:AR_MODEL_COEFF 5 Exog3=AS_Lag6 1.6691989661011526 +INFO:pyaf.std:AR_MODEL_COEFF 6 Exog3=AT_Lag5 1.669198966101151 +INFO:pyaf.std:AR_MODEL_COEFF 7 Exog3=AU_Lag4 1.6691989661011466 +INFO:pyaf.std:AR_MODEL_COEFF 8 Exog2=5_Lag4 1.6691989661011444 +INFO:pyaf.std:AR_MODEL_COEFF 9 Exog2=5_Lag28 -0.8383550959251904 +INFO:pyaf.std:AR_MODEL_COEFF 10 Exog2=3_Lag30 -0.838355095925184 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.377824068069458 -INFO:pyaf.std:START_FORECASTING 'Ozone2' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone2' 0.4563100337982178 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 6.9178383350372314 +INFO:pyaf.std:START_FORECASTING '['Ozone2']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone2']' 1.0162174701690674 Forecast Columns Index(['Time', 'Ozone2', 'row_number', 'Time_Normalized', '_Ozone2', '_Ozone2_LinearTrend', '_Ozone2_LinearTrend_residue', '_Ozone2_LinearTrend_residue_zeroCycle', @@ -437,47 +453,49 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 5.2 KB None Forecasts - [[Timestamp('1972-01-01 00:00:00') nan 3.797911129980429] - [Timestamp('1972-02-01 00:00:00') nan 3.3115498555176215] - [Timestamp('1972-03-01 00:00:00') nan 3.4458907904325797] - [Timestamp('1972-04-01 00:00:00') nan 3.7802236485042733] - [Timestamp('1972-05-01 00:00:00') nan 5.09982474330621] - [Timestamp('1972-06-01 00:00:00') nan 5.393125188036233] - [Timestamp('1972-07-01 00:00:00') nan 5.721529861809875] - [Timestamp('1972-08-01 00:00:00') nan 5.985167766318561] - [Timestamp('1972-09-01 00:00:00') nan 12.4450901408844] - [Timestamp('1972-10-01 00:00:00') nan 4.221582996482068] - [Timestamp('1972-11-01 00:00:00') nan 3.846949088923938] - [Timestamp('1972-12-01 00:00:00') nan 3.46950454477462]] + [[Timestamp('1972-01-01 00:00:00') nan 3.79791112998043] + [Timestamp('1972-02-01 00:00:00') nan 3.311549855517625] + [Timestamp('1972-03-01 00:00:00') nan 3.445890790432582] + [Timestamp('1972-04-01 00:00:00') nan 3.7802236485042746] + [Timestamp('1972-05-01 00:00:00') nan 5.099824743306211] + [Timestamp('1972-06-01 00:00:00') nan 5.393125188036231] + [Timestamp('1972-07-01 00:00:00') nan 5.7215298618098736] + [Timestamp('1972-08-01 00:00:00') nan 5.985167766318559] + [Timestamp('1972-09-01 00:00:00') nan 12.44509014088441] + [Timestamp('1972-10-01 00:00:00') nan 4.221582996482066] + [Timestamp('1972-11-01 00:00:00') nan 3.8469490889239393] + [Timestamp('1972-12-01 00:00:00') nan 3.4695045447746224]] { - "Dataset": { - "Signal": "Ozone2", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone2": { + "Dataset": { + "Signal": "Ozone2", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "ARX", + "Best_Decomposition": "_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "ARX", - "Best_Decomposition": "_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "1.4994398611500226", - "MAPE": "0.3014", - "MASE": "0.5276", - "RMSE": "2.057703776386393" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "1.4994398611500228", + "MAPE": "0.3014", + "MASE": "0.5276", + "RMSE": "2.0577037763863935" + } } } diff --git a/tests/references/expsmooth_expsmooth_dataset_ausgdp.log b/tests/references/expsmooth_expsmooth_dataset_ausgdp.log index 3391ec1e5..fd693ed6f 100644 --- a/tests/references/expsmooth_expsmooth_dataset_ausgdp.log +++ b/tests/references/expsmooth_expsmooth_dataset_ausgdp.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'ausgdp' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ausgdp' 4.073939800262451 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ausgdp']' 3.1502797603607178 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1971.5 TimeMax=1992.25 TimeDelta=0.25 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ausgdp' Length=107 Min=4612 Max=7618 Mean=5870.186915887851 StdDev=802.9723651957509 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_ausgdp' Min=4612 Max=7618 Mean=5870.186915887851 StdDev=802.9723651957509 @@ -11,24 +11,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_ausgdp_ConstantTrend_residue_zeroCycle_residue_AR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0021 MAPE_Forecast=0.0019 MAPE_Test=0.0024 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0021 SMAPE_Forecast=0.0019 SMAPE_Test=0.0024 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2958 MASE_Forecast=0.2526 MASE_Test=0.2681 -INFO:pyaf.std:MODEL_L1 L1_Fit=11.195246643965545 L1_Forecast=13.199465577850441 L1_Test=18.230923553006505 -INFO:pyaf.std:MODEL_L2 L2_Fit=15.827943136904903 L2_Forecast=14.901713960799588 L2_Test=19.805574250012764 +INFO:pyaf.std:MODEL_L1 L1_Fit=11.195246643965534 L1_Forecast=13.19946557785239 L1_Test=18.230923553011053 +INFO:pyaf.std:MODEL_L2 L2_Fit=15.82794313690497 L2_Forecast=14.90171396080241 L2_Test=19.805574250016594 INFO:pyaf.std:MODEL_COMPLEXITY 21 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5558.428571428572 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _ausgdp_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag2 -3.110320793947267 -INFO:pyaf.std:AR_MODEL_COEFF 2 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag1 2.6140275565089217 -INFO:pyaf.std:AR_MODEL_COEFF 3 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag3 2.452202147592248 -INFO:pyaf.std:AR_MODEL_COEFF 4 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag4 -1.465163419911378 -INFO:pyaf.std:AR_MODEL_COEFF 5 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag17 1.2168693744095638 -INFO:pyaf.std:AR_MODEL_COEFF 6 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag16 -1.1609301412695543 -INFO:pyaf.std:AR_MODEL_COEFF 7 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag9 -1.1198618293372924 -INFO:pyaf.std:AR_MODEL_COEFF 8 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag10 1.1081983493108365 -INFO:pyaf.std:AR_MODEL_COEFF 9 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.9162426723034237 -INFO:pyaf.std:AR_MODEL_COEFF 10 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag15 0.8541504467350307 +INFO:pyaf.std:AR_MODEL_COEFF 1 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag2 -3.1103207939472606 +INFO:pyaf.std:AR_MODEL_COEFF 2 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag1 2.6140275565089177 +INFO:pyaf.std:AR_MODEL_COEFF 3 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag3 2.4522021475922346 +INFO:pyaf.std:AR_MODEL_COEFF 4 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag4 -1.465163419911347 +INFO:pyaf.std:AR_MODEL_COEFF 5 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag17 1.216869374409576 +INFO:pyaf.std:AR_MODEL_COEFF 6 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag16 -1.1609301412695878 +INFO:pyaf.std:AR_MODEL_COEFF 7 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag9 -1.1198618293373002 +INFO:pyaf.std:AR_MODEL_COEFF 8 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag10 1.1081983493108394 +INFO:pyaf.std:AR_MODEL_COEFF 9 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.9162426723034127 +INFO:pyaf.std:AR_MODEL_COEFF 10 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag15 0.8541504467350707 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'ausgdp' PERFORMANCE MAPE_FORECAST ausgdp 0.0019 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ausgdp' 4.858598947525024 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ausgdp']' 3.297628879547119 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1971.5 TimeMax=1991.75 TimeDelta=0.25 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ausgdp' Length=107 Min=4612 Max=7618 Mean=5870.186915887851 StdDev=802.9723651957509 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_ausgdp' Min=4612 Max=7618 Mean=5870.186915887851 StdDev=802.9723651957509 @@ -40,24 +49,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_ausgdp_ConstantTrend_residue_zeroCycle_residue_AR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0021 MAPE_Forecast=0.0018 MAPE_Test=0.0023 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0021 SMAPE_Forecast=0.0018 SMAPE_Test=0.0023 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2936 MASE_Forecast=0.2489 MASE_Test=0.2388 -INFO:pyaf.std:MODEL_L1 L1_Fit=11.207285251086843 L1_Forecast=12.072512378441692 L1_Test=17.350466428555592 -INFO:pyaf.std:MODEL_L2 L2_Fit=15.90883937931806 L2_Forecast=13.41305510876767 L2_Test=19.227339797806238 +INFO:pyaf.std:MODEL_L1 L1_Fit=11.207285251087175 L1_Forecast=12.072512378439612 L1_Test=17.35046642854786 +INFO:pyaf.std:MODEL_L2 L2_Fit=15.908839379318046 L2_Forecast=13.413055108764677 L2_Test=19.227339797799647 INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5538.5 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _ausgdp_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag2 -3.093183318971164 -INFO:pyaf.std:AR_MODEL_COEFF 2 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag1 2.6085439466081155 -INFO:pyaf.std:AR_MODEL_COEFF 3 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag3 2.4192692606067334 -INFO:pyaf.std:AR_MODEL_COEFF 4 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag4 -1.4319034015155125 -INFO:pyaf.std:AR_MODEL_COEFF 5 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag17 1.2490577938710983 -INFO:pyaf.std:AR_MODEL_COEFF 6 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag16 -1.1880429342709218 -INFO:pyaf.std:AR_MODEL_COEFF 7 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag9 -1.093372226112354 -INFO:pyaf.std:AR_MODEL_COEFF 8 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag10 1.0784689541953218 -INFO:pyaf.std:AR_MODEL_COEFF 9 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.931476443481313 -INFO:pyaf.std:AR_MODEL_COEFF 10 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag15 0.8782329237784632 +INFO:pyaf.std:AR_MODEL_COEFF 1 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag2 -3.0931833189711426 +INFO:pyaf.std:AR_MODEL_COEFF 2 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag1 2.608543946608111 +INFO:pyaf.std:AR_MODEL_COEFF 3 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag3 2.4192692606067023 +INFO:pyaf.std:AR_MODEL_COEFF 4 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag4 -1.4319034015154732 +INFO:pyaf.std:AR_MODEL_COEFF 5 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag17 1.2490577938710679 +INFO:pyaf.std:AR_MODEL_COEFF 6 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag16 -1.1880429342708874 +INFO:pyaf.std:AR_MODEL_COEFF 7 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag9 -1.093372226112345 +INFO:pyaf.std:AR_MODEL_COEFF 8 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag10 1.0784689541953103 +INFO:pyaf.std:AR_MODEL_COEFF 9 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.9314764434812824 +INFO:pyaf.std:AR_MODEL_COEFF 10 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag15 0.8782329237784229 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'ausgdp' PERFORMANCE MAPE_FORECAST ausgdp 0.0018 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ausgdp' 4.2455055713653564 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ausgdp']' 3.025616407394409 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1971.5 TimeMax=1991.0 TimeDelta=0.25 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ausgdp' Length=107 Min=4612 Max=7618 Mean=5870.186915887851 StdDev=802.9723651957509 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_ausgdp' Min=4612 Max=7618 Mean=5870.186915887851 StdDev=802.9723651957509 @@ -69,24 +87,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_ausgdp_ConstantTrend_residue_zeroCycle_residue_AR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0022 MAPE_Forecast=0.0016 MAPE_Test=0.002 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0022 SMAPE_Forecast=0.0016 SMAPE_Test=0.002 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.301 MASE_Forecast=0.2348 MASE_Test=0.2535 -INFO:pyaf.std:MODEL_L1 L1_Fit=11.645733407191788 L1_Forecast=10.665430397213186 L1_Test=14.70029980208858 -INFO:pyaf.std:MODEL_L2 L2_Fit=16.313919654570093 L2_Forecast=11.884823342432034 L2_Test=16.165936589958974 +INFO:pyaf.std:MODEL_L1 L1_Fit=11.645733407191926 L1_Forecast=10.665430397212685 L1_Test=14.700299802082895 +INFO:pyaf.std:MODEL_L2 L2_Fit=16.313919654570128 L2_Forecast=11.884823342432055 L2_Test=16.165936589954107 INFO:pyaf.std:MODEL_COMPLEXITY 19 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5508.139240506329 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _ausgdp_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag2 -3.039684643121582 -INFO:pyaf.std:AR_MODEL_COEFF 2 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag1 2.590470372038635 -INFO:pyaf.std:AR_MODEL_COEFF 3 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag3 2.3094970504571477 -INFO:pyaf.std:AR_MODEL_COEFF 4 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag4 -1.2950863496766418 -INFO:pyaf.std:AR_MODEL_COEFF 5 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag9 -1.041548860621059 -INFO:pyaf.std:AR_MODEL_COEFF 6 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.9773774952706298 -INFO:pyaf.std:AR_MODEL_COEFF 7 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag10 0.9491944539036044 -INFO:pyaf.std:AR_MODEL_COEFF 8 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag17 0.8949676843557202 -INFO:pyaf.std:AR_MODEL_COEFF 9 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag15 0.7750960402584268 -INFO:pyaf.std:AR_MODEL_COEFF 10 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag8 0.6560721956690676 +INFO:pyaf.std:AR_MODEL_COEFF 1 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag2 -3.03968464312159 +INFO:pyaf.std:AR_MODEL_COEFF 2 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag1 2.590470372038639 +INFO:pyaf.std:AR_MODEL_COEFF 3 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag3 2.309497050457151 +INFO:pyaf.std:AR_MODEL_COEFF 4 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag4 -1.2950863496766383 +INFO:pyaf.std:AR_MODEL_COEFF 5 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag9 -1.0415488606210335 +INFO:pyaf.std:AR_MODEL_COEFF 6 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.9773774952706179 +INFO:pyaf.std:AR_MODEL_COEFF 7 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag10 0.9491944539035846 +INFO:pyaf.std:AR_MODEL_COEFF 8 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag17 0.8949676843557177 +INFO:pyaf.std:AR_MODEL_COEFF 9 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag15 0.775096040258402 +INFO:pyaf.std:AR_MODEL_COEFF 10 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag8 0.6560721956690413 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'ausgdp' PERFORMANCE MAPE_FORECAST ausgdp 0.0016 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ausgdp' 3.2233948707580566 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ausgdp']' 2.9860994815826416 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1971.5 TimeMax=1990.25 TimeDelta=0.25 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ausgdp' Length=107 Min=4612 Max=7618 Mean=5870.186915887851 StdDev=802.9723651957509 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_ausgdp' Min=4612 Max=7618 Mean=5870.186915887851 StdDev=802.9723651957509 @@ -98,19 +125,28 @@ INFO:pyaf.std:AUTOREG_DETAIL '_ausgdp_ConstantTrend_residue_zeroCycle_residue_AR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0022 MAPE_Forecast=0.0024 MAPE_Test=0.0012 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0022 SMAPE_Forecast=0.0024 SMAPE_Test=0.0012 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3015 MASE_Forecast=0.3555 MASE_Test=0.1567 -INFO:pyaf.std:MODEL_L1 L1_Fit=11.640550448287836 L1_Forecast=15.720829477578071 L1_Test=8.549754525498352 -INFO:pyaf.std:MODEL_L2 L2_Fit=16.130092557337278 L2_Forecast=18.510163572281034 L2_Test=9.937731174194802 +INFO:pyaf.std:MODEL_L1 L1_Fit=11.64055044828798 L1_Forecast=15.720829477578599 L1_Test=8.54975452549555 +INFO:pyaf.std:MODEL_L2 L2_Fit=16.13009255733731 L2_Forecast=18.510163572282213 L2_Test=9.937731174192505 INFO:pyaf.std:MODEL_COMPLEXITY 19 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5471.328947368421 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _ausgdp_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_COEFF 1 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag2 -2.799669802019456 -INFO:pyaf.std:AR_MODEL_COEFF 2 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag1 2.477862163777265 -INFO:pyaf.std:AR_MODEL_COEFF 3 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag3 2.0673145702632274 -INFO:pyaf.std:AR_MODEL_COEFF 4 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag4 -1.1196232968137845 -INFO:pyaf.std:AR_MODEL_COEFF 5 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.7706982343050324 -INFO:pyaf.std:AR_MODEL_COEFF 6 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag10 0.6957781333309259 -INFO:pyaf.std:AR_MODEL_COEFF 7 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag8 0.4710753016350799 -INFO:pyaf.std:AR_MODEL_COEFF 8 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag15 0.36714790041169465 -INFO:pyaf.std:AR_MODEL_COEFF 9 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag5 0.36554132879783807 -INFO:pyaf.std:AR_MODEL_COEFF 10 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.3310837920059687 +INFO:pyaf.std:AR_MODEL_COEFF 2 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag1 2.477862163777271 +INFO:pyaf.std:AR_MODEL_COEFF 3 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag3 2.0673145702632216 +INFO:pyaf.std:AR_MODEL_COEFF 4 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag4 -1.1196232968137614 +INFO:pyaf.std:AR_MODEL_COEFF 5 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.7706982343050179 +INFO:pyaf.std:AR_MODEL_COEFF 6 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag10 0.6957781333308851 +INFO:pyaf.std:AR_MODEL_COEFF 7 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag8 0.4710753016350889 +INFO:pyaf.std:AR_MODEL_COEFF 8 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag15 0.36714790041172696 +INFO:pyaf.std:AR_MODEL_COEFF 9 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag5 0.36554132879779455 +INFO:pyaf.std:AR_MODEL_COEFF 10 _ausgdp_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.33108379200591087 INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST ausgdp 0.0024 diff --git a/tests/references/expsmooth_expsmooth_dataset_bonds.log b/tests/references/expsmooth_expsmooth_dataset_bonds.log index 4ca4e8b4d..e9572b44d 100644 --- a/tests/references/expsmooth_expsmooth_dataset_bonds.log +++ b/tests/references/expsmooth_expsmooth_dataset_bonds.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'bonds' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'bonds' 4.914234161376953 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['bonds']' 3.124946117401123 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1994.0 TimeMax=2002.08333333333 TimeDelta=0.08333333333329973 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='bonds' Length=125 Min=3.32 Max=8.12 Mean=5.683040000000001 StdDev=1.0925333671792363 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_bonds' Min=3.32 Max=8.12 Mean=5.683040000000001 StdDev=1.0925333671792363 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9898 MASE_Forecast=1.0435 MASE_Test=1.1711 INFO:pyaf.std:MODEL_L1 L1_Fit=0.2003061224489796 L1_Forecast=0.19000000000000006 L1_Test=0.44500000000000006 INFO:pyaf.std:MODEL_L2 L2_Fit=0.2399085710202526 L2_Forecast=0.25119713374160946 L2_Test=0.44972213643537723 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 5.83 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _bonds_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'bonds' PERFORMANCE MAPE_FORECAST bonds 0.0459 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'bonds' 4.411815404891968 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['bonds']' 3.5077414512634277 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1994.0 TimeMax=2001.91666666667 TimeDelta=0.08333333333336763 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='bonds' Length=125 Min=3.32 Max=8.12 Mean=5.683040000000001 StdDev=1.0925333671792363 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_bonds' Min=3.32 Max=8.12 Mean=5.683040000000001 StdDev=1.0925333671792363 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9896 MASE_Forecast=0.9748 MASE_Test=0.7961 INFO:pyaf.std:MODEL_L1 L1_Fit=0.20270833333333335 L1_Forecast=0.18400000000000008 L1_Test=0.3025 INFO:pyaf.std:MODEL_L2 L2_Fit=0.24207436873820407 L2_Forecast=0.24698178070456947 L2_Test=0.3434748899119119 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 5.83 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _bonds_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'bonds' PERFORMANCE MAPE_FORECAST bonds 0.044 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'bonds' 5.050215244293213 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['bonds']' 3.5746500492095947 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1994.0 TimeMax=2001.66666666667 TimeDelta=0.08333333333336876 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='bonds' Length=125 Min=3.32 Max=8.12 Mean=5.683040000000001 StdDev=1.0925333671792363 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_bonds' Min=3.32 Max=8.12 Mean=5.683040000000001 StdDev=1.0925333671792363 @@ -49,24 +67,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_bonds_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0282 MAPE_Forecast=0.0558 MAPE_Test=0.0677 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0281 SMAPE_Forecast=0.0546 SMAPE_Test=0.0663 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8406 MASE_Forecast=1.1002 MASE_Test=1.4227 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.17113704414389558 L1_Forecast=0.23535004906294374 L1_Test=0.2824994439164555 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.21364945997935747 L2_Forecast=0.2778191370797826 L2_Test=0.29944536505389513 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.17113704414389566 L1_Forecast=0.23535004906294335 L1_Test=0.28249944391645493 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.21364945997935753 L2_Forecast=0.27781913707978245 L2_Test=0.2994453650538947 INFO:pyaf.std:MODEL_COMPLEXITY 23 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.15010752688172 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _bonds_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9930055455795113 -INFO:pyaf.std:AR_MODEL_COEFF 2 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.19946456784314207 -INFO:pyaf.std:AR_MODEL_COEFF 3 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag27 -0.19581839049993136 -INFO:pyaf.std:AR_MODEL_COEFF 4 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag6 0.12705258447027612 -INFO:pyaf.std:AR_MODEL_COEFF 5 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag26 0.12321565731201084 -INFO:pyaf.std:AR_MODEL_COEFF 6 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag7 0.10166655952389686 -INFO:pyaf.std:AR_MODEL_COEFF 7 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.08586958532707357 -INFO:pyaf.std:AR_MODEL_COEFF 8 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag16 0.07109092581297077 -INFO:pyaf.std:AR_MODEL_COEFF 9 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag23 0.06952565340269404 -INFO:pyaf.std:AR_MODEL_COEFF 10 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag29 0.06826595095093321 +INFO:pyaf.std:AR_MODEL_COEFF 1 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9930055455795115 +INFO:pyaf.std:AR_MODEL_COEFF 2 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.19946456784314212 +INFO:pyaf.std:AR_MODEL_COEFF 3 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag27 -0.19581839049993144 +INFO:pyaf.std:AR_MODEL_COEFF 4 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag6 0.12705258447027615 +INFO:pyaf.std:AR_MODEL_COEFF 5 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag26 0.12321565731201078 +INFO:pyaf.std:AR_MODEL_COEFF 6 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag7 0.10166655952389696 +INFO:pyaf.std:AR_MODEL_COEFF 7 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.08586958532707384 +INFO:pyaf.std:AR_MODEL_COEFF 8 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag16 0.0710909258129706 +INFO:pyaf.std:AR_MODEL_COEFF 9 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag23 0.06952565340269362 +INFO:pyaf.std:AR_MODEL_COEFF 10 _bonds_ConstantTrend_residue_zeroCycle_residue_Lag29 0.06826595095093316 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'bonds' PERFORMANCE MAPE_FORECAST bonds 0.0558 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'bonds' 3.303277015686035 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['bonds']' 2.545306921005249 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1994.0 TimeMax=2001.41666666667 TimeDelta=0.08333333333336995 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='bonds' Length=125 Min=3.32 Max=8.12 Mean=5.683040000000001 StdDev=1.0925333671792363 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_bonds' Min=3.32 Max=8.12 Mean=5.683040000000001 StdDev=1.0925333671792363 @@ -81,6 +108,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9889 MASE_Forecast=0.9635 MASE_Test=0.9988 INFO:pyaf.std:MODEL_L1 L1_Fit=0.20255555555555552 L1_Forecast=0.17913043478260873 L1_Test=0.2433333333333335 INFO:pyaf.std:MODEL_L2 L2_Fit=0.24160803886552376 L2_Forecast=0.22515695009018277 L2_Test=0.3165964834506748 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 5.83 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _bonds_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST bonds 0.0403 diff --git a/tests/references/expsmooth_expsmooth_dataset_canadagas.log b/tests/references/expsmooth_expsmooth_dataset_canadagas.log index b363aaa2d..3b2d6e4f7 100644 --- a/tests/references/expsmooth_expsmooth_dataset_canadagas.log +++ b/tests/references/expsmooth_expsmooth_dataset_canadagas.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'canadagas' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'canadagas' 6.65060830116272 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['canadagas']' 4.974682807922363 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1960.0 TimeMax=1995.91666666667 TimeDelta=0.08333333333334089 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='canadagas' Length=542 Min=0.966 Max=19.5284 Mean=9.776504981549817 StdDev=5.135749933391726 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_canadagas' Min=0.966 Max=19.5284 Mean=9.776504981549817 StdDev=5.135749933391726 @@ -11,24 +11,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_canadagas_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0285 MAPE_Forecast=0.0165 MAPE_Test=0.0199 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0284 SMAPE_Forecast=0.0165 SMAPE_Test=0.0195 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3794 MASE_Forecast=0.3887 MASE_Test=0.1314 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.18478320913421792 L1_Forecast=0.2872351594239203 L1_Test=0.33952886151105766 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.2429148373906406 L2_Forecast=0.3473323715313845 L2_Test=0.4520671724573957 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.18478320913421756 L1_Forecast=0.28723515942391614 L1_Test=0.33952886151105766 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.24291483739064051 L2_Forecast=0.3473323715313819 L2_Test=0.452067172457384 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 7.817603009259259 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _canadagas_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6940200838464898 -INFO:pyaf.std:AR_MODEL_COEFF 2 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag48 0.3369132028984424 -INFO:pyaf.std:AR_MODEL_COEFF 3 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag24 0.23265236957829083 -INFO:pyaf.std:AR_MODEL_COEFF 4 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.2240800458891845 -INFO:pyaf.std:AR_MODEL_COEFF 5 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag12 0.2028422077904218 -INFO:pyaf.std:AR_MODEL_COEFF 6 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag36 0.2015686547077008 -INFO:pyaf.std:AR_MODEL_COEFF 7 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.1932883761469899 -INFO:pyaf.std:AR_MODEL_COEFF 8 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag52 0.18825678700564114 -INFO:pyaf.std:AR_MODEL_COEFF 9 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.1721370262957081 -INFO:pyaf.std:AR_MODEL_COEFF 10 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.16931349990716082 +INFO:pyaf.std:AR_MODEL_COEFF 1 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6940200838464896 +INFO:pyaf.std:AR_MODEL_COEFF 2 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag48 0.33691320289844495 +INFO:pyaf.std:AR_MODEL_COEFF 3 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag24 0.2326523695782901 +INFO:pyaf.std:AR_MODEL_COEFF 4 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.2240800458891838 +INFO:pyaf.std:AR_MODEL_COEFF 5 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag12 0.2028422077904204 +INFO:pyaf.std:AR_MODEL_COEFF 6 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag36 0.20156865470770144 +INFO:pyaf.std:AR_MODEL_COEFF 7 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.193288376146989 +INFO:pyaf.std:AR_MODEL_COEFF 8 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag52 0.18825678700564163 +INFO:pyaf.std:AR_MODEL_COEFF 9 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.17213702629570865 +INFO:pyaf.std:AR_MODEL_COEFF 10 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.169313499907161 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'canadagas' PERFORMANCE MAPE_FORECAST canadagas 0.0165 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'canadagas' 6.932136297225952 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['canadagas']' 4.1238343715667725 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1960.0 TimeMax=1995.75 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='canadagas' Length=542 Min=0.966 Max=19.5284 Mean=9.776504981549817 StdDev=5.135749933391726 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_canadagas' Min=0.966 Max=19.5284 Mean=9.776504981549817 StdDev=5.135749933391726 @@ -40,24 +49,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_canadagas_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0286 MAPE_Forecast=0.0165 MAPE_Test=0.0128 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0285 SMAPE_Forecast=0.0164 SMAPE_Test=0.0127 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3804 MASE_Forecast=0.3906 MASE_Test=0.1576 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.18543509122274957 L1_Forecast=0.28620753796111764 L1_Test=0.224871576133979 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.24345226373220508 L2_Forecast=0.34693284998091356 L2_Test=0.3320688217375438 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.18543509122274945 L1_Forecast=0.2862075379611199 L1_Test=0.22487157613397724 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.24345226373220502 L2_Forecast=0.3469328499809144 L2_Test=0.3320688217375461 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 7.776982325581395 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _canadagas_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6939721478974874 -INFO:pyaf.std:AR_MODEL_COEFF 2 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag48 0.3385012560728566 -INFO:pyaf.std:AR_MODEL_COEFF 3 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag24 0.2305183297360767 -INFO:pyaf.std:AR_MODEL_COEFF 4 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.2245269951278609 -INFO:pyaf.std:AR_MODEL_COEFF 5 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag12 0.20206700713456113 -INFO:pyaf.std:AR_MODEL_COEFF 6 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag36 0.20204338014570494 -INFO:pyaf.std:AR_MODEL_COEFF 7 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19326404137551068 -INFO:pyaf.std:AR_MODEL_COEFF 8 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag52 0.1850592389166209 -INFO:pyaf.std:AR_MODEL_COEFF 9 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.1712953953275862 -INFO:pyaf.std:AR_MODEL_COEFF 10 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.168731179878255 +INFO:pyaf.std:AR_MODEL_COEFF 1 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6939721478974873 +INFO:pyaf.std:AR_MODEL_COEFF 2 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag48 0.3385012560728575 +INFO:pyaf.std:AR_MODEL_COEFF 3 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag24 0.230518329736076 +INFO:pyaf.std:AR_MODEL_COEFF 4 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.22452699512785984 +INFO:pyaf.std:AR_MODEL_COEFF 5 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag12 0.2020670071345605 +INFO:pyaf.std:AR_MODEL_COEFF 6 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag36 0.20204338014570586 +INFO:pyaf.std:AR_MODEL_COEFF 7 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19326404137551015 +INFO:pyaf.std:AR_MODEL_COEFF 8 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag52 0.18505923891662074 +INFO:pyaf.std:AR_MODEL_COEFF 9 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.1712953953275853 +INFO:pyaf.std:AR_MODEL_COEFF 10 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.16873117987825445 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'canadagas' PERFORMANCE MAPE_FORECAST canadagas 0.0165 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'canadagas' 5.2138073444366455 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['canadagas']' 3.7754786014556885 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1960.0 TimeMax=1995.5 TimeDelta=0.08333333333333333 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='canadagas' Length=542 Min=0.966 Max=19.5284 Mean=9.776504981549817 StdDev=5.135749933391726 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_canadagas' Min=0.966 Max=19.5284 Mean=9.776504981549817 StdDev=5.135749933391726 @@ -69,24 +87,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_canadagas_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0288 MAPE_Forecast=0.0164 MAPE_Test=0.0114 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0287 SMAPE_Forecast=0.0164 SMAPE_Test=0.0113 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3818 MASE_Forecast=0.3898 MASE_Test=0.2268 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.18582885430158436 L1_Forecast=0.2849336343070568 L1_Test=0.20015195804316388 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.24399098525795423 L2_Forecast=0.3476684959823442 L2_Test=0.2717464335171317 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.18582885430158438 L1_Forecast=0.2849336343070586 L1_Test=0.2001519580431652 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.24399098525795423 L2_Forecast=0.3476684959823449 L2_Test=0.2717464335171357 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 7.721799531615924 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _canadagas_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6947775676605459 -INFO:pyaf.std:AR_MODEL_COEFF 2 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag48 0.34532749639379245 -INFO:pyaf.std:AR_MODEL_COEFF 3 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.23108921093899892 -INFO:pyaf.std:AR_MODEL_COEFF 4 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag24 0.22961299628525086 -INFO:pyaf.std:AR_MODEL_COEFF 5 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag12 0.2005495323564222 -INFO:pyaf.std:AR_MODEL_COEFF 6 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag36 0.19697576939610623 -INFO:pyaf.std:AR_MODEL_COEFF 7 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19376817942880384 -INFO:pyaf.std:AR_MODEL_COEFF 8 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag52 0.18511490865786628 -INFO:pyaf.std:AR_MODEL_COEFF 9 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.17324734082714027 -INFO:pyaf.std:AR_MODEL_COEFF 10 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.1711032616656224 +INFO:pyaf.std:AR_MODEL_COEFF 1 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6947775676605457 +INFO:pyaf.std:AR_MODEL_COEFF 2 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag48 0.34532749639379406 +INFO:pyaf.std:AR_MODEL_COEFF 3 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.23108921093899842 +INFO:pyaf.std:AR_MODEL_COEFF 4 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag24 0.22961299628525061 +INFO:pyaf.std:AR_MODEL_COEFF 5 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag12 0.2005495323564233 +INFO:pyaf.std:AR_MODEL_COEFF 6 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag36 0.19697576939610567 +INFO:pyaf.std:AR_MODEL_COEFF 7 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19376817942880414 +INFO:pyaf.std:AR_MODEL_COEFF 8 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag52 0.1851149086578663 +INFO:pyaf.std:AR_MODEL_COEFF 9 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.17324734082714066 +INFO:pyaf.std:AR_MODEL_COEFF 10 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.17110326166562348 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'canadagas' PERFORMANCE MAPE_FORECAST canadagas 0.0164 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'canadagas' 6.32930326461792 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['canadagas']' 4.330089807510376 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1960.0 TimeMax=1995.25 TimeDelta=0.08333333333333333 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='canadagas' Length=542 Min=0.966 Max=19.5284 Mean=9.776504981549817 StdDev=5.135749933391726 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_canadagas' Min=0.966 Max=19.5284 Mean=9.776504981549817 StdDev=5.135749933391726 @@ -98,19 +125,28 @@ INFO:pyaf.std:AUTOREG_DETAIL '_canadagas_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0289 MAPE_Forecast=0.0164 MAPE_Test=0.014 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0288 SMAPE_Forecast=0.0164 SMAPE_Test=0.0138 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3812 MASE_Forecast=0.3891 MASE_Test=0.3188 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.1857054815801831 L1_Forecast=0.2833066700200253 L1_Test=0.24779805638105432 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.2441385607929863 L2_Forecast=0.3415784405899561 L2_Test=0.3450532881065173 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.18570548158018338 L1_Forecast=0.2833066700200262 L1_Test=0.2477980563810546 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.24413856079298643 L2_Forecast=0.34157844058995696 L2_Test=0.3450532881065167 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 7.667915094339622 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _canadagas_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6896921945528506 -INFO:pyaf.std:AR_MODEL_COEFF 2 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag48 0.3462684321864066 -INFO:pyaf.std:AR_MODEL_COEFF 3 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.2354726419522541 -INFO:pyaf.std:AR_MODEL_COEFF 4 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag24 0.23004580974160904 -INFO:pyaf.std:AR_MODEL_COEFF 5 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag12 0.20378361266612316 -INFO:pyaf.std:AR_MODEL_COEFF 6 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag36 0.19200675545162038 -INFO:pyaf.std:AR_MODEL_COEFF 7 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19080807941898548 -INFO:pyaf.std:AR_MODEL_COEFF 8 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag52 0.1821367328109509 -INFO:pyaf.std:AR_MODEL_COEFF 9 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.17538590703998116 -INFO:pyaf.std:AR_MODEL_COEFF 10 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.17488759775826684 +INFO:pyaf.std:AR_MODEL_COEFF 1 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag1 0.68969219455285 +INFO:pyaf.std:AR_MODEL_COEFF 2 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag48 0.3462684321864069 +INFO:pyaf.std:AR_MODEL_COEFF 3 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.23547264195225265 +INFO:pyaf.std:AR_MODEL_COEFF 4 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag24 0.23004580974160888 +INFO:pyaf.std:AR_MODEL_COEFF 5 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag12 0.203783612666122 +INFO:pyaf.std:AR_MODEL_COEFF 6 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag36 0.19200675545162063 +INFO:pyaf.std:AR_MODEL_COEFF 7 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.1908080794189847 +INFO:pyaf.std:AR_MODEL_COEFF 8 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag52 0.18213673281095014 +INFO:pyaf.std:AR_MODEL_COEFF 9 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.17538590703998178 +INFO:pyaf.std:AR_MODEL_COEFF 10 _canadagas_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.17488759775826634 INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST canadagas 0.0164 diff --git a/tests/references/expsmooth_expsmooth_dataset_carparts.log b/tests/references/expsmooth_expsmooth_dataset_carparts.log index 74743d525..6f9a58d70 100644 --- a/tests/references/expsmooth_expsmooth_dataset_carparts.log +++ b/tests/references/expsmooth_expsmooth_dataset_carparts.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'carparts' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'carparts' 4.156693458557129 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['carparts']' 2.275771379470825 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998.0 TimeMax=2001.16666666667 TimeDelta=0.0833333333334191 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='carparts' Length=51 Min=0 Max=6 Mean=1.7450980392156863 StdDev=1.6901460344713686 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_carparts' Min=0 Max=89 Mean=54.6078431372549 StdDev=28.832904971116477 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=2.0382 MASE_Forecast=0.7875 MASE_Test=10000000 INFO:pyaf.std:MODEL_L1 L1_Fit=3.21827744904668 L1_Forecast=0.7 L1_Test=1.0 INFO:pyaf.std:MODEL_L2 L2_Fit=7.778037507132792 L2_Forecast=1.0488088481701516 L2_Test=1.0 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 45.51282051282051 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_carparts_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'carparts' PERFORMANCE MAPE_FORECAST carparts 0.5 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'carparts' 3.4791932106018066 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['carparts']' 3.0203378200531006 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998.0 TimeMax=2001.0 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='carparts' Length=51 Min=0 Max=6 Mean=1.7450980392156863 StdDev=1.6901460344713686 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_carparts' Min=0 Max=89 Mean=54.6078431372549 StdDev=28.832904971116477 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=2.0962 MASE_Forecast=0.8 MASE_Test=1.0 INFO:pyaf.std:MODEL_L1 L1_Fit=3.260774287801315 L1_Forecast=0.8 L1_Test=1.0 INFO:pyaf.std:MODEL_L2 L2_Fit=7.683264594810572 L2_Forecast=1.2649110640673518 L2_Test=1.224744871391589 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 43.648648648648646 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_carparts_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'carparts' PERFORMANCE MAPE_FORECAST carparts 0.5 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'carparts' 4.502494812011719 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['carparts']' 3.0599005222320557 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998.0 TimeMax=2000.75 TimeDelta=0.08333333333333333 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='carparts' Length=51 Min=0 Max=6 Mean=1.7450980392156863 StdDev=1.6901460344713686 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_carparts' Min=0 Max=89 Mean=54.6078431372549 StdDev=28.832904971116477 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=2.1662 MASE_Forecast=0.8 MASE_Test=1.0208 INFO:pyaf.std:MODEL_L1 L1_Fit=3.347750865051903 L1_Forecast=1.0 L1_Test=0.875 INFO:pyaf.std:MODEL_L2 L2_Fit=7.5489784147829795 L2_Forecast=1.4529663145135578 L2_Test=1.1726039399558574 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 40.8235294117647 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_carparts_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'carparts' PERFORMANCE MAPE_FORECAST carparts 0.5556 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'carparts' 3.567410469055176 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['carparts']' 2.5339508056640625 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998.0 TimeMax=2002.16666666667 TimeDelta=0.08333333333339851 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='carparts' Length=51 Min=0 Max=6 Mean=1.7450980392156863 StdDev=1.6901460344713686 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='RelDiff_carparts' Min=-0.99999998 Max=83333332.33333333 Mean=5228757.99313726 StdDev=14192189.729788994 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.2646 MASE_Forecast=1.2646 MASE_Test=1.2646 INFO:pyaf.std:MODEL_L1 L1_Fit=1.7450980392156863 L1_Forecast=1.7450980392156863 L1_Test=1.7450980392156863 INFO:pyaf.std:MODEL_L2 L2_Fit=2.4293951478328357 L2_Forecast=2.4293951478328357 L2_Test=2.4293951478328357 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:REALTIVE_DIFFERENCING_TRANSFORMATION RelativeDifference 0.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5228757.99313726 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES RelDiff_carparts_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST carparts 0.7059 diff --git a/tests/references/expsmooth_expsmooth_dataset_dji.log b/tests/references/expsmooth_expsmooth_dataset_dji.log index e86cf4f57..798a34127 100644 --- a/tests/references/expsmooth_expsmooth_dataset_dji.log +++ b/tests/references/expsmooth_expsmooth_dataset_dji.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'dji' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'dji' 5.0581889152526855 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['dji']' 4.0487401485443115 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1990.0 TimeMax=2003.58333333333 TimeDelta=0.08333333333331334 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='dji' Length=207 Min=2442.33 Max=12621.7 Mean=7394.091352657005 StdDev=3206.5512711844176 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_dji' Min=2442.33 Max=12621.7 Mean=7394.091352657005 StdDev=3206.5512711844176 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9939 MASE_Forecast=0.992 MASE_Test=2.5577 INFO:pyaf.std:MODEL_L1 L1_Fit=241.33902439024394 L1_Forecast=207.43707317073176 L1_Test=219.44999999999982 INFO:pyaf.std:MODEL_L2 L2_Fit=347.29614893435564 L2_Forecast=251.44449372454014 L2_Test=256.94478978955783 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 2590.54 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _dji_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'dji' PERFORMANCE MAPE_FORECAST dji 0.0194 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'dji' 5.319889307022095 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['dji']' 2.8439583778381348 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1990.0 TimeMax=2003.41666666667 TimeDelta=0.08333333333335358 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='dji' Length=207 Min=2442.33 Max=12621.7 Mean=7394.091352657005 StdDev=3206.5512711844176 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_dji' Min=2442.33 Max=12621.7 Mean=7394.091352657005 StdDev=3206.5512711844176 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9938 MASE_Forecast=1.0048 MASE_Test=1.0529 INFO:pyaf.std:MODEL_L1 L1_Fit=241.66185185185185 L1_Forecast=208.1829268292683 L1_Test=209.67500000000018 INFO:pyaf.std:MODEL_L2 L2_Fit=348.5949124222364 L2_Forecast=251.99998550135464 L2_Test=232.0502046971736 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 2590.54 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _dji_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'dji' PERFORMANCE MAPE_FORECAST dji 0.0197 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'dji' 5.68262505531311 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['dji']' 4.5795629024505615 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1990.0 TimeMax=2003.16666666667 TimeDelta=0.08333333333335396 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='dji' Length=207 Min=2442.33 Max=12621.7 Mean=7394.091352657005 StdDev=3206.5512711844176 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_dji' Min=2442.33 Max=12621.7 Mean=7394.091352657005 StdDev=3206.5512711844176 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9937 MASE_Forecast=1.0344 MASE_Test=0.9769 INFO:pyaf.std:MODEL_L1 L1_Fit=239.97427672955973 L1_Forecast=212.31525000000002 L1_Test=234.36249999999995 INFO:pyaf.std:MODEL_L2 L2_Fit=348.3345684221732 L2_Forecast=259.27222485738815 L2_Test=255.82558755918075 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 2590.54 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _dji_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'dji' PERFORMANCE MAPE_FORECAST dji 0.0209 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'dji' 4.504197359085083 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['dji']' 4.132838010787964 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1990.0 TimeMax=2002.91666666667 TimeDelta=0.08333333333335435 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='dji' Length=207 Min=2442.33 Max=12621.7 Mean=7394.091352657005 StdDev=3206.5512711844176 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_dji' Min=2442.33 Max=12621.7 Mean=7394.091352657005 StdDev=3206.5512711844176 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9936 MASE_Forecast=1.0084 MASE_Test=1.0278 INFO:pyaf.std:MODEL_L1 L1_Fit=241.05326923076922 L1_Forecast=218.82076923076923 L1_Test=198.75833333333335 INFO:pyaf.std:MODEL_L2 L2_Fit=350.57686865036084 L2_Forecast=263.1586160620479 L2_Test=229.33672296429134 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 2590.54 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _dji_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST dji 0.022 diff --git a/tests/references/expsmooth_expsmooth_dataset_djiclose.log b/tests/references/expsmooth_expsmooth_dataset_djiclose.log index aef5ff481..29cd4b4b7 100644 --- a/tests/references/expsmooth_expsmooth_dataset_djiclose.log +++ b/tests/references/expsmooth_expsmooth_dataset_djiclose.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'djiclose' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'djiclose' 6.409085035324097 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['djiclose']' 4.0611467361450195 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1928.83333333333 TimeMax=1991.91666666667 TimeDelta=0.08333333333334195 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='djiclose' Length=950 Min=-30.7 Max=35.76 Mean=0.5629052631578947 StdDev=5.350795964544231 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_djiclose' Min=-145.87 Max=539.5700000000003 Mean=209.25226315789473 StdDev=170.91349601367054 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7423 MASE_Forecast=0.6987 MASE_Test=0.7492 INFO:pyaf.std:MODEL_L1 L1_Fit=4.071636841152595 L1_Forecast=3.102894736842105 L1_Test=2.405 INFO:pyaf.std:MODEL_L2 L2_Fit=7.281406036606122 L2_Forecast=4.0187344826580995 L2_Test=2.891375105378062 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 143.00072559366748 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_djiclose_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'djiclose' PERFORMANCE MAPE_FORECAST djiclose 1.0 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'djiclose' 5.954767227172852 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['djiclose']' 3.5229086875915527 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1928.83333333333 TimeMax=1991.75 TimeDelta=0.08333333333333764 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='djiclose' Length=950 Min=-30.7 Max=35.76 Mean=0.5629052631578947 StdDev=5.350795964544231 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_djiclose' Min=-145.87 Max=539.5700000000003 Mean=209.25226315789473 StdDev=170.91349601367054 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7426 MASE_Forecast=0.6981 MASE_Test=0.606 INFO:pyaf.std:MODEL_L1 L1_Fit=4.061569349962207 L1_Forecast=3.160105263157895 L1_Test=2.2725 INFO:pyaf.std:MODEL_L2 L2_Fit=7.266074950025031 L2_Forecast=4.0873250807575205 L2_Test=2.8733038474898542 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 142.39642857142854 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_djiclose_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'djiclose' PERFORMANCE MAPE_FORECAST djiclose 1.0 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'djiclose' 6.411283016204834 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['djiclose']' 5.205140113830566 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1928.83333333333 TimeMax=1991.5 TimeDelta=0.08333333333333767 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='djiclose' Length=950 Min=-30.7 Max=35.76 Mean=0.5629052631578947 StdDev=5.350795964544231 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_djiclose' Min=-145.87 Max=539.5700000000003 Mean=209.25226315789473 StdDev=170.91349601367054 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7429 MASE_Forecast=0.6908 MASE_Test=0.6745 INFO:pyaf.std:MODEL_L1 L1_Fit=4.072249170648085 L1_Forecast=3.1489417989417996 L1_Test=2.19875 INFO:pyaf.std:MODEL_L2 L2_Fit=7.259213985660512 L2_Forecast=4.084885872652086 L2_Test=2.6844203284880703 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 141.48362549800794 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_djiclose_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'djiclose' PERFORMANCE MAPE_FORECAST djiclose 1.0 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'djiclose' 5.008768320083618 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['djiclose']' 4.096529483795166 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1928.83333333333 TimeMax=1991.25 TimeDelta=0.08333333333333769 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='djiclose' Length=950 Min=-30.7 Max=35.76 Mean=0.5629052631578947 StdDev=5.350795964544231 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_djiclose' Min=-145.87 Max=539.5700000000003 Mean=209.25226315789473 StdDev=170.91349601367054 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7437 MASE_Forecast=0.6879 MASE_Test=0.699 INFO:pyaf.std:MODEL_L1 L1_Fit=4.070135288888889 L1_Forecast=3.178351063829787 L1_Test=2.3416666666666672 INFO:pyaf.std:MODEL_L2 L2_Fit=7.247707553042113 L2_Forecast=4.104128759230201 L2_Test=2.89452644370946 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 140.57146666666662 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_djiclose_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST djiclose 1.0 diff --git a/tests/references/expsmooth_expsmooth_dataset_enplanements.log b/tests/references/expsmooth_expsmooth_dataset_enplanements.log index 7753c2038..f88b4eb07 100644 --- a/tests/references/expsmooth_expsmooth_dataset_enplanements.log +++ b/tests/references/expsmooth_expsmooth_dataset_enplanements.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'enplanements' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'enplanements' 6.682732343673706 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['enplanements']' 4.798672199249268 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1979.0 TimeMax=1997.58333333333 TimeDelta=0.08333333333331872 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='enplanements' Length=282 Min=20.14 Max=56.14 Mean=35.66656028368794 StdDev=9.309453702515183 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_enplanements' Min=20.14 Max=56.14 Mean=35.66656028368794 StdDev=9.309453702515183 @@ -11,24 +11,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_enplanements_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0243 MAPE_Forecast=0.0261 MAPE_Test=0.0291 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0243 SMAPE_Forecast=0.0239 SMAPE_Test=0.0294 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3833 MASE_Forecast=0.3362 MASE_Test=0.3739 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7411003888965555 L1_Forecast=1.0765436434040423 L1_Test=1.3871927455240431 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.001024756646994 L2_Forecast=2.5380390793418606 L2_Test=1.4667096002410007 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7411003888965562 L1_Forecast=1.0765436434040399 L1_Test=1.3871927455240645 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.0010247566469943 L2_Forecast=2.5380390793418592 L2_Test=1.4667096002410278 INFO:pyaf.std:MODEL_COMPLEXITY 56 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 32.58232142857143 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _enplanements_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7005163163940005 -INFO:pyaf.std:AR_MODEL_COEFF 2 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag12 0.40833657294710146 -INFO:pyaf.std:AR_MODEL_COEFF 3 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.4010208761390682 -INFO:pyaf.std:AR_MODEL_COEFF 4 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag60 0.27197293427780467 -INFO:pyaf.std:AR_MODEL_COEFF 5 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag27 0.20426355107975144 -INFO:pyaf.std:AR_MODEL_COEFF 6 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.19616986482867052 -INFO:pyaf.std:AR_MODEL_COEFF 7 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.19051429257150151 -INFO:pyaf.std:AR_MODEL_COEFF 8 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.17327259632661215 -INFO:pyaf.std:AR_MODEL_COEFF 9 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag24 0.15313552769273212 -INFO:pyaf.std:AR_MODEL_COEFF 10 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag8 0.13515596270671984 +INFO:pyaf.std:AR_MODEL_COEFF 1 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7005163163940018 +INFO:pyaf.std:AR_MODEL_COEFF 2 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag12 0.4083365729471011 +INFO:pyaf.std:AR_MODEL_COEFF 3 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.4010208761390695 +INFO:pyaf.std:AR_MODEL_COEFF 4 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag60 0.271972934277804 +INFO:pyaf.std:AR_MODEL_COEFF 5 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag27 0.20426355107975291 +INFO:pyaf.std:AR_MODEL_COEFF 6 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.19616986482866902 +INFO:pyaf.std:AR_MODEL_COEFF 7 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.190514292571501 +INFO:pyaf.std:AR_MODEL_COEFF 8 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.17327259632661338 +INFO:pyaf.std:AR_MODEL_COEFF 9 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag24 0.15313552769273253 +INFO:pyaf.std:AR_MODEL_COEFF 10 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag8 0.1351559627067205 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'enplanements' PERFORMANCE MAPE_FORECAST enplanements 0.0261 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'enplanements' 5.786861181259155 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['enplanements']' 3.120157480239868 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1979.0 TimeMax=1997.41666666667 TimeDelta=0.08333333333334808 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='enplanements' Length=282 Min=20.14 Max=56.14 Mean=35.66656028368794 StdDev=9.309453702515183 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_enplanements' Min=20.14 Max=56.14 Mean=35.66656028368794 StdDev=9.309453702515183 @@ -40,24 +49,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_enplanements_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0245 MAPE_Forecast=0.0253 MAPE_Test=0.0414 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0245 SMAPE_Forecast=0.0231 SMAPE_Test=0.041 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3824 MASE_Forecast=0.3245 MASE_Test=1.099 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7448183232282465 L1_Forecast=1.0382638930717902 L1_Test=1.970882850941532 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.004693536781072 L2_Forecast=2.5457764864653756 L2_Test=2.1099398222240984 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7448183232282457 L1_Forecast=1.0382638930717898 L1_Test=1.970882850941532 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.004693536781072 L2_Forecast=2.5457764864653734 L2_Test=2.1099398222241024 INFO:pyaf.std:MODEL_COMPLEXITY 55 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 32.43094594594594 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _enplanements_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7044255911999827 -INFO:pyaf.std:AR_MODEL_COEFF 2 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.41575914091877825 -INFO:pyaf.std:AR_MODEL_COEFF 3 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag12 0.4111538946892963 -INFO:pyaf.std:AR_MODEL_COEFF 4 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag60 0.2974304332106858 -INFO:pyaf.std:AR_MODEL_COEFF 5 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag27 0.2070852203261648 -INFO:pyaf.std:AR_MODEL_COEFF 6 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.20342666785710672 -INFO:pyaf.std:AR_MODEL_COEFF 7 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.18758253784293272 -INFO:pyaf.std:AR_MODEL_COEFF 8 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.18621547475590366 -INFO:pyaf.std:AR_MODEL_COEFF 9 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag8 0.1627028792220638 -INFO:pyaf.std:AR_MODEL_COEFF 10 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag24 0.1624960191183198 +INFO:pyaf.std:AR_MODEL_COEFF 1 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7044255911999824 +INFO:pyaf.std:AR_MODEL_COEFF 2 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.41575914091877963 +INFO:pyaf.std:AR_MODEL_COEFF 3 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag12 0.41115389468929703 +INFO:pyaf.std:AR_MODEL_COEFF 4 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag60 0.29743043321068535 +INFO:pyaf.std:AR_MODEL_COEFF 5 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag27 0.20708522032616336 +INFO:pyaf.std:AR_MODEL_COEFF 6 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.20342666785710692 +INFO:pyaf.std:AR_MODEL_COEFF 7 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.18758253784293222 +INFO:pyaf.std:AR_MODEL_COEFF 8 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.1862154747559041 +INFO:pyaf.std:AR_MODEL_COEFF 9 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag8 0.1627028792220643 +INFO:pyaf.std:AR_MODEL_COEFF 10 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag24 0.16249601911832023 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'enplanements' PERFORMANCE MAPE_FORECAST enplanements 0.0253 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'enplanements' 6.465994358062744 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['enplanements']' 4.89136815071106 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1979.0 TimeMax=1997.16666666667 TimeDelta=0.08333333333334829 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='enplanements' Length=282 Min=20.14 Max=56.14 Mean=35.66656028368794 StdDev=9.309453702515183 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_enplanements' Min=20.14 Max=56.14 Mean=35.66656028368794 StdDev=9.309453702515183 @@ -69,24 +87,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_enplanements_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0248 MAPE_Forecast=0.0238 MAPE_Test=0.0374 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0248 SMAPE_Forecast=0.0216 SMAPE_Test=0.0372 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3856 MASE_Forecast=0.3104 MASE_Test=0.6269 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7508629861153063 L1_Forecast=0.9819081770206307 L1_Test=1.6721320607683303 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.009809612531233 L2_Forecast=2.533826527418021 L2_Test=1.8511046905513289 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.750862986115306 L1_Forecast=0.9819081770206295 L1_Test=1.6721320607683268 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.0098096125312328 L2_Forecast=2.5338265274180185 L2_Test=1.8511046905513286 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 32.235068493150685 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _enplanements_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7081480810281504 -INFO:pyaf.std:AR_MODEL_COEFF 2 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.41286472597616597 -INFO:pyaf.std:AR_MODEL_COEFF 3 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag12 0.40345665024023947 -INFO:pyaf.std:AR_MODEL_COEFF 4 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag60 0.2809664768234144 -INFO:pyaf.std:AR_MODEL_COEFF 5 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag27 0.21242894389833258 -INFO:pyaf.std:AR_MODEL_COEFF 6 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.21024459913782914 -INFO:pyaf.std:AR_MODEL_COEFF 7 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.18440966933013403 -INFO:pyaf.std:AR_MODEL_COEFF 8 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.17136454311287824 -INFO:pyaf.std:AR_MODEL_COEFF 9 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag24 0.16484557236098835 -INFO:pyaf.std:AR_MODEL_COEFF 10 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag8 0.1618853367906461 +INFO:pyaf.std:AR_MODEL_COEFF 1 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7081480810281511 +INFO:pyaf.std:AR_MODEL_COEFF 2 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.4128647259761656 +INFO:pyaf.std:AR_MODEL_COEFF 3 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag12 0.4034566502402398 +INFO:pyaf.std:AR_MODEL_COEFF 4 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag60 0.28096647682341525 +INFO:pyaf.std:AR_MODEL_COEFF 5 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag27 0.21242894389833228 +INFO:pyaf.std:AR_MODEL_COEFF 6 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.21024459913783106 +INFO:pyaf.std:AR_MODEL_COEFF 7 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.18440966933013325 +INFO:pyaf.std:AR_MODEL_COEFF 8 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.17136454311287957 +INFO:pyaf.std:AR_MODEL_COEFF 9 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag24 0.16484557236098804 +INFO:pyaf.std:AR_MODEL_COEFF 10 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag8 0.16188533679064598 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'enplanements' PERFORMANCE MAPE_FORECAST enplanements 0.0238 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'enplanements' 5.273252248764038 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['enplanements']' 4.51191782951355 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1979.0 TimeMax=1996.91666666667 TimeDelta=0.0833333333333485 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='enplanements' Length=282 Min=20.14 Max=56.14 Mean=35.66656028368794 StdDev=9.309453702515183 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_enplanements' Min=20.14 Max=56.14 Mean=35.66656028368794 StdDev=9.309453702515183 @@ -98,19 +125,28 @@ INFO:pyaf.std:AUTOREG_DETAIL '_enplanements_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0249 MAPE_Forecast=0.0144 MAPE_Test=0.0753 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0249 SMAPE_Forecast=0.0144 SMAPE_Test=0.0649 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3914 MASE_Forecast=0.2504 MASE_Test=0.5616 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.750857025965762 L1_Forecast=0.6926572967735125 L1_Test=2.763448779674223 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.0097413997409121 L2_Forecast=0.8986338121206527 L2_Test=5.354343975680267 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7508570259657616 L1_Forecast=0.6926572967735122 L1_Test=2.763448779674215 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.0097413997409121 L2_Forecast=0.898633812120651 L2_Test=5.354343975680265 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 32.07643518518518 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _enplanements_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_COEFF 1 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7107693431092237 -INFO:pyaf.std:AR_MODEL_COEFF 2 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.4354511176517557 -INFO:pyaf.std:AR_MODEL_COEFF 3 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag12 0.39594459961954265 +INFO:pyaf.std:AR_MODEL_COEFF 2 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.43545111765175526 +INFO:pyaf.std:AR_MODEL_COEFF 3 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag12 0.39594459961954204 INFO:pyaf.std:AR_MODEL_COEFF 4 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag60 0.2665244059542366 -INFO:pyaf.std:AR_MODEL_COEFF 5 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag27 0.2266474896672514 -INFO:pyaf.std:AR_MODEL_COEFF 6 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.21497511713274126 -INFO:pyaf.std:AR_MODEL_COEFF 7 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.19626576042708968 -INFO:pyaf.std:AR_MODEL_COEFF 8 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag8 0.17743741794570844 -INFO:pyaf.std:AR_MODEL_COEFF 9 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag24 0.16920577307077117 -INFO:pyaf.std:AR_MODEL_COEFF 10 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag14 0.16791339656515436 +INFO:pyaf.std:AR_MODEL_COEFF 5 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag27 0.22664748966725004 +INFO:pyaf.std:AR_MODEL_COEFF 6 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.2149751171327407 +INFO:pyaf.std:AR_MODEL_COEFF 7 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.1962657604270886 +INFO:pyaf.std:AR_MODEL_COEFF 8 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag8 0.17743741794570814 +INFO:pyaf.std:AR_MODEL_COEFF 9 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag24 0.16920577307077173 +INFO:pyaf.std:AR_MODEL_COEFF 10 _enplanements_ConstantTrend_residue_zeroCycle_residue_Lag14 0.16791339656515347 INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST enplanements 0.0144 diff --git a/tests/references/expsmooth_expsmooth_dataset_fmsales.log b/tests/references/expsmooth_expsmooth_dataset_fmsales.log index 9be5bdaf8..d920fe7d0 100644 --- a/tests/references/expsmooth_expsmooth_dataset_fmsales.log +++ b/tests/references/expsmooth_expsmooth_dataset_fmsales.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'fmsales' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'fmsales' 3.920713424682617 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['fmsales']' 3.745128631591797 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1 TimeMax=48 TimeDelta=1 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='fmsales' Length=62 Min=22.2413797644 Max=51.91408072312 Mean=32.47486108225597 StdDev=5.445690335762301 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_fmsales' Min=22.2413797644 Max=51.91408072312 Mean=32.47486108225597 StdDev=5.445690335762301 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9792 MASE_Forecast=0.9579 MASE_Test=0.5217 INFO:pyaf.std:MODEL_L1 L1_Fit=2.105835485309583 L1_Forecast=3.700795328261666 L1_Test=2.9865208982299993 INFO:pyaf.std:MODEL_L2 L2_Fit=2.838167186185903 L2_Forecast=5.902575689216325 L2_Test=4.05138805207826 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 23.056130494239998 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _fmsales_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'fmsales' PERFORMANCE MAPE_FORECAST fmsales 0.1018 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'fmsales' 4.61824893951416 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['fmsales']' 2.9655921459198 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1 TimeMax=46 TimeDelta=1 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='fmsales' Length=62 Min=22.2413797644 Max=51.91408072312 Mean=32.47486108225597 StdDev=5.445690335762301 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_fmsales' Min=22.2413797644 Max=51.91408072312 Mean=32.47486108225597 StdDev=5.445690335762301 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9783 MASE_Forecast=0.9537 MASE_Test=0.8578 INFO:pyaf.std:MODEL_L1 L1_Fit=2.1589742100578255 L1_Forecast=3.6766912103983334 L1_Test=2.007395210755 INFO:pyaf.std:MODEL_L2 L2_Fit=2.8881804703191998 L2_Forecast=5.9083673288353475 L2_Test=2.9556268681235416 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 23.056130494239998 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _fmsales_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'fmsales' PERFORMANCE MAPE_FORECAST fmsales 0.0998 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'fmsales' 4.473785638809204 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['fmsales']' 2.8983497619628906 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1 TimeMax=43 TimeDelta=1 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='fmsales' Length=62 Min=22.2413797644 Max=51.91408072312 Mean=32.47486108225597 StdDev=5.445690335762301 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_fmsales' Min=22.2413797644 Max=51.91408072312 Mean=32.47486108225597 StdDev=5.445690335762301 @@ -49,14 +67,23 @@ INFO:pyaf.std:AUTOREG_DETAIL '_fmsales_LinearTrend_residue_zeroCycle_residue_NoA INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1029 MAPE_Forecast=0.1067 MAPE_Test=0.2855 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1006 SMAPE_Forecast=0.1084 SMAPE_Test=0.2474 INFO:pyaf.std:MODEL_MASE MASE_Fit=1.4293 MASE_Forecast=0.8974 MASE_Test=4.2039 -INFO:pyaf.std:MODEL_L1 L1_Fit=3.025136655287573 L1_Forecast=4.171475509471438 L1_Test=8.459931636122906 -INFO:pyaf.std:MODEL_L2 L2_Fit=3.839925959543561 L2_Forecast=5.668188433343207 L2_Test=8.71025414407644 +INFO:pyaf.std:MODEL_L1 L1_Fit=3.0251366552875725 L1_Forecast=4.17147550947144 L1_Test=8.45993163612291 +INFO:pyaf.std:MODEL_L2 L2_Fit=3.839925959543561 L2_Forecast=5.668188433343207 L2_Test=8.710254144076444 INFO:pyaf.std:MODEL_COMPLEXITY 16 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (27.33123752832896, array([8.29437017])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _fmsales_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'fmsales' PERFORMANCE MAPE_FORECAST fmsales 0.1067 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'fmsales' 5.219470262527466 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['fmsales']' 3.802814483642578 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1 TimeMax=62 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='fmsales' Length=62 Min=22.2413797644 Max=51.91408072312 Mean=32.47486108225597 StdDev=5.445690335762301 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_fmsales' Min=22.2413797644 Max=51.91408072312 Mean=32.47486108225597 StdDev=5.445690335762301 @@ -68,19 +95,28 @@ INFO:pyaf.std:AUTOREG_DETAIL '_fmsales_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0715 MAPE_Forecast=0.0715 MAPE_Test=0.0715 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.071 SMAPE_Forecast=0.071 SMAPE_Test=0.071 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.937 MASE_Forecast=0.937 MASE_Test=0.937 -INFO:pyaf.std:MODEL_L1 L1_Fit=2.326446576365642 L1_Forecast=2.326446576365642 L1_Test=2.326446576365642 -INFO:pyaf.std:MODEL_L2 L2_Fit=3.060739121311148 L2_Forecast=3.060739121311148 L2_Test=3.060739121311148 +INFO:pyaf.std:MODEL_L1 L1_Fit=2.3264465763656426 L1_Forecast=2.3264465763656426 L1_Test=2.3264465763656426 +INFO:pyaf.std:MODEL_L2 L2_Fit=3.0607391213111486 L2_Forecast=3.0607391213111486 L2_Test=3.0607391213111486 INFO:pyaf.std:MODEL_COMPLEXITY 15 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 32.47486108225597 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _fmsales_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6494545122301195 -INFO:pyaf.std:AR_MODEL_COEFF 2 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag11 0.528630694675162 -INFO:pyaf.std:AR_MODEL_COEFF 3 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.40658324225472703 -INFO:pyaf.std:AR_MODEL_COEFF 4 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag2 0.24645850941782868 -INFO:pyaf.std:AR_MODEL_COEFF 5 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.2056268504826847 -INFO:pyaf.std:AR_MODEL_COEFF 6 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.1747969731384776 -INFO:pyaf.std:AR_MODEL_COEFF 7 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag15 0.15052663952090709 -INFO:pyaf.std:AR_MODEL_COEFF 8 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag4 0.14430693700039227 -INFO:pyaf.std:AR_MODEL_COEFF 9 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.1043941184708812 -INFO:pyaf.std:AR_MODEL_COEFF 10 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag6 0.05051884838281853 +INFO:pyaf.std:AR_MODEL_COEFF 1 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6494545122301192 +INFO:pyaf.std:AR_MODEL_COEFF 2 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag11 0.5286306946751625 +INFO:pyaf.std:AR_MODEL_COEFF 3 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.406583242254728 +INFO:pyaf.std:AR_MODEL_COEFF 4 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag2 0.24645850941782882 +INFO:pyaf.std:AR_MODEL_COEFF 5 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.2056268504826854 +INFO:pyaf.std:AR_MODEL_COEFF 6 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.17479697313847747 +INFO:pyaf.std:AR_MODEL_COEFF 7 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag15 0.15052663952090767 +INFO:pyaf.std:AR_MODEL_COEFF 8 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag4 0.14430693700039218 +INFO:pyaf.std:AR_MODEL_COEFF 9 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.10439411847088112 +INFO:pyaf.std:AR_MODEL_COEFF 10 _fmsales_ConstantTrend_residue_zeroCycle_residue_Lag6 0.05051884838281837 INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST fmsales 0.0715 diff --git a/tests/references/expsmooth_expsmooth_dataset_freight.log b/tests/references/expsmooth_expsmooth_dataset_freight.log index bb86ca227..4d1d0e4da 100644 --- a/tests/references/expsmooth_expsmooth_dataset_freight.log +++ b/tests/references/expsmooth_expsmooth_dataset_freight.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'freight' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'freight' 3.6855790615081787 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['freight']' 3.891495704650879 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1947 TimeMax=1982 TimeDelta=1 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='freight' Length=47 Min=298.1 Max=6243.1 Mean=2177.3468085106388 StdDev=1474.6269293940315 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_freight' Min=298.1 Max=6243.1 Mean=2177.3468085106388 StdDev=1474.6269293940315 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9722 MASE_Forecast=0.8961 MASE_Test=0.7282 INFO:pyaf.std:MODEL_L1 L1_Fit=1237.3138888888889 L1_Forecast=248.92777777777778 L1_Test=677.95 INFO:pyaf.std:MODEL_L2 L2_Fit=1658.271304911969 L2_Forecast=291.47780599711 L2_Test=723.6195150077145 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 4631.45 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _freight_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'freight' PERFORMANCE MAPE_FORECAST freight 0.2863 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'freight' 4.238959312438965 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['freight']' 2.856386184692383 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1947 TimeMax=1980 TimeDelta=1 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='freight' Length=47 Min=298.1 Max=6243.1 Mean=2177.3468085106388 StdDev=1474.6269293940315 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_freight' Min=298.1 Max=6243.1 Mean=2177.3468085106388 StdDev=1474.6269293940315 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9706 MASE_Forecast=1.3169 MASE_Test=0.8402 INFO:pyaf.std:MODEL_L1 L1_Fit=1260.3632352941177 L1_Forecast=390.5888888888889 L1_Test=442.975 INFO:pyaf.std:MODEL_L2 L2_Fit=1692.447509504053 L2_Forecast=503.7303246777982 L2_Test=532.5430804169744 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 4631.45 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _freight_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'freight' PERFORMANCE MAPE_FORECAST freight 0.5883 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'freight' 4.113903522491455 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['freight']' 3.1295032501220703 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1947 TimeMax=1977 TimeDelta=1 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='freight' Length=47 Min=298.1 Max=6243.1 Mean=2177.3468085106388 StdDev=1474.6269293940315 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_freight' Min=298.1 Max=6243.1 Mean=2177.3468085106388 StdDev=1474.6269293940315 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9677 MASE_Forecast=1.3029 MASE_Test=0.9222 INFO:pyaf.std:MODEL_L1 L1_Fit=1140.3548387096776 L1_Forecast=1242.7562500000001 L1_Test=355.8125 INFO:pyaf.std:MODEL_L2 L2_Fit=1535.4465557237315 L2_Forecast=1809.4694407995678 L2_Test=437.04419184448614 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 4631.45 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _freight_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'freight' PERFORMANCE MAPE_FORECAST freight 0.8435 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'freight' 3.4489824771881104 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['freight']' 3.1263790130615234 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1947 TimeMax=1993 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='freight' Length=47 Min=298.1 Max=6243.1 Mean=2177.3468085106388 StdDev=1474.6269293940315 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_freight' Min=298.1 Max=6243.1 Mean=2177.3468085106388 StdDev=1474.6269293940315 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9787 MASE_Forecast=0.9787 MASE_Test=0.9787 INFO:pyaf.std:MODEL_L1 L1_Fit=1024.245744680851 L1_Forecast=1024.245744680851 L1_Test=1024.245744680851 INFO:pyaf.std:MODEL_L2 L2_Fit=1464.5235820544567 L2_Forecast=1464.5235820544567 L2_Test=1464.5235820544567 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 4631.45 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _freight_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST freight 0.529 diff --git a/tests/references/expsmooth_expsmooth_dataset_frexport.log b/tests/references/expsmooth_expsmooth_dataset_frexport.log index 1bbf29ce6..a33a18499 100644 --- a/tests/references/expsmooth_expsmooth_dataset_frexport.log +++ b/tests/references/expsmooth_expsmooth_dataset_frexport.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'frexport' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'frexport' 2.793140172958374 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['frexport']' 3.6211280822753906 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=6.75 TimeDelta=0.25 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='frexport' Length=24 Min=341 Max=854 Mean=548.7083333333334 StdDev=137.446498429591 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_frexport' Min=341 Max=854 Mean=548.7083333333334 StdDev=137.446498429591 @@ -12,19 +12,28 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0459 MAPE_Forecast=0.0459 MAPE_Test=0.0459 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.045 SMAPE_Forecast=0.045 SMAPE_Test=0.045 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2777 MASE_Forecast=0.2777 MASE_Test=0.2777 INFO:pyaf.std:MODEL_L1 L1_Fit=23.12176009545192 L1_Forecast=23.12176009545192 L1_Test=23.12176009545192 -INFO:pyaf.std:MODEL_L2 L2_Fit=28.88318526770828 L2_Forecast=28.88318526770828 L2_Test=28.88318526770828 +INFO:pyaf.std:MODEL_L2 L2_Fit=28.883185267708274 L2_Forecast=28.883185267708274 L2_Test=28.883185267708274 INFO:pyaf.std:MODEL_COMPLEXITY 6 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 548.7083333333334 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _frexport_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag4 1.153876506666612 -INFO:pyaf.std:AR_MODEL_COEFF 2 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5257406340934825 -INFO:pyaf.std:AR_MODEL_COEFF 3 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.5135022261523631 -INFO:pyaf.std:AR_MODEL_COEFF 4 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1714632391891056 -INFO:pyaf.std:AR_MODEL_COEFF 5 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.03585851627319697 -INFO:pyaf.std:AR_MODEL_COEFF 6 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag2 0.03546879238986876 +INFO:pyaf.std:AR_MODEL_COEFF 1 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag4 1.1538765066666121 +INFO:pyaf.std:AR_MODEL_COEFF 2 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag1 0.525740634093483 +INFO:pyaf.std:AR_MODEL_COEFF 3 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.5135022261523638 +INFO:pyaf.std:AR_MODEL_COEFF 4 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.17146323918910528 +INFO:pyaf.std:AR_MODEL_COEFF 5 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.035858516273196195 +INFO:pyaf.std:AR_MODEL_COEFF 6 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag2 0.03546879238986786 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'frexport' PERFORMANCE MAPE_FORECAST frexport 0.0459 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'frexport' 3.854763984680176 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['frexport']' 3.2669413089752197 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=6.75 TimeDelta=0.25 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='frexport' Length=24 Min=341 Max=854 Mean=548.7083333333334 StdDev=137.446498429591 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_frexport' Min=341 Max=854 Mean=548.7083333333334 StdDev=137.446498429591 @@ -37,19 +46,28 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0459 MAPE_Forecast=0.0459 MAPE_Test=0.0459 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.045 SMAPE_Forecast=0.045 SMAPE_Test=0.045 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2777 MASE_Forecast=0.2777 MASE_Test=0.2777 INFO:pyaf.std:MODEL_L1 L1_Fit=23.12176009545192 L1_Forecast=23.12176009545192 L1_Test=23.12176009545192 -INFO:pyaf.std:MODEL_L2 L2_Fit=28.88318526770828 L2_Forecast=28.88318526770828 L2_Test=28.88318526770828 +INFO:pyaf.std:MODEL_L2 L2_Fit=28.883185267708274 L2_Forecast=28.883185267708274 L2_Test=28.883185267708274 INFO:pyaf.std:MODEL_COMPLEXITY 6 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 548.7083333333334 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _frexport_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag4 1.153876506666612 -INFO:pyaf.std:AR_MODEL_COEFF 2 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5257406340934825 -INFO:pyaf.std:AR_MODEL_COEFF 3 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.5135022261523631 -INFO:pyaf.std:AR_MODEL_COEFF 4 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1714632391891056 -INFO:pyaf.std:AR_MODEL_COEFF 5 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.03585851627319697 -INFO:pyaf.std:AR_MODEL_COEFF 6 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag2 0.03546879238986876 +INFO:pyaf.std:AR_MODEL_COEFF 1 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag4 1.1538765066666121 +INFO:pyaf.std:AR_MODEL_COEFF 2 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag1 0.525740634093483 +INFO:pyaf.std:AR_MODEL_COEFF 3 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.5135022261523638 +INFO:pyaf.std:AR_MODEL_COEFF 4 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.17146323918910528 +INFO:pyaf.std:AR_MODEL_COEFF 5 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.035858516273196195 +INFO:pyaf.std:AR_MODEL_COEFF 6 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag2 0.03546879238986786 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'frexport' PERFORMANCE MAPE_FORECAST frexport 0.0459 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'frexport' 4.521223306655884 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['frexport']' 2.984135627746582 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=6.75 TimeDelta=0.25 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='frexport' Length=24 Min=341 Max=854 Mean=548.7083333333334 StdDev=137.446498429591 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_frexport' Min=341 Max=854 Mean=548.7083333333334 StdDev=137.446498429591 @@ -62,19 +80,28 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0459 MAPE_Forecast=0.0459 MAPE_Test=0.0459 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.045 SMAPE_Forecast=0.045 SMAPE_Test=0.045 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2777 MASE_Forecast=0.2777 MASE_Test=0.2777 INFO:pyaf.std:MODEL_L1 L1_Fit=23.12176009545192 L1_Forecast=23.12176009545192 L1_Test=23.12176009545192 -INFO:pyaf.std:MODEL_L2 L2_Fit=28.88318526770828 L2_Forecast=28.88318526770828 L2_Test=28.88318526770828 +INFO:pyaf.std:MODEL_L2 L2_Fit=28.883185267708274 L2_Forecast=28.883185267708274 L2_Test=28.883185267708274 INFO:pyaf.std:MODEL_COMPLEXITY 6 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 548.7083333333334 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _frexport_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag4 1.153876506666612 -INFO:pyaf.std:AR_MODEL_COEFF 2 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5257406340934825 -INFO:pyaf.std:AR_MODEL_COEFF 3 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.5135022261523631 -INFO:pyaf.std:AR_MODEL_COEFF 4 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1714632391891056 -INFO:pyaf.std:AR_MODEL_COEFF 5 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.03585851627319697 -INFO:pyaf.std:AR_MODEL_COEFF 6 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag2 0.03546879238986876 +INFO:pyaf.std:AR_MODEL_COEFF 1 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag4 1.1538765066666121 +INFO:pyaf.std:AR_MODEL_COEFF 2 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag1 0.525740634093483 +INFO:pyaf.std:AR_MODEL_COEFF 3 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.5135022261523638 +INFO:pyaf.std:AR_MODEL_COEFF 4 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.17146323918910528 +INFO:pyaf.std:AR_MODEL_COEFF 5 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.035858516273196195 +INFO:pyaf.std:AR_MODEL_COEFF 6 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag2 0.03546879238986786 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'frexport' PERFORMANCE MAPE_FORECAST frexport 0.0459 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'frexport' 4.461570501327515 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['frexport']' 3.397899866104126 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=6.75 TimeDelta=0.25 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='frexport' Length=24 Min=341 Max=854 Mean=548.7083333333334 StdDev=137.446498429591 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_frexport' Min=341 Max=854 Mean=548.7083333333334 StdDev=137.446498429591 @@ -87,14 +114,23 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0459 MAPE_Forecast=0.0459 MAPE_Test=0.0459 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.045 SMAPE_Forecast=0.045 SMAPE_Test=0.045 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2777 MASE_Forecast=0.2777 MASE_Test=0.2777 INFO:pyaf.std:MODEL_L1 L1_Fit=23.12176009545192 L1_Forecast=23.12176009545192 L1_Test=23.12176009545192 -INFO:pyaf.std:MODEL_L2 L2_Fit=28.88318526770828 L2_Forecast=28.88318526770828 L2_Test=28.88318526770828 +INFO:pyaf.std:MODEL_L2 L2_Fit=28.883185267708274 L2_Forecast=28.883185267708274 L2_Test=28.883185267708274 INFO:pyaf.std:MODEL_COMPLEXITY 6 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 548.7083333333334 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _frexport_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag4 1.153876506666612 -INFO:pyaf.std:AR_MODEL_COEFF 2 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5257406340934825 -INFO:pyaf.std:AR_MODEL_COEFF 3 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.5135022261523631 -INFO:pyaf.std:AR_MODEL_COEFF 4 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1714632391891056 -INFO:pyaf.std:AR_MODEL_COEFF 5 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.03585851627319697 -INFO:pyaf.std:AR_MODEL_COEFF 6 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag2 0.03546879238986876 +INFO:pyaf.std:AR_MODEL_COEFF 1 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag4 1.1538765066666121 +INFO:pyaf.std:AR_MODEL_COEFF 2 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag1 0.525740634093483 +INFO:pyaf.std:AR_MODEL_COEFF 3 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.5135022261523638 +INFO:pyaf.std:AR_MODEL_COEFF 4 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.17146323918910528 +INFO:pyaf.std:AR_MODEL_COEFF 5 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.035858516273196195 +INFO:pyaf.std:AR_MODEL_COEFF 6 _frexport_ConstantTrend_residue_zeroCycle_residue_Lag2 0.03546879238986786 INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST frexport 0.0459 diff --git a/tests/references/expsmooth_expsmooth_dataset_gasprice.log b/tests/references/expsmooth_expsmooth_dataset_gasprice.log index 0e9ec3c0d..76c067eb0 100644 --- a/tests/references/expsmooth_expsmooth_dataset_gasprice.log +++ b/tests/references/expsmooth_expsmooth_dataset_gasprice.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'gasprice' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'gasprice' 4.414876222610474 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['gasprice']' 3.6297168731689453 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1991.0 TimeMax=2003.5 TimeDelta=0.08333333333333333 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='gasprice' Length=191 Min=11.35 Max=74.41 Mean=27.948376963350782 StdDev=14.349021857109438 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_gasprice' Min=11.35 Max=74.41 Mean=27.948376963350782 StdDev=14.349021857109438 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9934 MASE_Forecast=0.9806 MASE_Test=13.4211 INFO:pyaf.std:MODEL_L1 L1_Fit=1.2528476821192054 L1_Forecast=3.066578947368421 L1_Test=2.549999999999997 INFO:pyaf.std:MODEL_L2 L2_Fit=1.6451554406319968 L2_Forecast=3.8084652938863504 L2_Test=3.4744927687361766 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 25.23 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _gasprice_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'gasprice' PERFORMANCE MAPE_FORECAST gasprice 0.0603 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'gasprice' 4.1199586391448975 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['gasprice']' 4.187219619750977 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1991.0 TimeMax=2003.33333333333 TimeDelta=0.08333333333331132 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='gasprice' Length=191 Min=11.35 Max=74.41 Mean=27.948376963350782 StdDev=14.349021857109438 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_gasprice' Min=11.35 Max=74.41 Mean=27.948376963350782 StdDev=14.349021857109438 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9933 MASE_Forecast=0.9971 MASE_Test=0.8217 INFO:pyaf.std:MODEL_L1 L1_Fit=1.2519463087248321 L1_Forecast=2.856842105263158 L1_Test=3.9274999999999984 INFO:pyaf.std:MODEL_L2 L2_Fit=1.642915235122816 L2_Forecast=3.5184610870453907 L2_Test=5.277279128490365 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 25.23 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _gasprice_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'gasprice' PERFORMANCE MAPE_FORECAST gasprice 0.0582 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'gasprice' 5.167984247207642 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['gasprice']' 3.6074485778808594 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1991.0 TimeMax=2003.08333333333 TimeDelta=0.08333333333331086 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='gasprice' Length=191 Min=11.35 Max=74.41 Mean=27.948376963350782 StdDev=14.349021857109438 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_gasprice' Min=11.35 Max=74.41 Mean=27.948376963350782 StdDev=14.349021857109438 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9932 MASE_Forecast=0.9951 MASE_Test=1.1606 INFO:pyaf.std:MODEL_L1 L1_Fit=1.2247945205479451 L1_Forecast=2.825945945945946 L1_Test=3.428749999999999 INFO:pyaf.std:MODEL_L2 L2_Fit=1.5882160234005642 L2_Forecast=3.467295074713881 L2_Test=4.622024718670381 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 25.23 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _gasprice_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'gasprice' PERFORMANCE MAPE_FORECAST gasprice 0.0624 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'gasprice' 5.126728057861328 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['gasprice']' 3.8506882190704346 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1991.0 TimeMax=2002.83333333333 TimeDelta=0.08333333333331039 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='gasprice' Length=191 Min=11.35 Max=74.41 Mean=27.948376963350782 StdDev=14.349021857109438 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_gasprice' Min=11.35 Max=74.41 Mean=27.948376963350782 StdDev=14.349021857109438 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.993 MASE_Forecast=1.0028 MASE_Test=0.9427 INFO:pyaf.std:MODEL_L1 L1_Fit=1.184195804195804 L1_Forecast=2.831944444444445 L1_Test=3.2933333333333312 INFO:pyaf.std:MODEL_L2 L2_Fit=1.5377037644269675 L2_Forecast=3.41885255279864 L2_Test=4.330921764859454 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 25.23 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _gasprice_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST gasprice 0.0669 diff --git a/tests/references/expsmooth_expsmooth_dataset_hospital.log b/tests/references/expsmooth_expsmooth_dataset_hospital.log index 9ca1c1b69..afe6048b9 100644 --- a/tests/references/expsmooth_expsmooth_dataset_hospital.log +++ b/tests/references/expsmooth_expsmooth_dataset_hospital.log @@ -1,34 +1,33 @@ INFO:pyaf.std:START_TRAINING 'hospital' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'hospital' 3.4576518535614014 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['hospital']' 2.7479467391967773 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000.0 TimeMax=2005.33333333333 TimeDelta=0.08333333333328241 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='hospital' Length=84 Min=35 Max=108 Mean=60.51190476190476 StdDev=18.35139416503426 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_hospital' Min=35 Max=108 Mean=60.51190476190476 StdDev=18.35139416503426 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_hospital_LinearTrend_residue_bestCycle_byL2_residue_AR(21)' [LinearTrend + Cycle + AR] -INFO:pyaf.std:TREND_DETAIL '_hospital_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_hospital_LinearTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_hospital_LinearTrend_residue_bestCycle_byL2_residue_AR(21)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1581 MAPE_Forecast=0.1619 MAPE_Test=0.0241 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1557 SMAPE_Forecast=0.1422 SMAPE_Test=0.0244 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7849 MASE_Forecast=0.9305 MASE_Test=0.1116 -INFO:pyaf.std:MODEL_L1 L1_Fit=10.190944409722844 L1_Forecast=6.455289915504948 L1_Test=1.2281405850750033 -INFO:pyaf.std:MODEL_L2 L2_Fit=12.687870434298622 L2_Forecast=8.980406815195062 L2_Test=1.2281650888248756 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:BEST_DECOMPOSITION '_hospital_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_hospital_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_hospital_Lag1Trend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_hospital_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1962 MAPE_Forecast=0.1781 MAPE_Test=0.181 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1933 SMAPE_Forecast=0.1733 SMAPE_Test=0.1722 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9846 MASE_Forecast=1.1192 MASE_Test=0.8182 +INFO:pyaf.std:MODEL_L1 L1_Fit=12.784615384615385 L1_Forecast=7.764705882352941 L1_Test=9.0 +INFO:pyaf.std:MODEL_L2 L2_Fit=17.013569245209496 L2_Forecast=9.83391490470307 L2_Test=9.219544457292887 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 45 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _hospital_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag1 0.4375271328639314 -INFO:pyaf.std:AR_MODEL_COEFF 2 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag11 0.31718832832035393 -INFO:pyaf.std:AR_MODEL_COEFF 3 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag10 -0.29778318332172804 -INFO:pyaf.std:AR_MODEL_COEFF 4 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag4 0.20557121756817479 -INFO:pyaf.std:AR_MODEL_COEFF 5 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag6 -0.17234806549653936 -INFO:pyaf.std:AR_MODEL_COEFF 6 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag16 -0.16231025847744995 -INFO:pyaf.std:AR_MODEL_COEFF 7 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag14 0.1414799780818248 -INFO:pyaf.std:AR_MODEL_COEFF 8 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag5 0.11078984342941095 -INFO:pyaf.std:AR_MODEL_COEFF 9 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag15 -0.06861252585134717 -INFO:pyaf.std:AR_MODEL_COEFF 10 _hospital_LinearTrend_residue_bestCycle_byL2_residue_Lag9 0.06826322993323755 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'hospital' -PERFORMANCE MAPE_FORECAST hospital 0.1619 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'hospital' 4.380534648895264 +PERFORMANCE MAPE_FORECAST hospital 0.1781 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['hospital']' 4.250967264175415 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000.0 TimeMax=2005.25 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='hospital' Length=84 Min=35 Max=108 Mean=60.51190476190476 StdDev=18.35139416503426 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_hospital' Min=35 Max=108 Mean=60.51190476190476 StdDev=18.35139416503426 @@ -43,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9844 MASE_Forecast=1.0547 MASE_Test=1.0455 INFO:pyaf.std:MODEL_L1 L1_Fit=12.78125 L1_Forecast=7.3125 L1_Test=11.5 INFO:pyaf.std:MODEL_L2 L2_Fit=17.068794626452096 L2_Forecast=9.41740410091868 L2_Test=11.874342087037917 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 45 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _hospital_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'hospital' PERFORMANCE MAPE_FORECAST hospital 0.1595 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'hospital' 4.009160757064819 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['hospital']' 3.028578758239746 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000.0 TimeMax=2004.91666666667 TimeDelta=0.08333333333338858 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='hospital' Length=84 Min=35 Max=108 Mean=60.51190476190476 StdDev=18.35139416503426 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_hospital' Min=-42.0 Max=45.0 Mean=0.011904761904761904 StdDev=15.671094142081381 @@ -62,25 +70,43 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.7458 MASE_Forecast=0.7555 MASE_Test=0.8234 INFO:pyaf.std:MODEL_L1 L1_Fit=22.251111111111115 L1_Forecast=7.504166666666666 L1_Test=8.116666666666665 INFO:pyaf.std:MODEL_L2 L2_Fit=28.525196789011847 L2_Forecast=10.254457025551812 L2_Test=8.95159824339269 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 45 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend -0.03333333333333333 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_hospital_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'hospital' PERFORMANCE MAPE_FORECAST hospital 0.1501 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'hospital' 4.991507530212402 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['hospital']' 3.573838949203491 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000.0 TimeMax=2004.66666666667 TimeDelta=0.08333333333339153 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='hospital' Length=84 Min=35 Max=108 Mean=60.51190476190476 StdDev=18.35139416503426 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_hospital' Min=-42.0 Max=45.0 Mean=0.011904761904761904 StdDev=15.671094142081381 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_hospital_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_hospital_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] INFO:pyaf.std:TREND_DETAIL 'Diff_hospital_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_hospital_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_hospital_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2997 MAPE_Forecast=0.1128 MAPE_Test=0.1695 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3763 SMAPE_Forecast=0.1247 SMAPE_Test=0.1671 -INFO:pyaf.std:MODEL_MASE MASE_Fit=1.7738 MASE_Forecast=0.5689 MASE_Test=0.9061 -INFO:pyaf.std:MODEL_L1 L1_Fit=23.028176501860692 L1_Forecast=6.176767676767668 L1_Test=7.331439393939384 -INFO:pyaf.std:MODEL_L2 L2_Fit=29.064609552552344 L2_Forecast=9.673213086835407 L2_Test=7.951009014267602 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:CYCLE_DETAIL 'Diff_hospital_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff_hospital_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2943 MAPE_Forecast=0.129 MAPE_Test=0.167 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.369 SMAPE_Forecast=0.1387 SMAPE_Test=0.1651 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.7519 MASE_Forecast=0.6253 MASE_Test=0.8981 +INFO:pyaf.std:MODEL_L1 L1_Fit=22.743613419513693 L1_Forecast=6.789473684210528 L1_Test=7.266081871345032 +INFO:pyaf.std:MODEL_L2 L2_Fit=28.90259518118838 L2_Forecast=9.873157797577502 L2_Test=7.953062649013144 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 45 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend -0.017543859649122806 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_hospital_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -PERFORMANCE MAPE_FORECAST hospital 0.1128 +PERFORMANCE MAPE_FORECAST hospital 0.129 diff --git a/tests/references/expsmooth_expsmooth_dataset_jewelry.log b/tests/references/expsmooth_expsmooth_dataset_jewelry.log index 8f50223a0..7228af7d1 100644 --- a/tests/references/expsmooth_expsmooth_dataset_jewelry.log +++ b/tests/references/expsmooth_expsmooth_dataset_jewelry.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'jewelry' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'jewelry' 3.7953944206237793 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['jewelry']' 3.314265489578247 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998.07692307692 TimeMax=1999.92307692308 TimeDelta=0.019230769230835183 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='jewelry' Length=124 Min=46 Max=426 Mean=124.7258064516129 StdDev=64.43367917049599 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_jewelry' Min=46 Max=426 Mean=124.7258064516129 StdDev=64.43367917049599 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.1904 MASE_Forecast=1.1915 MASE_Test=0.9796 INFO:pyaf.std:MODEL_L1 L1_Fit=37.745987883940906 L1_Forecast=46.71793814432989 L1_Test=24.48969072164948 INFO:pyaf.std:MODEL_L2 L2_Fit=58.03260634224021 L2_Forecast=88.5534243960142 L2_Test=27.49536236607993 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 116.01030927835052 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _jewelry_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'jewelry' PERFORMANCE MAPE_FORECAST jewelry 0.1974 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'jewelry' 4.009104013442993 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['jewelry']' 4.36174750328064 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998.07692307692 TimeMax=1999.90384615385 TimeDelta=0.01923076923084361 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='jewelry' Length=124 Min=46 Max=426 Mean=124.7258064516129 StdDev=64.43367917049599 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_jewelry' Min=46 Max=426 Mean=124.7258064516129 StdDev=64.43367917049599 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.1221 MASE_Forecast=1.4999 MASE_Test=1.0415 INFO:pyaf.std:MODEL_L1 L1_Fit=33.992838541666664 L1_Forecast=61.364583333333336 L1_Test=22.21875 INFO:pyaf.std:MODEL_L2 L2_Fit=48.902582397089894 L2_Forecast=111.80954061436722 L2_Test=26.87327392712879 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 112.78125 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _jewelry_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'jewelry' PERFORMANCE MAPE_FORECAST jewelry 0.2299 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'jewelry' 4.086991548538208 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['jewelry']' 3.72409987449646 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998.07692307692 TimeMax=1999.82692307692 TimeDelta=0.019230769230769232 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='jewelry' Length=124 Min=46 Max=426 Mean=124.7258064516129 StdDev=64.43367917049599 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_jewelry' Min=46 Max=426 Mean=124.7258064516129 StdDev=64.43367917049599 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9891 MASE_Forecast=1.0198 MASE_Test=0.9427 INFO:pyaf.std:MODEL_L1 L1_Fit=29.07608695652174 L1_Forecast=40.791666666666664 L1_Test=50.5 INFO:pyaf.std:MODEL_L2 L2_Fit=43.77176477256027 L2_Forecast=64.54617210441943 L2_Test=71.5122367151245 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 108 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _jewelry_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'jewelry' PERFORMANCE MAPE_FORECAST jewelry 0.2313 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'jewelry' 5.081962823867798 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['jewelry']' 3.6094346046447754 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998.07692307692 TimeMax=1999.76923076923 TimeDelta=0.01923076923079686 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='jewelry' Length=124 Min=46 Max=426 Mean=124.7258064516129 StdDev=64.43367917049599 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_jewelry' Min=46 Max=426 Mean=124.7258064516129 StdDev=64.43367917049599 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9888 MASE_Forecast=0.9715 MASE_Test=0.9684 INFO:pyaf.std:MODEL_L1 L1_Fit=29.39325842696629 L1_Forecast=42.34782608695652 L1_Test=39.0 INFO:pyaf.std:MODEL_L2 L2_Fit=44.351507074453295 L2_Forecast=65.89715306907193 L2_Test=59.31975500061791 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 108 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _jewelry_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST jewelry 0.2457 diff --git a/tests/references/expsmooth_expsmooth_dataset_mcopper.log b/tests/references/expsmooth_expsmooth_dataset_mcopper.log index bcd976750..1389873d6 100644 --- a/tests/references/expsmooth_expsmooth_dataset_mcopper.log +++ b/tests/references/expsmooth_expsmooth_dataset_mcopper.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'mcopper' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'mcopper' 5.101961135864258 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['mcopper']' 4.209702491760254 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1960.0 TimeMax=1997.33333333333 TimeDelta=0.08333333333332606 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='mcopper' Length=564 Min=216.6 Max=4306.0 Mean=997.8104609929078 StdDev=606.066125450793 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_mcopper' Min=216.6 Max=4306.0 Mean=997.8104609929078 StdDev=606.066125450793 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9978 MASE_Forecast=0.9975 MASE_Test=1.172 INFO:pyaf.std:MODEL_L1 L1_Fit=42.738530066815144 L1_Forecast=66.41238938053098 L1_Test=281.39999999999986 INFO:pyaf.std:MODEL_L2 L2_Fit=63.94561859959314 L2_Forecast=121.46714986190801 L2_Test=284.4145741694682 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 255.2 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _mcopper_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'mcopper' PERFORMANCE MAPE_FORECAST mcopper 0.0397 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'mcopper' 5.464546203613281 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['mcopper']' 5.1963348388671875 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1960.0 TimeMax=1997.25 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='mcopper' Length=564 Min=216.6 Max=4306.0 Mean=997.8104609929078 StdDev=606.066125450793 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_mcopper' Min=216.6 Max=4306.0 Mean=997.8104609929078 StdDev=606.066125450793 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9978 MASE_Forecast=1.0008 MASE_Test=0.7838 INFO:pyaf.std:MODEL_L1 L1_Fit=42.67053571428572 L1_Forecast=67.1125 L1_Test=156.0 INFO:pyaf.std:MODEL_L2 L2_Fit=63.923462884475036 L2_Forecast=122.13467683550927 L2_Test=202.2890259010606 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 255.2 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _mcopper_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'mcopper' PERFORMANCE MAPE_FORECAST mcopper 0.0404 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'mcopper' 4.974180459976196 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['mcopper']' 4.384707689285278 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1960.0 TimeMax=1996.91666666667 TimeDelta=0.08333333333334068 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='mcopper' Length=564 Min=216.6 Max=4306.0 Mean=997.8104609929078 StdDev=606.066125450793 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_mcopper' Min=216.6 Max=4306.0 Mean=997.8104609929078 StdDev=606.066125450793 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9977 MASE_Forecast=1.0077 MASE_Test=1.3046 INFO:pyaf.std:MODEL_L1 L1_Fit=42.65157657657657 L1_Forecast=55.4357142857143 L1_Test=263.8625 INFO:pyaf.std:MODEL_L2 L2_Fit=63.9852145435192 L2_Forecast=91.9520993312745 L2_Test=335.4531692352898 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 255.2 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _mcopper_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'mcopper' PERFORMANCE MAPE_FORECAST mcopper 0.0382 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'mcopper' 6.601006746292114 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['mcopper']' 4.779906988143921 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1960.0 TimeMax=1996.66666666667 TimeDelta=0.08333333333334074 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='mcopper' Length=564 Min=216.6 Max=4306.0 Mean=997.8104609929078 StdDev=606.066125450793 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_mcopper' Min=216.6 Max=4306.0 Mean=997.8104609929078 StdDev=606.066125450793 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9977 MASE_Forecast=0.9925 MASE_Test=0.9335 INFO:pyaf.std:MODEL_L1 L1_Fit=42.62698412698413 L1_Forecast=48.27387387387389 L1_Test=258.3416666666667 INFO:pyaf.std:MODEL_L2 L2_Fit=63.987380239637666 L2_Forecast=63.86391500401177 L2_Test=342.3749516246771 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 255.2 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _mcopper_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST mcopper 0.0368 diff --git a/tests/references/expsmooth_expsmooth_dataset_msales.log b/tests/references/expsmooth_expsmooth_dataset_msales.log index f5849a059..836eef570 100644 --- a/tests/references/expsmooth_expsmooth_dataset_msales.log +++ b/tests/references/expsmooth_expsmooth_dataset_msales.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'msales' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'msales' 3.6361069679260254 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['msales']' 3.2516729831695557 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=3.1666666666666696 TimeDelta=0.08333333333333345 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='msales' Length=36 Min=0 Max=1 Mean=0.4722222222222222 StdDev=0.4992277987669841 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_msales' Min=1 Max=17 Mean=5.972222222222222 StdDev=5.008249367632872 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.1254 MASE_Forecast=2.5714 MASE_Test=10000000 INFO:pyaf.std:MODEL_L1 L1_Fit=0.38957475994513036 L1_Forecast=0.8571428571428571 L1_Test=1.0 INFO:pyaf.std:MODEL_L2 L2_Fit=0.7288482931137599 L2_Forecast=0.9258200997725514 L2_Test=1.0 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.5185185185185186 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_msales_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'msales' PERFORMANCE MAPE_FORECAST msales 0.8571 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'msales' 3.1031248569488525 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['msales']' 2.9869606494903564 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=3.0 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='msales' Length=36 Min=0 Max=1 Mean=0.4722222222222222 StdDev=0.4992277987669841 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_msales' Min=1 Max=17 Mean=5.972222222222222 StdDev=5.008249367632872 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.2096 MASE_Forecast=1.1429 MASE_Test=10000000 INFO:pyaf.std:MODEL_L1 L1_Fit=0.4032 L1_Forecast=0.5714285714285714 L1_Test=1.0 INFO:pyaf.std:MODEL_L2 L2_Fit=0.7021794642397341 L2_Forecast=0.7559289460184544 L2_Test=1.0 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.08 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_msales_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'msales' PERFORMANCE MAPE_FORECAST msales 0.5714 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'msales' 4.047101259231567 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['msales']' 3.266441822052002 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=3.9166666666666696 TimeDelta=0.08333333333333341 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='msales' Length=36 Min=0 Max=1 Mean=0.4722222222222222 StdDev=0.4992277987669841 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_msales' Min=1 Max=17 Mean=5.972222222222222 StdDev=5.008249367632872 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.6991 MASE_Forecast=1.6991 MASE_Test=1.6991 INFO:pyaf.std:MODEL_L1 L1_Fit=0.5825617283950617 L1_Forecast=0.5825617283950617 L1_Test=0.5825617283950617 INFO:pyaf.std:MODEL_L2 L2_Fit=1.0635761716852632 L2_Forecast=1.0635761716852632 L2_Test=1.0635761716852632 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5.972222222222222 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_msales_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'msales' PERFORMANCE MAPE_FORECAST msales 0.5826 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'msales' 3.384752035140991 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['msales']' 3.5951874256134033 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=3.9166666666666696 TimeDelta=0.08333333333333341 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='msales' Length=36 Min=0 Max=1 Mean=0.4722222222222222 StdDev=0.4992277987669841 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_msales' Min=1 Max=17 Mean=5.972222222222222 StdDev=5.008249367632872 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.6991 MASE_Forecast=1.6991 MASE_Test=1.6991 INFO:pyaf.std:MODEL_L1 L1_Fit=0.5825617283950617 L1_Forecast=0.5825617283950617 L1_Test=0.5825617283950617 INFO:pyaf.std:MODEL_L2 L2_Fit=1.0635761716852632 L2_Forecast=1.0635761716852632 L2_Test=1.0635761716852632 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5.972222222222222 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_msales_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST msales 0.5826 diff --git a/tests/references/expsmooth_expsmooth_dataset_partx.log b/tests/references/expsmooth_expsmooth_dataset_partx.log index 8cfc2550e..161e5b7b0 100644 --- a/tests/references/expsmooth_expsmooth_dataset_partx.log +++ b/tests/references/expsmooth_expsmooth_dataset_partx.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'partx' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'partx' 3.410382032394409 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['partx']' 3.6955490112304688 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=4.16666666666667 TimeDelta=0.08333333333333341 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='partx' Length=51 Min=0 Max=5 Mean=0.6274509803921569 StdDev=1.1195374449898194 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_partx' Min=0 Max=32 Mean=11.882352941176471 StdDev=11.47486094379161 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8292 MASE_Forecast=0.5727 MASE_Test=0.0 INFO:pyaf.std:MODEL_L1 L1_Fit=0.8073635765943459 L1_Forecast=0.7 L1_Test=0.0 INFO:pyaf.std:MODEL_L2 L2_Fit=1.71780642546125 L2_Forecast=1.0488088481701516 L2_Test=0.0 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.487179487179487 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_partx_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'partx' PERFORMANCE MAPE_FORECAST partx 0.5 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'partx' 3.314575672149658 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['partx']' 2.861417293548584 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=4.0 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='partx' Length=51 Min=0 Max=5 Mean=0.6274509803921569 StdDev=1.1195374449898194 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_partx' Min=0 Max=32 Mean=11.882352941176471 StdDev=11.47486094379161 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8275 MASE_Forecast=0.8182 MASE_Test=0.75 INFO:pyaf.std:MODEL_L1 L1_Fit=0.689554419284149 L1_Forecast=1.0 L1_Test=0.5 INFO:pyaf.std:MODEL_L2 L2_Fit=1.5281045604497954 L2_Forecast=1.5491933384829668 L2_Test=1.0 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5.513513513513513 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_partx_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'partx' PERFORMANCE MAPE_FORECAST partx 0.6 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'partx' 3.634035587310791 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['partx']' 4.2746262550354 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=3.75 TimeDelta=0.08333333333333333 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='partx' Length=51 Min=0 Max=5 Mean=0.6274509803921569 StdDev=1.1195374449898194 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_partx' Min=0 Max=32 Mean=11.882352941176471 StdDev=11.47486094379161 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7922 MASE_Forecast=0.8366 MASE_Test=0.7 INFO:pyaf.std:MODEL_L1 L1_Fit=0.48010380622837373 L1_Forecast=1.7777777777777777 L1_Test=0.5 INFO:pyaf.std:MODEL_L2 L2_Fit=1.0940064868932646 L2_Forecast=2.494438257849294 L2_Test=0.8660254037844386 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.323529411764706 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_partx_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'partx' PERFORMANCE MAPE_FORECAST partx 0.6667 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'partx' 3.3214316368103027 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['partx']' 3.356996536254883 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=5.16666666666667 TimeDelta=0.0833333333333334 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='partx' Length=51 Min=0 Max=5 Mean=0.6274509803921569 StdDev=1.1195374449898194 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_partx' Min=-3.0 Max=5.0 Mean=0.0 StdDev=1.495090003192804 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6536 MASE_Forecast=0.6536 MASE_Test=0.6536 INFO:pyaf.std:MODEL_L1 L1_Fit=0.6274509803921569 L1_Forecast=0.6274509803921569 L1_Test=0.6274509803921569 INFO:pyaf.std:MODEL_L2 L2_Fit=1.2833778958394957 L2_Forecast=1.2833778958394957 L2_Test=1.2833778958394957 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_partx_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST partx 0.3333 diff --git a/tests/references/expsmooth_expsmooth_dataset_ukcars.log b/tests/references/expsmooth_expsmooth_dataset_ukcars.log index 7f4930fe7..d899e183b 100644 --- a/tests/references/expsmooth_expsmooth_dataset_ukcars.log +++ b/tests/references/expsmooth_expsmooth_dataset_ukcars.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'ukcars' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ukcars' 3.923757791519165 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ukcars']' 3.4918763637542725 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1977.0 TimeMax=1998.75 TimeDelta=0.25 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ukcars' Length=113 Min=171.153 Max=494.311 Mean=333.4777522123894 StdDev=78.21193814976164 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_ukcars' Min=330.371 Max=37682.986000000004 Mean=17058.76386725664 StdDev=10825.83386022961 @@ -11,24 +11,33 @@ INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_ukcars_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1049 MAPE_Forecast=0.0585 MAPE_Test=0.0418 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1126 SMAPE_Forecast=0.0581 SMAPE_Test=0.0409 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7735 MASE_Forecast=0.669 MASE_Test=0.4508 -INFO:pyaf.std:MODEL_L1 L1_Fit=31.906128299453123 L1_Forecast=24.0153185553631 L1_Test=17.46974597963151 -INFO:pyaf.std:MODEL_L2 L2_Fit=57.58616981997759 L2_Forecast=28.544831832976875 L2_Test=18.10330777924043 +INFO:pyaf.std:MODEL_L1 L1_Fit=31.906128299452835 L1_Forecast=24.0153185553631 L1_Test=17.46974597963151 +INFO:pyaf.std:MODEL_L2 L2_Fit=57.58616981997598 L2_Forecast=28.544831832978595 L2_Test=18.103307779238527 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12588.319352272729 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_ukcars_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag1 1.2794577357334633 -INFO:pyaf.std:AR_MODEL_COEFF 2 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.4997948644143176 -INFO:pyaf.std:AR_MODEL_COEFF 3 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag10 0.38726991599544264 -INFO:pyaf.std:AR_MODEL_COEFF 4 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.3857790602549336 -INFO:pyaf.std:AR_MODEL_COEFF 5 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag7 0.3414842343329012 -INFO:pyaf.std:AR_MODEL_COEFF 6 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag16 0.28500637998641676 -INFO:pyaf.std:AR_MODEL_COEFF 7 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.23214811790604173 -INFO:pyaf.std:AR_MODEL_COEFF 8 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.19985902326619734 -INFO:pyaf.std:AR_MODEL_COEFF 9 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag20 0.18238488493479899 -INFO:pyaf.std:AR_MODEL_COEFF 10 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag4 0.17813293825528895 +INFO:pyaf.std:AR_MODEL_COEFF 1 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag1 1.2794577357334331 +INFO:pyaf.std:AR_MODEL_COEFF 2 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.4997948644143597 +INFO:pyaf.std:AR_MODEL_COEFF 3 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag10 0.38726991599543803 +INFO:pyaf.std:AR_MODEL_COEFF 4 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.385779060254794 +INFO:pyaf.std:AR_MODEL_COEFF 5 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag7 0.34148423433293495 +INFO:pyaf.std:AR_MODEL_COEFF 6 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag16 0.2850063799863805 +INFO:pyaf.std:AR_MODEL_COEFF 7 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.23214811790600348 +INFO:pyaf.std:AR_MODEL_COEFF 8 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.199859023266163 +INFO:pyaf.std:AR_MODEL_COEFF 9 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag20 0.18238488493492716 +INFO:pyaf.std:AR_MODEL_COEFF 10 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag4 0.1781329382551621 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'ukcars' PERFORMANCE MAPE_FORECAST ukcars 0.0585 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ukcars' 4.198536396026611 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ukcars']' 3.567638635635376 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1977.0 TimeMax=1998.5 TimeDelta=0.25 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ukcars' Length=113 Min=171.153 Max=494.311 Mean=333.4777522123894 StdDev=78.21193814976164 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_ukcars' Min=330.371 Max=37682.986000000004 Mean=17058.76386725664 StdDev=10825.83386022961 @@ -40,24 +49,33 @@ INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_ukcars_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1065 MAPE_Forecast=0.0649 MAPE_Test=0.0358 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1143 SMAPE_Forecast=0.0643 SMAPE_Test=0.0356 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7711 MASE_Forecast=0.7286 MASE_Test=0.4303 -INFO:pyaf.std:MODEL_L1 L1_Fit=32.083532287331614 L1_Forecast=26.710721798961593 L1_Test=14.760839613862402 -INFO:pyaf.std:MODEL_L2 L2_Fit=57.880431056995654 L2_Forecast=31.445537788860758 L2_Test=15.100572806801646 +INFO:pyaf.std:MODEL_L1 L1_Fit=32.083532287332474 L1_Forecast=26.710721798958616 L1_Test=14.760839613862402 +INFO:pyaf.std:MODEL_L2 L2_Fit=57.880431056995775 L2_Forecast=31.445537788857756 L2_Test=15.100572806801276 INFO:pyaf.std:MODEL_COMPLEXITY 53 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12418.11959770115 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_ukcars_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag1 1.2821795704893058 -INFO:pyaf.std:AR_MODEL_COEFF 2 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.5077472376768583 -INFO:pyaf.std:AR_MODEL_COEFF 3 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag10 0.38452194131338935 -INFO:pyaf.std:AR_MODEL_COEFF 4 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.3788991853207042 -INFO:pyaf.std:AR_MODEL_COEFF 5 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag7 0.32678796506645047 -INFO:pyaf.std:AR_MODEL_COEFF 6 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag16 0.274252181355105 -INFO:pyaf.std:AR_MODEL_COEFF 7 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.22348560787855076 -INFO:pyaf.std:AR_MODEL_COEFF 8 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag20 0.20179008505531956 -INFO:pyaf.std:AR_MODEL_COEFF 9 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.2010476663227675 -INFO:pyaf.std:AR_MODEL_COEFF 10 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag4 0.18235590649293498 +INFO:pyaf.std:AR_MODEL_COEFF 1 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag1 1.2821795704892722 +INFO:pyaf.std:AR_MODEL_COEFF 2 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.5077472376768094 +INFO:pyaf.std:AR_MODEL_COEFF 3 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag10 0.3845219413133698 +INFO:pyaf.std:AR_MODEL_COEFF 4 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.37889918532062394 +INFO:pyaf.std:AR_MODEL_COEFF 5 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag7 0.3267879650665118 +INFO:pyaf.std:AR_MODEL_COEFF 6 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag16 0.27425218135517637 +INFO:pyaf.std:AR_MODEL_COEFF 7 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.22348560787860558 +INFO:pyaf.std:AR_MODEL_COEFF 8 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag20 0.2017900850552468 +INFO:pyaf.std:AR_MODEL_COEFF 9 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20104766632277182 +INFO:pyaf.std:AR_MODEL_COEFF 10 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag4 0.18235590649300476 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'ukcars' PERFORMANCE MAPE_FORECAST ukcars 0.0649 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ukcars' 3.8948192596435547 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ukcars']' 3.6304967403411865 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1977.0 TimeMax=1997.75 TimeDelta=0.25 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ukcars' Length=113 Min=171.153 Max=494.311 Mean=333.4777522123894 StdDev=78.21193814976164 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_ukcars' Min=330.371 Max=37682.986000000004 Mean=17058.76386725664 StdDev=10825.83386022961 @@ -69,48 +87,66 @@ INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_ukcars_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1094 MAPE_Forecast=0.0588 MAPE_Test=0.0517 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1175 SMAPE_Forecast=0.0582 SMAPE_Test=0.0512 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7807 MASE_Forecast=0.6479 MASE_Test=0.6914 -INFO:pyaf.std:MODEL_L1 L1_Fit=32.88658679206735 L1_Forecast=24.143853422234166 L1_Test=21.380825770155454 -INFO:pyaf.std:MODEL_L2 L2_Fit=58.782703261272566 L2_Forecast=30.281994654664746 L2_Test=23.122940399452503 +INFO:pyaf.std:MODEL_L1 L1_Fit=32.886586792067064 L1_Forecast=24.14385342222793 L1_Test=21.380825770154544 +INFO:pyaf.std:MODEL_L2 L2_Fit=58.782703261271934 L2_Forecast=30.281994654660128 L2_Test=23.122940399451853 INFO:pyaf.std:MODEL_COMPLEXITY 53 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 11913.174511904761 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_ukcars_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag1 1.2762290325098342 -INFO:pyaf.std:AR_MODEL_COEFF 2 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.5040225699181682 -INFO:pyaf.std:AR_MODEL_COEFF 3 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag10 0.3923805675699522 -INFO:pyaf.std:AR_MODEL_COEFF 4 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.3801757587431416 -INFO:pyaf.std:AR_MODEL_COEFF 5 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag7 0.3314868340809719 -INFO:pyaf.std:AR_MODEL_COEFF 6 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag16 0.2725390459533594 -INFO:pyaf.std:AR_MODEL_COEFF 7 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.2210579535439982 -INFO:pyaf.std:AR_MODEL_COEFF 8 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20079948945883053 -INFO:pyaf.std:AR_MODEL_COEFF 9 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag20 0.20051969777878037 -INFO:pyaf.std:AR_MODEL_COEFF 10 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag4 0.18466462270166156 +INFO:pyaf.std:AR_MODEL_COEFF 1 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag1 1.2762290325097934 +INFO:pyaf.std:AR_MODEL_COEFF 2 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.5040225699181802 +INFO:pyaf.std:AR_MODEL_COEFF 3 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag10 0.39238056756993667 +INFO:pyaf.std:AR_MODEL_COEFF 4 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.3801757587431611 +INFO:pyaf.std:AR_MODEL_COEFF 5 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag7 0.3314868340809614 +INFO:pyaf.std:AR_MODEL_COEFF 6 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag16 0.27253904595337997 +INFO:pyaf.std:AR_MODEL_COEFF 7 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.22105795354400853 +INFO:pyaf.std:AR_MODEL_COEFF 8 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20079948945881695 +INFO:pyaf.std:AR_MODEL_COEFF 9 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag20 0.20051969777876724 +INFO:pyaf.std:AR_MODEL_COEFF 10 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag4 0.18466462270168693 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'ukcars' PERFORMANCE MAPE_FORECAST ukcars 0.0588 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ukcars' 4.6766932010650635 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ukcars']' 4.091702461242676 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1977.0 TimeMax=1996.75 TimeDelta=0.25 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ukcars' Length=113 Min=171.153 Max=494.311 Mean=333.4777522123894 StdDev=78.21193814976164 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_ukcars' Min=171.153 Max=494.311 Mean=333.4777522123894 StdDev=78.21193814976164 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_ukcars_ConstantTrend_residue_bestCycle_byL2_residue_AR(28)' [ConstantTrend + Cycle + AR] -INFO:pyaf.std:TREND_DETAIL '_ukcars_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_ukcars_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_ukcars_ConstantTrend_residue_bestCycle_byL2_residue_AR(28)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0691 MAPE_Forecast=0.063 MAPE_Test=0.071 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0677 SMAPE_Forecast=0.0627 SMAPE_Test=0.0752 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4556 MASE_Forecast=0.6407 MASE_Test=1.1301 -INFO:pyaf.std:MODEL_L1 L1_Fit=19.121063965601174 L1_Forecast=26.045723735738648 L1_Test=28.78897885654709 -INFO:pyaf.std:MODEL_L2 L2_Fit=25.079538813781507 L2_Forecast=31.58311679347306 L2_Test=39.06459329323811 -INFO:pyaf.std:MODEL_COMPLEXITY 28 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_ukcars' Min=330.371 Max=37682.986000000004 Mean=17058.76386725664 StdDev=10825.83386022961 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' +INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_AR(28)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL 'CumSum_ukcars_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'CumSum_ukcars_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_AR(28)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1097 MAPE_Forecast=0.0547 MAPE_Test=0.0604 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1181 SMAPE_Forecast=0.0544 SMAPE_Test=0.0593 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7834 MASE_Forecast=0.5604 MASE_Test=0.9641 +INFO:pyaf.std:MODEL_L1 L1_Fit=32.87641212522941 L1_Forecast=22.78217855585463 L1_Test=24.560147335141565 +INFO:pyaf.std:MODEL_L2 L2_Fit=60.105199257990485 L2_Forecast=28.54131618077838 L2_Test=27.736992760971006 +INFO:pyaf.std:MODEL_COMPLEXITY 52 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 11258.121087500002 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_ukcars_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag4 0.490333944356374 -INFO:pyaf.std:AR_MODEL_COEFF 2 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 0.3699797530359726 -INFO:pyaf.std:AR_MODEL_COEFF 3 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag11 -0.34437609072182507 -INFO:pyaf.std:AR_MODEL_COEFF 4 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag8 0.30085693687404214 -INFO:pyaf.std:AR_MODEL_COEFF 5 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag9 -0.29115363897059304 -INFO:pyaf.std:AR_MODEL_COEFF 6 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag16 0.2431333151769504 -INFO:pyaf.std:AR_MODEL_COEFF 7 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag12 -0.2121065836204563 -INFO:pyaf.std:AR_MODEL_COEFF 8 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag25 -0.15741296171724783 -INFO:pyaf.std:AR_MODEL_COEFF 9 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag3 0.1568664681082159 -INFO:pyaf.std:AR_MODEL_COEFF 10 _ukcars_ConstantTrend_residue_bestCycle_byL2_residue_Lag20 0.1335416179552776 +INFO:pyaf.std:AR_MODEL_COEFF 1 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag1 1.2813667262726367 +INFO:pyaf.std:AR_MODEL_COEFF 2 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.507717863007167 +INFO:pyaf.std:AR_MODEL_COEFF 3 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.401879649137052 +INFO:pyaf.std:AR_MODEL_COEFF 4 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag10 0.39720029543866464 +INFO:pyaf.std:AR_MODEL_COEFF 5 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag7 0.3538518608049158 +INFO:pyaf.std:AR_MODEL_COEFF 6 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag16 0.30173876486975987 +INFO:pyaf.std:AR_MODEL_COEFF 7 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag17 -0.23522134837296335 +INFO:pyaf.std:AR_MODEL_COEFF 8 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag4 0.20330594629250295 +INFO:pyaf.std:AR_MODEL_COEFF 9 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.1864519544097212 +INFO:pyaf.std:AR_MODEL_COEFF 10 CumSum_ukcars_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.1598391561289742 INFO:pyaf.std:AR_MODEL_DETAIL_END -PERFORMANCE MAPE_FORECAST ukcars 0.063 +PERFORMANCE MAPE_FORECAST ukcars 0.0547 diff --git a/tests/references/expsmooth_expsmooth_dataset_unemp.cci.log b/tests/references/expsmooth_expsmooth_dataset_unemp.cci.log index 58e42953a..a1f4bd84f 100644 --- a/tests/references/expsmooth_expsmooth_dataset_unemp.cci.log +++ b/tests/references/expsmooth_expsmooth_dataset_unemp.cci.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'unemp.cci' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'unemp.cci' 3.6291730403900146 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['unemp.cci']' 4.014683246612549 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1997.41666666667 TimeMax=2003.83333333333 TimeDelta=0.08333333333324869 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='unemp.cci' Length=100 Min=0 Max=1 Mean=0.49 StdDev=0.49989998999799956 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_unemp.cci' Min=0 Max=1 Mean=0.49 StdDev=0.49989998999799956 @@ -11,24 +11,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_unemp.cci_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=201061351.07 MAPE_Forecast=0.0 MAPE_Test=0.0 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.3407 SMAPE_Forecast=0.0 SMAPE_Test=0.0 INFO:pyaf.std:MODEL_MASE MASE_Fit=3.0963 MASE_Forecast=28.7728 MASE_Test=28.7728 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.04021227020996999 L1_Forecast=2.8772820792610787e-09 L1_Test=2.8772820792610787e-09 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.12132527774292165 L2_Forecast=2.8772820792610783e-09 L2_Test=2.8772820792610787e-09 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.04021227020997018 L1_Forecast=2.8772823013056836e-09 L1_Test=2.8772823013056836e-09 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.12132527774292168 L2_Forecast=2.8772823013056832e-09 L2_Test=2.8772823013056836e-09 INFO:pyaf.std:MODEL_COMPLEXITY 19 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.34615384615384615 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _unemp.cci_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5990290977611172 -INFO:pyaf.std:AR_MODEL_COEFF 2 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag2 0.22880875509456283 -INFO:pyaf.std:AR_MODEL_COEFF 3 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag3 0.08739716752257345 -INFO:pyaf.std:AR_MODEL_COEFF 4 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag4 0.03338274747315675 -INFO:pyaf.std:AR_MODEL_COEFF 5 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag5 0.01275107489689713 -INFO:pyaf.std:AR_MODEL_COEFF 6 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag6 0.004870477217534764 -INFO:pyaf.std:AR_MODEL_COEFF 7 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag7 0.0018603567557081077 -INFO:pyaf.std:AR_MODEL_COEFF 8 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag8 0.0007105930495893177 -INFO:pyaf.std:AR_MODEL_COEFF 9 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag9 0.00027142239306009695 -INFO:pyaf.std:AR_MODEL_COEFF 10 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag10 0.00010367412959126632 +INFO:pyaf.std:AR_MODEL_COEFF 1 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag1 0.599029097761117 +INFO:pyaf.std:AR_MODEL_COEFF 2 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag2 0.22880875509456308 +INFO:pyaf.std:AR_MODEL_COEFF 3 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag3 0.08739716752257276 +INFO:pyaf.std:AR_MODEL_COEFF 4 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag4 0.03338274747315663 +INFO:pyaf.std:AR_MODEL_COEFF 5 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag5 0.012751074896897006 +INFO:pyaf.std:AR_MODEL_COEFF 6 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag6 0.0048704772175348875 +INFO:pyaf.std:AR_MODEL_COEFF 7 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag7 0.0018603567557079533 +INFO:pyaf.std:AR_MODEL_COEFF 8 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag8 0.0007105930495897497 +INFO:pyaf.std:AR_MODEL_COEFF 9 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag9 0.000271422393060319 +INFO:pyaf.std:AR_MODEL_COEFF 10 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag10 0.00010367412959139122 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'unemp.cci' PERFORMANCE MAPE_FORECAST unemp.cci 0.0 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'unemp.cci' 3.596865653991699 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['unemp.cci']' 3.838315725326538 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1997.41666666667 TimeMax=2003.66666666667 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='unemp.cci' Length=100 Min=0 Max=1 Mean=0.49 StdDev=0.49989998999799956 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_unemp.cci' Min=0 Max=1 Mean=0.49 StdDev=0.49989998999799956 @@ -40,24 +49,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_unemp.cci_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=206352439.256 MAPE_Forecast=0.0 MAPE_Test=0.0 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.376 SMAPE_Forecast=0.0 SMAPE_Test=0.0 INFO:pyaf.std:MODEL_MASE MASE_Fit=3.0953 MASE_Forecast=37.4681 MASE_Test=37.4681 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.04127048784707556 L1_Forecast=3.7468126379280875e-09 L1_Test=3.7468126379280875e-09 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.12291129640137001 L2_Forecast=3.7468126379280875e-09 L2_Test=3.7468126379280875e-09 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.04127048784707559 L1_Forecast=3.74681274895039e-09 L1_Test=3.74681274895039e-09 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.12291129640137002 L2_Forecast=3.74681274895039e-09 L2_Test=3.74681274895039e-09 INFO:pyaf.std:MODEL_COMPLEXITY 19 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.32894736842105265 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _unemp.cci_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5990290977611168 -INFO:pyaf.std:AR_MODEL_COEFF 2 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag2 0.22880875509456333 -INFO:pyaf.std:AR_MODEL_COEFF 3 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag3 0.0873971675225727 -INFO:pyaf.std:AR_MODEL_COEFF 4 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag4 0.03338274747315627 -INFO:pyaf.std:AR_MODEL_COEFF 5 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag5 0.012751074896896529 -INFO:pyaf.std:AR_MODEL_COEFF 6 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag6 0.0048704772175327815 -INFO:pyaf.std:AR_MODEL_COEFF 7 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag7 0.0018603567557023692 -INFO:pyaf.std:AR_MODEL_COEFF 8 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag8 0.0007105930495756481 -INFO:pyaf.std:AR_MODEL_COEFF 9 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag9 0.0002714223930242281 -INFO:pyaf.std:AR_MODEL_COEFF 10 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag10 0.00010367412949806656 +INFO:pyaf.std:AR_MODEL_COEFF 1 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5990290977611167 +INFO:pyaf.std:AR_MODEL_COEFF 2 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2288087550945631 +INFO:pyaf.std:AR_MODEL_COEFF 3 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag3 0.08739716752257273 +INFO:pyaf.std:AR_MODEL_COEFF 4 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag4 0.03338274747315591 +INFO:pyaf.std:AR_MODEL_COEFF 5 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag5 0.012751074896896452 +INFO:pyaf.std:AR_MODEL_COEFF 6 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag6 0.004870477217532853 +INFO:pyaf.std:AR_MODEL_COEFF 7 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag7 0.0018603567557026988 +INFO:pyaf.std:AR_MODEL_COEFF 8 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag8 0.0007105930495756377 +INFO:pyaf.std:AR_MODEL_COEFF 9 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag9 0.0002714223930248248 +INFO:pyaf.std:AR_MODEL_COEFF 10 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag10 0.00010367412949764329 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'unemp.cci' PERFORMANCE MAPE_FORECAST unemp.cci 0.0 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'unemp.cci' 4.025026321411133 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['unemp.cci']' 4.002584218978882 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1997.41666666667 TimeMax=2003.41666666667 TimeDelta=0.08333333333333333 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='unemp.cci' Length=100 Min=0 Max=1 Mean=0.49 StdDev=0.49989998999799956 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_unemp.cci' Min=0 Max=1 Mean=0.49 StdDev=0.49989998999799956 @@ -69,24 +87,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_unemp.cci_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=214832676.4857 MAPE_Forecast=0.0 MAPE_Test=0.0 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.4326 SMAPE_Forecast=0.0 SMAPE_Test=0.0 INFO:pyaf.std:MODEL_MASE MASE_Fit=3.0936 MASE_Forecast=140.5752 MASE_Test=140.5752 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.04296653529284625 L1_Forecast=1.405751848260195e-08 L1_Test=1.405751848260195e-08 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.12541144316018385 L2_Forecast=1.4057518482601951e-08 L2_Test=1.405751848260195e-08 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.04296653529284646 L1_Forecast=1.405751892669116e-08 L1_Test=1.405751892669116e-08 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.12541144316018382 L2_Forecast=1.4057518926691161e-08 L2_Test=1.405751892669116e-08 INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.3013698630136986 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _unemp.cci_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5990290977611172 -INFO:pyaf.std:AR_MODEL_COEFF 2 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2288087550945646 -INFO:pyaf.std:AR_MODEL_COEFF 3 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag3 0.08739716752257683 -INFO:pyaf.std:AR_MODEL_COEFF 4 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag4 0.03338274747316646 -INFO:pyaf.std:AR_MODEL_COEFF 5 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag5 0.012751074896923729 -INFO:pyaf.std:AR_MODEL_COEFF 6 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag6 0.0048704772176054525 -INFO:pyaf.std:AR_MODEL_COEFF 7 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag7 0.0018603567558922278 -INFO:pyaf.std:AR_MODEL_COEFF 8 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag8 0.000710593050072407 -INFO:pyaf.std:AR_MODEL_COEFF 9 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag9 0.00027142239432568355 -INFO:pyaf.std:AR_MODEL_COEFF 10 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag10 0.00010367413290569144 +INFO:pyaf.std:AR_MODEL_COEFF 1 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5990290977611177 +INFO:pyaf.std:AR_MODEL_COEFF 2 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2288087550945645 +INFO:pyaf.std:AR_MODEL_COEFF 3 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag3 0.08739716752257688 +INFO:pyaf.std:AR_MODEL_COEFF 4 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag4 0.03338274747316634 +INFO:pyaf.std:AR_MODEL_COEFF 5 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag5 0.012751074896923823 +INFO:pyaf.std:AR_MODEL_COEFF 6 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag6 0.004870477217605078 +INFO:pyaf.std:AR_MODEL_COEFF 7 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag7 0.0018603567558921168 +INFO:pyaf.std:AR_MODEL_COEFF 8 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag8 0.0007105930500720739 +INFO:pyaf.std:AR_MODEL_COEFF 9 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag9 0.00027142239432570436 +INFO:pyaf.std:AR_MODEL_COEFF 10 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag10 0.00010367413290584757 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'unemp.cci' PERFORMANCE MAPE_FORECAST unemp.cci 0.0 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'unemp.cci' 4.515006065368652 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['unemp.cci']' 4.050910711288452 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1997.41666666667 TimeMax=2003.16666666667 TimeDelta=0.08333333333333333 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='unemp.cci' Length=100 Min=0 Max=1 Mean=0.49 StdDev=0.49989998999799956 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_unemp.cci' Min=0 Max=1 Mean=0.49 StdDev=0.49989998999799956 @@ -98,19 +125,28 @@ INFO:pyaf.std:AUTOREG_DETAIL '_unemp.cci_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=224039791.1922 MAPE_Forecast=0.0 MAPE_Test=0.0 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.4939 SMAPE_Forecast=0.0 SMAPE_Test=0.0 INFO:pyaf.std:MODEL_MASE MASE_Fit=3.0917 MASE_Forecast=649.1807 MASE_Test=649.1807 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.0448079582339681 L1_Forecast=6.491806692210389e-08 L1_Test=6.491806692210389e-08 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.044807958233968055 L1_Forecast=6.491806692210389e-08 L1_Test=6.491806692210389e-08 INFO:pyaf.std:MODEL_L2 L2_Fit=0.12807063874021976 L2_Forecast=6.491806692210388e-08 L2_Test=6.491806692210389e-08 INFO:pyaf.std:MODEL_COMPLEXITY 17 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 0.2714285714285714 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _unemp.cci_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5990290977611183 -INFO:pyaf.std:AR_MODEL_COEFF 2 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag2 0.22880875509456639 -INFO:pyaf.std:AR_MODEL_COEFF 3 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag3 0.0873971675225812 -INFO:pyaf.std:AR_MODEL_COEFF 4 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag4 0.03338274747317873 -INFO:pyaf.std:AR_MODEL_COEFF 5 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag5 0.012751074896955403 -INFO:pyaf.std:AR_MODEL_COEFF 6 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag6 0.004870477217688719 -INFO:pyaf.std:AR_MODEL_COEFF 7 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag7 0.0018603567561115211 -INFO:pyaf.std:AR_MODEL_COEFF 8 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag8 0.0007105930506463957 -INFO:pyaf.std:AR_MODEL_COEFF 9 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag9 0.00027142239582946676 -INFO:pyaf.std:AR_MODEL_COEFF 10 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag10 0.00010367413684219534 +INFO:pyaf.std:AR_MODEL_COEFF 1 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5990290977611186 +INFO:pyaf.std:AR_MODEL_COEFF 2 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag2 0.22880875509456644 +INFO:pyaf.std:AR_MODEL_COEFF 3 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag3 0.08739716752258113 +INFO:pyaf.std:AR_MODEL_COEFF 4 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag4 0.033382747473178445 +INFO:pyaf.std:AR_MODEL_COEFF 5 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag5 0.012751074896955497 +INFO:pyaf.std:AR_MODEL_COEFF 6 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag6 0.004870477217688657 +INFO:pyaf.std:AR_MODEL_COEFF 7 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag7 0.0018603567561112436 +INFO:pyaf.std:AR_MODEL_COEFF 8 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag8 0.0007105930506464096 +INFO:pyaf.std:AR_MODEL_COEFF 9 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag9 0.00027142239582957084 +INFO:pyaf.std:AR_MODEL_COEFF 10 _unemp.cci_ConstantTrend_residue_zeroCycle_residue_Lag10 0.00010367413684235494 INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST unemp.cci 0.0 diff --git a/tests/references/expsmooth_expsmooth_dataset_usgdp.log b/tests/references/expsmooth_expsmooth_dataset_usgdp.log index c668f182c..eab52d878 100644 --- a/tests/references/expsmooth_expsmooth_dataset_usgdp.log +++ b/tests/references/expsmooth_expsmooth_dataset_usgdp.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'usgdp' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'usgdp' 3.570011615753174 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['usgdp']' 3.9483563899993896 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1947.0 TimeMax=1993.75 TimeDelta=0.25 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='usgdp' Length=237 Min=1568.0 Max=11403.6 Mean=5168.4691983122375 StdDev=2763.166813429889 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_usgdp' Min=1568.0 Max=11403.6 Mean=5168.4691983122375 StdDev=2763.166813429889 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9947 MASE_Forecast=0.9998 MASE_Test=0.6481 INFO:pyaf.std:MODEL_L1 L1_Fit=41.97074468085106 L1_Forecast=78.32553191489356 L1_Test=100.65000000000055 INFO:pyaf.std:MODEL_L2 L2_Fit=52.18390301656778 L2_Forecast=88.26538682087273 L2_Test=114.52966864529107 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1570.5 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _usgdp_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'usgdp' PERFORMANCE MAPE_FORECAST usgdp 0.0084 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'usgdp' 4.326260328292847 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['usgdp']' 5.244510889053345 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1947.0 TimeMax=1993.25 TimeDelta=0.25 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='usgdp' Length=237 Min=1568.0 Max=11403.6 Mean=5168.4691983122375 StdDev=2763.166813429889 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_usgdp' Min=1568.0 Max=11403.6 Mean=5168.4691983122375 StdDev=2763.166813429889 @@ -33,11 +42,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9946 MASE_Forecast=0.9892 MASE_Test=0.9645 INFO:pyaf.std:MODEL_L1 L1_Fit=41.66989247311828 L1_Forecast=76.98297872340419 L1_Test=101.07500000000027 INFO:pyaf.std:MODEL_L2 L2_Fit=51.85742551115665 L2_Forecast=87.16054885437414 L2_Test=108.52155315880836 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1570.5 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _usgdp_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'usgdp' PERFORMANCE MAPE_FORECAST usgdp 0.0084 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'usgdp' 5.048330068588257 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['usgdp']' 5.113597393035889 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1947.0 TimeMax=1992.5 TimeDelta=0.25 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='usgdp' Length=237 Min=1568.0 Max=11403.6 Mean=5168.4691983122375 StdDev=2763.166813429889 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_usgdp' Min=1568.0 Max=11403.6 Mean=5168.4691983122375 StdDev=2763.166813429889 @@ -52,11 +70,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9945 MASE_Forecast=1.0025 MASE_Test=0.9896 INFO:pyaf.std:MODEL_L1 L1_Fit=41.65355191256831 L1_Forecast=73.03043478260865 L1_Test=98.88750000000005 INFO:pyaf.std:MODEL_L2 L2_Fit=51.85554441271249 L2_Forecast=84.38560303748501 L2_Test=102.89885203441295 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1570.5 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _usgdp_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'usgdp' PERFORMANCE MAPE_FORECAST usgdp 0.0081 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'usgdp' 4.882697582244873 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['usgdp']' 3.792506217956543 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1947.0 TimeMax=1991.75 TimeDelta=0.25 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='usgdp' Length=237 Min=1568.0 Max=11403.6 Mean=5168.4691983122375 StdDev=2763.166813429889 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_usgdp' Min=1568.0 Max=11403.6 Mean=5168.4691983122375 StdDev=2763.166813429889 @@ -71,6 +98,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9944 MASE_Forecast=1.0017 MASE_Test=0.9884 INFO:pyaf.std:MODEL_L1 L1_Fit=41.15111111111111 L1_Forecast=68.90888888888884 L1_Test=105.41666666666667 INFO:pyaf.std:MODEL_L2 L2_Fit=51.45716017556092 L2_Forecast=79.09823006869371 L2_Test=110.37805488411182 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1570.5 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _usgdp_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST usgdp 0.008 diff --git a/tests/references/expsmooth_expsmooth_dataset_usnetelec.log b/tests/references/expsmooth_expsmooth_dataset_usnetelec.log index a0fc4225b..f4854e96d 100644 --- a/tests/references/expsmooth_expsmooth_dataset_usnetelec.log +++ b/tests/references/expsmooth_expsmooth_dataset_usnetelec.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'usnetelec' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'usnetelec' 2.811814308166504 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['usnetelec']' 2.319650650024414 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1949 TimeMax=1990 TimeDelta=1 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='usnetelec' Length=55 Min=296.1 Max=3858.5 Mean=1972.06 StdDev=1119.355557062756 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_usnetelec' Min=-65.5 Max=259.9000000000001 Mean=64.58 StdDev=50.66391723002584 @@ -14,11 +14,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=2.87 MASE_Forecast=0.562 MASE_Test=3.6087 INFO:pyaf.std:MODEL_L1 L1_Fit=199.43690476190446 L1_Forecast=44.60909090909069 L1_Test=37.89166666666665 INFO:pyaf.std:MODEL_L2 L2_Fit=224.76160988134717 L2_Forecast=55.22255843037311 L2_Test=37.899935869192255 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 296.1 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 65.28333333333333 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_usnetelec_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'usnetelec' PERFORMANCE MAPE_FORECAST usnetelec 0.013 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'usnetelec' 2.707263231277466 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['usnetelec']' 2.947091579437256 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1949 TimeMax=1988 TimeDelta=1 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='usnetelec' Length=55 Min=296.1 Max=3858.5 Mean=1972.06 StdDev=1119.355557062756 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_usnetelec' Min=296.1 Max=3858.5 Mean=1972.06 StdDev=1119.355557062756 @@ -30,24 +39,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_usnetelec_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0218 MAPE_Forecast=0.0254 MAPE_Test=0.035 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0217 SMAPE_Forecast=0.0255 SMAPE_Test=0.0345 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3604 MASE_Forecast=1.1188 MASE_Test=2.0214 -INFO:pyaf.std:MODEL_L1 L1_Fit=23.272334556948806 L1_Forecast=81.38946316296332 L1_Test=133.3420030433183 -INFO:pyaf.std:MODEL_L2 L2_Fit=29.203836234960864 L2_Forecast=93.32726337765848 L2_Test=152.04476042279035 +INFO:pyaf.std:MODEL_L1 L1_Fit=23.27233455694893 L1_Forecast=81.38946316296291 L1_Test=133.34200304331853 +INFO:pyaf.std:MODEL_L2 L2_Fit=29.203836234960836 L2_Forecast=93.32726337765865 L2_Test=152.04476042278927 INFO:pyaf.std:MODEL_COMPLEXITY 10 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1425.135 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _usnetelec_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_COEFF 1 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag1 1.5703263921377677 -INFO:pyaf.std:AR_MODEL_COEFF 2 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.8831504235369863 -INFO:pyaf.std:AR_MODEL_COEFF 3 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag3 0.6165874457902463 -INFO:pyaf.std:AR_MODEL_COEFF 4 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag7 0.4354090268980063 -INFO:pyaf.std:AR_MODEL_COEFF 5 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag10 0.30487724947887396 -INFO:pyaf.std:AR_MODEL_COEFF 6 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.29438627440621523 -INFO:pyaf.std:AR_MODEL_COEFF 7 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.2643236272012017 -INFO:pyaf.std:AR_MODEL_COEFF 8 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.22439990675060054 -INFO:pyaf.std:AR_MODEL_COEFF 9 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.1965576912912429 -INFO:pyaf.std:AR_MODEL_COEFF 10 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.055822638389784296 +INFO:pyaf.std:AR_MODEL_COEFF 2 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.8831504235369891 +INFO:pyaf.std:AR_MODEL_COEFF 3 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag3 0.6165874457902509 +INFO:pyaf.std:AR_MODEL_COEFF 4 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag7 0.4354090268980028 +INFO:pyaf.std:AR_MODEL_COEFF 5 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag10 0.30487724947887695 +INFO:pyaf.std:AR_MODEL_COEFF 6 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.2943862744062207 +INFO:pyaf.std:AR_MODEL_COEFF 7 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.2643236272011976 +INFO:pyaf.std:AR_MODEL_COEFF 8 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.22439990675060184 +INFO:pyaf.std:AR_MODEL_COEFF 9 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.19655769129124712 +INFO:pyaf.std:AR_MODEL_COEFF 10 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.05582263838977994 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'usnetelec' PERFORMANCE MAPE_FORECAST usnetelec 0.0254 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'usnetelec' 3.184819221496582 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['usnetelec']' 3.8193345069885254 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1949 TimeMax=1985 TimeDelta=1 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='usnetelec' Length=55 Min=296.1 Max=3858.5 Mean=1972.06 StdDev=1119.355557062756 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_usnetelec' Min=296.1 Max=3858.5 Mean=1972.06 StdDev=1119.355557062756 @@ -59,23 +77,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_usnetelec_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0221 MAPE_Forecast=0.0292 MAPE_Test=0.0269 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.022 SMAPE_Forecast=0.03 SMAPE_Test=0.027 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3554 MASE_Forecast=0.9049 MASE_Test=1.2626 -INFO:pyaf.std:MODEL_L1 L1_Fit=22.550551563484145 L1_Forecast=86.77156013355011 L1_Test=100.25164281576701 -INFO:pyaf.std:MODEL_L2 L2_Fit=28.79765105925134 L2_Forecast=113.70170986023749 L2_Test=110.9202187938972 +INFO:pyaf.std:MODEL_L1 L1_Fit=22.55055156348401 L1_Forecast=86.77156013354879 L1_Test=100.25164281576576 +INFO:pyaf.std:MODEL_L2 L2_Fit=28.797651059251333 L2_Forecast=113.7017098602357 L2_Test=110.92021879389613 INFO:pyaf.std:MODEL_COMPLEXITY 9 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1330.6 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _usnetelec_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag1 1.4784549975293635 -INFO:pyaf.std:AR_MODEL_COEFF 2 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.8657777279232222 -INFO:pyaf.std:AR_MODEL_COEFF 3 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag3 0.5869673968600042 -INFO:pyaf.std:AR_MODEL_COEFF 4 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag7 0.534814688845805 -INFO:pyaf.std:AR_MODEL_COEFF 5 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.35874760349096724 -INFO:pyaf.std:AR_MODEL_COEFF 6 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.26807474093690664 -INFO:pyaf.std:AR_MODEL_COEFF 7 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag5 0.22230234499108642 -INFO:pyaf.std:AR_MODEL_COEFF 8 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.21657293266086663 -INFO:pyaf.std:AR_MODEL_COEFF 9 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.14324140680271746 +INFO:pyaf.std:AR_MODEL_COEFF 1 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag1 1.4784549975293637 +INFO:pyaf.std:AR_MODEL_COEFF 2 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.865777727923219 +INFO:pyaf.std:AR_MODEL_COEFF 3 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag3 0.5869673968599975 +INFO:pyaf.std:AR_MODEL_COEFF 4 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag7 0.5348146888457969 +INFO:pyaf.std:AR_MODEL_COEFF 5 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.3587476034909617 +INFO:pyaf.std:AR_MODEL_COEFF 6 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.2680747409368953 +INFO:pyaf.std:AR_MODEL_COEFF 7 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag5 0.22230234499107387 +INFO:pyaf.std:AR_MODEL_COEFF 8 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.21657293266085595 +INFO:pyaf.std:AR_MODEL_COEFF 9 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.14324140680271946 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'usnetelec' PERFORMANCE MAPE_FORECAST usnetelec 0.0292 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'usnetelec' 4.078610420227051 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['usnetelec']' 3.9584743976593018 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1949 TimeMax=2003 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='usnetelec' Length=55 Min=296.1 Max=3858.5 Mean=1972.06 StdDev=1119.355557062756 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_usnetelec' Min=296.1 Max=3858.5 Mean=1972.06 StdDev=1119.355557062756 @@ -87,19 +114,28 @@ INFO:pyaf.std:AUTOREG_DETAIL '_usnetelec_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0204 MAPE_Forecast=0.0204 MAPE_Test=0.0204 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0202 SMAPE_Forecast=0.0202 SMAPE_Test=0.0202 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4375 MASE_Forecast=0.4375 MASE_Test=0.4375 -INFO:pyaf.std:MODEL_L1 L1_Fit=30.875553307613824 L1_Forecast=30.875553307613824 L1_Test=30.875553307613824 -INFO:pyaf.std:MODEL_L2 L2_Fit=41.28204332769424 L2_Forecast=41.28204332769424 L2_Test=41.28204332769424 +INFO:pyaf.std:MODEL_L1 L1_Fit=30.875553307613956 L1_Forecast=30.875553307613956 L1_Test=30.875553307613956 +INFO:pyaf.std:MODEL_L2 L2_Fit=41.282043327694275 L2_Forecast=41.282043327694275 L2_Test=41.282043327694275 INFO:pyaf.std:MODEL_COMPLEXITY 13 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1972.06 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _usnetelec_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag1 1.331061585361157 -INFO:pyaf.std:AR_MODEL_COEFF 2 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag11 0.904680564059076 -INFO:pyaf.std:AR_MODEL_COEFF 3 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.8015727042472642 -INFO:pyaf.std:AR_MODEL_COEFF 4 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.7575792956206827 -INFO:pyaf.std:AR_MODEL_COEFF 5 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag4 0.7533458911950704 -INFO:pyaf.std:AR_MODEL_COEFF 6 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.6922492428119983 -INFO:pyaf.std:AR_MODEL_COEFF 7 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag9 0.5963063826033083 -INFO:pyaf.std:AR_MODEL_COEFF 8 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag6 0.32824811835832934 -INFO:pyaf.std:AR_MODEL_COEFF 9 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.32455768564064325 -INFO:pyaf.std:AR_MODEL_COEFF 10 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.28723561828666566 +INFO:pyaf.std:AR_MODEL_COEFF 1 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag1 1.3310615853611574 +INFO:pyaf.std:AR_MODEL_COEFF 2 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag11 0.9046805640590776 +INFO:pyaf.std:AR_MODEL_COEFF 3 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.8015727042472662 +INFO:pyaf.std:AR_MODEL_COEFF 4 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.7575792956206832 +INFO:pyaf.std:AR_MODEL_COEFF 5 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag4 0.7533458911950702 +INFO:pyaf.std:AR_MODEL_COEFF 6 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.6922492428119963 +INFO:pyaf.std:AR_MODEL_COEFF 7 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag9 0.5963063826033115 +INFO:pyaf.std:AR_MODEL_COEFF 8 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag6 0.3282481183583267 +INFO:pyaf.std:AR_MODEL_COEFF 9 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.32455768564064336 +INFO:pyaf.std:AR_MODEL_COEFF 10 _usnetelec_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.28723561828666627 INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST usnetelec 0.0204 diff --git a/tests/references/expsmooth_expsmooth_dataset_utility.log b/tests/references/expsmooth_expsmooth_dataset_utility.log index c0b213b27..6d34cd779 100644 --- a/tests/references/expsmooth_expsmooth_dataset_utility.log +++ b/tests/references/expsmooth_expsmooth_dataset_utility.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'utility' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'utility' 5.978131055831909 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['utility']' 6.416895389556885 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=101.666666666667 TimeDelta=0.0416666666666668 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='utility' Length=3024 Min=3798 Max=14940 Mean=8352.436507936507 StdDev=2433.02565296368 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_utility' Min=3798 Max=14940 Mean=8352.436507936507 StdDev=2433.02565296368 @@ -11,24 +11,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_utility_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0197 MAPE_Forecast=0.0284 MAPE_Test=0.024 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0197 SMAPE_Forecast=0.0282 SMAPE_Test=0.0234 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4395 MASE_Forecast=0.4747 MASE_Test=0.1524 -INFO:pyaf.std:MODEL_L1 L1_Fit=169.80619374708974 L1_Forecast=175.22067846349793 L1_Test=145.25986039212148 -INFO:pyaf.std:MODEL_L2 L2_Fit=240.10140371996988 L2_Forecast=298.9411425163253 L2_Test=199.95564134179838 +INFO:pyaf.std:MODEL_L1 L1_Fit=169.80619374709 L1_Forecast=175.2206784634984 L1_Test=145.2598603921274 +INFO:pyaf.std:MODEL_L2 L2_Fit=240.10140371996988 L2_Forecast=298.9411425163254 L2_Test=199.9556413418011 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 8855.787753413322 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _utility_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _utility_ConstantTrend_residue_zeroCycle_residue_Lag1 1.4384930042375657 -INFO:pyaf.std:AR_MODEL_COEFF 2 _utility_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.610881563625845 -INFO:pyaf.std:AR_MODEL_COEFF 3 _utility_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.5934584381625811 -INFO:pyaf.std:AR_MODEL_COEFF 4 _utility_ConstantTrend_residue_zeroCycle_residue_Lag24 0.4045200550511131 -INFO:pyaf.std:AR_MODEL_COEFF 5 _utility_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.19669092247250386 -INFO:pyaf.std:AR_MODEL_COEFF 6 _utility_ConstantTrend_residue_zeroCycle_residue_Lag3 0.18227570198236348 -INFO:pyaf.std:AR_MODEL_COEFF 7 _utility_ConstantTrend_residue_zeroCycle_residue_Lag50 0.180109576916608 -INFO:pyaf.std:AR_MODEL_COEFF 8 _utility_ConstantTrend_residue_zeroCycle_residue_Lag23 0.16573035971791153 -INFO:pyaf.std:AR_MODEL_COEFF 9 _utility_ConstantTrend_residue_zeroCycle_residue_Lag27 0.15300560753638873 -INFO:pyaf.std:AR_MODEL_COEFF 10 _utility_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.13388576910930117 +INFO:pyaf.std:AR_MODEL_COEFF 1 _utility_ConstantTrend_residue_zeroCycle_residue_Lag1 1.4384930042375639 +INFO:pyaf.std:AR_MODEL_COEFF 2 _utility_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.6108815636258464 +INFO:pyaf.std:AR_MODEL_COEFF 3 _utility_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.5934584381625826 +INFO:pyaf.std:AR_MODEL_COEFF 4 _utility_ConstantTrend_residue_zeroCycle_residue_Lag24 0.40452005505111166 +INFO:pyaf.std:AR_MODEL_COEFF 5 _utility_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.19669092247250392 +INFO:pyaf.std:AR_MODEL_COEFF 6 _utility_ConstantTrend_residue_zeroCycle_residue_Lag3 0.18227570198236134 +INFO:pyaf.std:AR_MODEL_COEFF 7 _utility_ConstantTrend_residue_zeroCycle_residue_Lag50 0.18010957691660695 +INFO:pyaf.std:AR_MODEL_COEFF 8 _utility_ConstantTrend_residue_zeroCycle_residue_Lag23 0.16573035971791314 +INFO:pyaf.std:AR_MODEL_COEFF 9 _utility_ConstantTrend_residue_zeroCycle_residue_Lag27 0.15300560753638998 +INFO:pyaf.std:AR_MODEL_COEFF 10 _utility_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.13388576910929895 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'utility' PERFORMANCE MAPE_FORECAST utility 0.0284 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'utility' 7.067402362823486 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['utility']' 6.751778602600098 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=101.625 TimeDelta=0.041666666666666664 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='utility' Length=3024 Min=3798 Max=14940 Mean=8352.436507936507 StdDev=2433.02565296368 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_utility' Min=3798 Max=14940 Mean=8352.436507936507 StdDev=2433.02565296368 @@ -40,24 +49,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_utility_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0197 MAPE_Forecast=0.0284 MAPE_Test=0.0317 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0197 SMAPE_Forecast=0.0283 SMAPE_Test=0.0311 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4394 MASE_Forecast=0.4742 MASE_Test=0.3114 -INFO:pyaf.std:MODEL_L1 L1_Fit=169.70631375116614 L1_Forecast=175.45243255956768 L1_Test=204.57395104148077 -INFO:pyaf.std:MODEL_L2 L2_Fit=239.9243556609358 L2_Forecast=299.466944147702 L2_Test=246.59561925374152 +INFO:pyaf.std:MODEL_L1 L1_Fit=169.70631375116605 L1_Forecast=175.45243255956754 L1_Test=204.5739510414794 +INFO:pyaf.std:MODEL_L2 L2_Fit=239.9243556609358 L2_Forecast=299.4669441477022 L2_Test=246.59561925374132 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 8856.436672185431 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _utility_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _utility_ConstantTrend_residue_zeroCycle_residue_Lag1 1.4393002252196454 +INFO:pyaf.std:AR_MODEL_COEFF 1 _utility_ConstantTrend_residue_zeroCycle_residue_Lag1 1.4393002252196463 INFO:pyaf.std:AR_MODEL_COEFF 2 _utility_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.6118496934298435 -INFO:pyaf.std:AR_MODEL_COEFF 3 _utility_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.5934006826907925 -INFO:pyaf.std:AR_MODEL_COEFF 4 _utility_ConstantTrend_residue_zeroCycle_residue_Lag24 0.4035715764254455 -INFO:pyaf.std:AR_MODEL_COEFF 5 _utility_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.19487266457423616 -INFO:pyaf.std:AR_MODEL_COEFF 6 _utility_ConstantTrend_residue_zeroCycle_residue_Lag3 0.18148286032415334 -INFO:pyaf.std:AR_MODEL_COEFF 7 _utility_ConstantTrend_residue_zeroCycle_residue_Lag50 0.17794312007900728 -INFO:pyaf.std:AR_MODEL_COEFF 8 _utility_ConstantTrend_residue_zeroCycle_residue_Lag23 0.1649399979945149 -INFO:pyaf.std:AR_MODEL_COEFF 9 _utility_ConstantTrend_residue_zeroCycle_residue_Lag27 0.15397464382143897 -INFO:pyaf.std:AR_MODEL_COEFF 10 _utility_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.13197492780241787 +INFO:pyaf.std:AR_MODEL_COEFF 3 _utility_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.5934006826907955 +INFO:pyaf.std:AR_MODEL_COEFF 4 _utility_ConstantTrend_residue_zeroCycle_residue_Lag24 0.4035715764254483 +INFO:pyaf.std:AR_MODEL_COEFF 5 _utility_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.19487266457423816 +INFO:pyaf.std:AR_MODEL_COEFF 6 _utility_ConstantTrend_residue_zeroCycle_residue_Lag3 0.18148286032415467 +INFO:pyaf.std:AR_MODEL_COEFF 7 _utility_ConstantTrend_residue_zeroCycle_residue_Lag50 0.17794312007900953 +INFO:pyaf.std:AR_MODEL_COEFF 8 _utility_ConstantTrend_residue_zeroCycle_residue_Lag23 0.16493999799451584 +INFO:pyaf.std:AR_MODEL_COEFF 9 _utility_ConstantTrend_residue_zeroCycle_residue_Lag27 0.15397464382144085 +INFO:pyaf.std:AR_MODEL_COEFF 10 _utility_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.13197492780241782 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'utility' PERFORMANCE MAPE_FORECAST utility 0.0284 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'utility' 7.18709397315979 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['utility']' 7.231292009353638 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=101.458333333333 TimeDelta=0.04166666666666653 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='utility' Length=3024 Min=3798 Max=14940 Mean=8352.436507936507 StdDev=2433.02565296368 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_utility' Min=3798 Max=14940 Mean=8352.436507936507 StdDev=2433.02565296368 @@ -69,24 +87,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_utility_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0197 MAPE_Forecast=0.0284 MAPE_Test=0.0222 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0197 SMAPE_Forecast=0.0283 SMAPE_Test=0.0218 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4392 MASE_Forecast=0.4751 MASE_Test=0.3304 -INFO:pyaf.std:MODEL_L1 L1_Fit=169.7540624545814 L1_Forecast=175.93829012334734 L1_Test=150.98115758497363 -INFO:pyaf.std:MODEL_L2 L2_Fit=240.00629293478497 L2_Forecast=299.5784112034111 L2_Test=206.64404989731298 +INFO:pyaf.std:MODEL_L1 L1_Fit=169.75406245458132 L1_Forecast=175.93829012334683 L1_Test=150.98115758497408 +INFO:pyaf.std:MODEL_L2 L2_Fit=240.00629293478494 L2_Forecast=299.57841120341084 L2_Test=206.6440498973147 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 8857.114427860697 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _utility_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_COEFF 1 _utility_ConstantTrend_residue_zeroCycle_residue_Lag1 1.4393386613279295 -INFO:pyaf.std:AR_MODEL_COEFF 2 _utility_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.6109682740656156 -INFO:pyaf.std:AR_MODEL_COEFF 3 _utility_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.5938846288392111 -INFO:pyaf.std:AR_MODEL_COEFF 4 _utility_ConstantTrend_residue_zeroCycle_residue_Lag24 0.4035760490747463 -INFO:pyaf.std:AR_MODEL_COEFF 5 _utility_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.19478498397580082 -INFO:pyaf.std:AR_MODEL_COEFF 6 _utility_ConstantTrend_residue_zeroCycle_residue_Lag3 0.17985211949591445 -INFO:pyaf.std:AR_MODEL_COEFF 7 _utility_ConstantTrend_residue_zeroCycle_residue_Lag50 0.1776697628914068 -INFO:pyaf.std:AR_MODEL_COEFF 8 _utility_ConstantTrend_residue_zeroCycle_residue_Lag23 0.16536308356892432 -INFO:pyaf.std:AR_MODEL_COEFF 9 _utility_ConstantTrend_residue_zeroCycle_residue_Lag27 0.15527538620585785 -INFO:pyaf.std:AR_MODEL_COEFF 10 _utility_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.1313020306576907 +INFO:pyaf.std:AR_MODEL_COEFF 2 _utility_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.6109682740656137 +INFO:pyaf.std:AR_MODEL_COEFF 3 _utility_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.5938846288392098 +INFO:pyaf.std:AR_MODEL_COEFF 4 _utility_ConstantTrend_residue_zeroCycle_residue_Lag24 0.40357604907474587 +INFO:pyaf.std:AR_MODEL_COEFF 5 _utility_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.19478498397580257 +INFO:pyaf.std:AR_MODEL_COEFF 6 _utility_ConstantTrend_residue_zeroCycle_residue_Lag3 0.1798521194959132 +INFO:pyaf.std:AR_MODEL_COEFF 7 _utility_ConstantTrend_residue_zeroCycle_residue_Lag50 0.17766976289140785 +INFO:pyaf.std:AR_MODEL_COEFF 8 _utility_ConstantTrend_residue_zeroCycle_residue_Lag23 0.16536308356892584 +INFO:pyaf.std:AR_MODEL_COEFF 9 _utility_ConstantTrend_residue_zeroCycle_residue_Lag27 0.1552753862058559 +INFO:pyaf.std:AR_MODEL_COEFF 10 _utility_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.13130203065769136 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'utility' PERFORMANCE MAPE_FORECAST utility 0.0284 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'utility' 9.962433815002441 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['utility']' 7.927987337112427 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=101.333333333333 TimeDelta=0.041666666666666526 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='utility' Length=3024 Min=3798 Max=14940 Mean=8352.436507936507 StdDev=2433.02565296368 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_utility' Min=3798 Max=14940 Mean=8352.436507936507 StdDev=2433.02565296368 @@ -98,19 +125,28 @@ INFO:pyaf.std:AUTOREG_DETAIL '_utility_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0197 MAPE_Forecast=0.0285 MAPE_Test=0.0211 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0197 SMAPE_Forecast=0.0283 SMAPE_Test=0.0209 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4388 MASE_Forecast=0.4745 MASE_Test=0.4269 -INFO:pyaf.std:MODEL_L1 L1_Fit=169.73940355790165 L1_Forecast=176.01771257631478 L1_Test=153.692558468979 -INFO:pyaf.std:MODEL_L2 L2_Fit=240.05104599150513 L2_Forecast=299.8955016735878 L2_Test=197.62047149938326 +INFO:pyaf.std:MODEL_L1 L1_Fit=169.73940355790177 L1_Forecast=176.01771257631503 L1_Test=153.69255846897568 +INFO:pyaf.std:MODEL_L2 L2_Fit=240.05104599150522 L2_Forecast=299.895501673588 L2_Test=197.62047149938076 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 8857.189705271898 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _utility_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _utility_ConstantTrend_residue_zeroCycle_residue_Lag1 1.4395226594296093 -INFO:pyaf.std:AR_MODEL_COEFF 2 _utility_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.6112474075912486 -INFO:pyaf.std:AR_MODEL_COEFF 3 _utility_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.5943816556749982 -INFO:pyaf.std:AR_MODEL_COEFF 4 _utility_ConstantTrend_residue_zeroCycle_residue_Lag24 0.40441890646242273 -INFO:pyaf.std:AR_MODEL_COEFF 5 _utility_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.19516511778569853 -INFO:pyaf.std:AR_MODEL_COEFF 6 _utility_ConstantTrend_residue_zeroCycle_residue_Lag3 0.1806373079191128 -INFO:pyaf.std:AR_MODEL_COEFF 7 _utility_ConstantTrend_residue_zeroCycle_residue_Lag50 0.1780617524773633 -INFO:pyaf.std:AR_MODEL_COEFF 8 _utility_ConstantTrend_residue_zeroCycle_residue_Lag23 0.16514248347275223 -INFO:pyaf.std:AR_MODEL_COEFF 9 _utility_ConstantTrend_residue_zeroCycle_residue_Lag27 0.15604051298672528 -INFO:pyaf.std:AR_MODEL_COEFF 10 _utility_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.12983337356616295 +INFO:pyaf.std:AR_MODEL_COEFF 1 _utility_ConstantTrend_residue_zeroCycle_residue_Lag1 1.4395226594296116 +INFO:pyaf.std:AR_MODEL_COEFF 2 _utility_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.6112474075912517 +INFO:pyaf.std:AR_MODEL_COEFF 3 _utility_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.5943816556750003 +INFO:pyaf.std:AR_MODEL_COEFF 4 _utility_ConstantTrend_residue_zeroCycle_residue_Lag24 0.4044189064624206 +INFO:pyaf.std:AR_MODEL_COEFF 5 _utility_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.19516511778569967 +INFO:pyaf.std:AR_MODEL_COEFF 6 _utility_ConstantTrend_residue_zeroCycle_residue_Lag3 0.18063730791911659 +INFO:pyaf.std:AR_MODEL_COEFF 7 _utility_ConstantTrend_residue_zeroCycle_residue_Lag50 0.17806175247736336 +INFO:pyaf.std:AR_MODEL_COEFF 8 _utility_ConstantTrend_residue_zeroCycle_residue_Lag23 0.16514248347275454 +INFO:pyaf.std:AR_MODEL_COEFF 9 _utility_ConstantTrend_residue_zeroCycle_residue_Lag27 0.15604051298672428 +INFO:pyaf.std:AR_MODEL_COEFF 10 _utility_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.12983337356616423 INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST utility 0.0285 diff --git a/tests/references/expsmooth_expsmooth_dataset_vehicles.log b/tests/references/expsmooth_expsmooth_dataset_vehicles.log index 88561a812..59b0c7a82 100644 --- a/tests/references/expsmooth_expsmooth_dataset_vehicles.log +++ b/tests/references/expsmooth_expsmooth_dataset_vehicles.log @@ -1,96 +1,132 @@ INFO:pyaf.std:START_TRAINING 'vehicles' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'vehicles' 5.284151792526245 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['vehicles']' 4.981847524642944 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=57.1666666666667 TimeDelta=0.04166666666666669 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='vehicles' Length=1689 Min=154 Max=5549 Mean=2060.4179988158676 StdDev=1339.1492330503788 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_vehicles' Min=154 Max=5549 Mean=2060.4179988158676 StdDev=1339.1492330503788 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_vehicles_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [ConstantTrend + Cycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_vehicles_ConstantTrend_residue_zeroCycle_residue_AR(64)' [ConstantTrend + NoCycle + AR] INFO:pyaf.std:TREND_DETAIL '_vehicles_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_vehicles_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_vehicles_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1493 MAPE_Forecast=0.1791 MAPE_Test=0.0426 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1408 SMAPE_Forecast=0.1736 SMAPE_Test=0.0416 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3842 MASE_Forecast=0.4306 MASE_Test=0.1963 -INFO:pyaf.std:MODEL_L1 L1_Fit=171.9913040157834 L1_Forecast=195.07187357491625 L1_Test=208.24818674659446 -INFO:pyaf.std:MODEL_L2 L2_Fit=259.8197301075505 L2_Forecast=281.9064400488972 L2_Test=244.18674332269455 -INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:CYCLE_DETAIL '_vehicles_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_vehicles_ConstantTrend_residue_zeroCycle_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1841 MAPE_Forecast=0.1981 MAPE_Test=0.1187 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1623 SMAPE_Forecast=0.1785 SMAPE_Test=0.1274 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4592 MASE_Forecast=0.5055 MASE_Test=0.4824 +INFO:pyaf.std:MODEL_L1 L1_Fit=205.54886100351175 L1_Forecast=229.00281872770097 L1_Test=511.7777627037017 +INFO:pyaf.std:MODEL_L2 L2_Fit=320.4412106849622 L2_Forecast=352.3076395071518 L2_Test=565.7302836233365 +INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 2062.677538917717 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _vehicles_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 1.2075558869215854 -INFO:pyaf.std:AR_MODEL_COEFF 2 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag2 -0.5384491957453102 -INFO:pyaf.std:AR_MODEL_COEFF 3 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag23 0.21594905358871774 -INFO:pyaf.std:AR_MODEL_COEFF 4 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag50 0.19936163085524522 -INFO:pyaf.std:AR_MODEL_COEFF 5 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag61 -0.1870548857814549 -INFO:pyaf.std:AR_MODEL_COEFF 6 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag62 0.1535370049489582 -INFO:pyaf.std:AR_MODEL_COEFF 7 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag12 -0.15067326913858797 -INFO:pyaf.std:AR_MODEL_COEFF 8 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag51 -0.14004031588786986 -INFO:pyaf.std:AR_MODEL_COEFF 9 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag3 0.1290236008485624 -INFO:pyaf.std:AR_MODEL_COEFF 10 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag13 0.11900519838636094 +INFO:pyaf.std:AR_MODEL_COEFF 1 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag1 1.3016572791972298 +INFO:pyaf.std:AR_MODEL_COEFF 2 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.6774669483377798 +INFO:pyaf.std:AR_MODEL_COEFF 3 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.4002359365947258 +INFO:pyaf.std:AR_MODEL_COEFF 4 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.33612823218720944 +INFO:pyaf.std:AR_MODEL_COEFF 5 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag50 0.27274268025433523 +INFO:pyaf.std:AR_MODEL_COEFF 6 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag13 0.2676398961057284 +INFO:pyaf.std:AR_MODEL_COEFF 7 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag23 0.2623919873406721 +INFO:pyaf.std:AR_MODEL_COEFF 8 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag48 0.25481113573511793 +INFO:pyaf.std:AR_MODEL_COEFF 9 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.24509175878279058 +INFO:pyaf.std:AR_MODEL_COEFF 10 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag24 0.239883391384454 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'vehicles' -PERFORMANCE MAPE_FORECAST vehicles 0.1791 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'vehicles' 5.670559406280518 +PERFORMANCE MAPE_FORECAST vehicles 0.1981 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['vehicles']' 5.368628978729248 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=57.125 TimeDelta=0.041666666666666664 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='vehicles' Length=1689 Min=154 Max=5549 Mean=2060.4179988158676 StdDev=1339.1492330503788 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_vehicles' Min=154 Max=5549 Mean=2060.4179988158676 StdDev=1339.1492330503788 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_vehicles_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [ConstantTrend + Cycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_vehicles_ConstantTrend_residue_zeroCycle_residue_AR(64)' [ConstantTrend + NoCycle + AR] INFO:pyaf.std:TREND_DETAIL '_vehicles_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_vehicles_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_vehicles_ConstantTrend_residue_bestCycle_byL2_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1493 MAPE_Forecast=0.1798 MAPE_Test=0.081 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1408 SMAPE_Forecast=0.1745 SMAPE_Test=0.0766 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3842 MASE_Forecast=0.4328 MASE_Test=0.0886 -INFO:pyaf.std:MODEL_L1 L1_Fit=172.11968054763997 L1_Forecast=195.35219932599836 L1_Test=140.63801368016271 -INFO:pyaf.std:MODEL_L2 L2_Fit=259.91337477155855 L2_Forecast=282.275858680826 L2_Test=180.21498927834276 -INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:CYCLE_DETAIL '_vehicles_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_vehicles_ConstantTrend_residue_zeroCycle_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1841 MAPE_Forecast=0.1995 MAPE_Test=0.0937 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1622 SMAPE_Forecast=0.1796 SMAPE_Test=0.0969 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4591 MASE_Forecast=0.5086 MASE_Test=0.1779 +INFO:pyaf.std:MODEL_L1 L1_Fit=205.67077824936288 L1_Forecast=229.55700014653252 L1_Test=282.5557553832541 +INFO:pyaf.std:MODEL_L2 L2_Fit=320.5562219991151 L2_Forecast=352.80938880136324 L2_Test=402.23025964792527 +INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 2064.091988130564 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _vehicles_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 1.2076537388223905 -INFO:pyaf.std:AR_MODEL_COEFF 2 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag2 -0.5385569379722134 -INFO:pyaf.std:AR_MODEL_COEFF 3 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag23 0.21603809359830595 -INFO:pyaf.std:AR_MODEL_COEFF 4 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag50 0.1993430916277937 -INFO:pyaf.std:AR_MODEL_COEFF 5 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag61 -0.186978262635416 -INFO:pyaf.std:AR_MODEL_COEFF 6 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag62 0.15347997312140618 -INFO:pyaf.std:AR_MODEL_COEFF 7 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag12 -0.1505851650453851 -INFO:pyaf.std:AR_MODEL_COEFF 8 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag51 -0.14015191752560396 -INFO:pyaf.std:AR_MODEL_COEFF 9 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag3 0.12904209191345528 -INFO:pyaf.std:AR_MODEL_COEFF 10 _vehicles_ConstantTrend_residue_bestCycle_byL2_residue_Lag13 0.1190364844971362 +INFO:pyaf.std:AR_MODEL_COEFF 1 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag1 1.3015371813323817 +INFO:pyaf.std:AR_MODEL_COEFF 2 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.6773475107077778 +INFO:pyaf.std:AR_MODEL_COEFF 3 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.4002163373301517 +INFO:pyaf.std:AR_MODEL_COEFF 4 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.3362405822278062 +INFO:pyaf.std:AR_MODEL_COEFF 5 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag50 0.2728034894480379 +INFO:pyaf.std:AR_MODEL_COEFF 6 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag13 0.26755296002264073 +INFO:pyaf.std:AR_MODEL_COEFF 7 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag23 0.26246106159915006 +INFO:pyaf.std:AR_MODEL_COEFF 8 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag48 0.2548986453958576 +INFO:pyaf.std:AR_MODEL_COEFF 9 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.24511828807186994 +INFO:pyaf.std:AR_MODEL_COEFF 10 _vehicles_ConstantTrend_residue_zeroCycle_residue_Lag24 0.23991640129599895 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'vehicles' -PERFORMANCE MAPE_FORECAST vehicles 0.1798 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'vehicles' 5.5198118686676025 +PERFORMANCE MAPE_FORECAST vehicles 0.1995 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['vehicles']' 4.930326461791992 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=56.9583333333333 TimeDelta=0.041666666666666644 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='vehicles' Length=1689 Min=154 Max=5549 Mean=2060.4179988158676 StdDev=1339.1492330503788 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_vehicles' Min=154 Max=5549 Mean=2060.4179988158676 StdDev=1339.1492330503788 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_vehicles_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [Lag1Trend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_vehicles_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_vehicles_Lag1Trend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_vehicles_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1895 MAPE_Test=0.3308 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1541 SMAPE_Forecast=0.1822 SMAPE_Test=0.4966 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5202 MASE_Forecast=0.5504 MASE_Test=0.2918 -INFO:pyaf.std:MODEL_L1 L1_Fit=233.2410448554422 L1_Forecast=249.13999576091567 L1_Test=209.66071428571428 -INFO:pyaf.std:MODEL_L2 L2_Fit=358.4366899518749 L2_Forecast=368.52108503601346 L2_Test=311.5231896730669 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_vehicles' Min=699 Max=3480046 Mean=1731871.9982238011 StdDev=1018880.8500892685 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' +INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_vehicles_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [Lag1Trend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL 'CumSum_vehicles_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL 'CumSum_vehicles_Lag1Trend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_vehicles_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1705 MAPE_Forecast=0.1898 MAPE_Test=0.2112 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1532 SMAPE_Forecast=0.1723 SMAPE_Test=0.2832 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5023 MASE_Forecast=0.5431 MASE_Test=0.1565 +INFO:pyaf.std:MODEL_L1 L1_Fit=225.24479166666666 L1_Forecast=245.86943620178042 L1_Test=112.4375 +INFO:pyaf.std:MODEL_L2 L2_Fit=414.9845522081859 L2_Forecast=421.4470022050884 L2_Test=144.00922279493074 +INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 699 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_vehicles_Lag1Trend_residue_bestCycle_byMAPE 24 2330.5 {0: 793.0, 1: 395.0, 2: 245.5, 3: 213.0, 4: 211.0, 5: 361.0, 6: 1134.5, 7: 3741.0, 8: 5038.5, 9: 4378.0, 10: 3328.5, 11: 2944.0, 12: 2811.5, 13: 2765.0, 14: 2682.0, 15: 2551.5, 16: 2691.5, 17: 3003.0, 18: 3743.5, 19: 3049.0, 20: 2312.5, 21: 1598.5, 22: 1390.5, 23: 1159.0} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'vehicles' -PERFORMANCE MAPE_FORECAST vehicles 0.1895 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'vehicles' 5.885704278945923 +PERFORMANCE MAPE_FORECAST vehicles 0.1898 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['vehicles']' 6.1584296226501465 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1.0 TimeMax=56.8333333333333 TimeDelta=0.041666666666666644 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='vehicles' Length=1689 Min=154 Max=5549 Mean=2060.4179988158676 StdDev=1339.1492330503788 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_vehicles' Min=154 Max=5549 Mean=2060.4179988158676 StdDev=1339.1492330503788 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_vehicles_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [Lag1Trend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_vehicles_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_vehicles_Lag1Trend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_vehicles_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.165 MAPE_Forecast=0.1899 MAPE_Test=0.2495 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1542 SMAPE_Forecast=0.1827 SMAPE_Test=0.3591 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.52 MASE_Forecast=0.5539 MASE_Test=0.2955 -INFO:pyaf.std:MODEL_L1 L1_Fit=233.39116234250469 L1_Forecast=250.58006338899196 L1_Test=159.90102813852818 -INFO:pyaf.std:MODEL_L2 L2_Fit=358.6856743327195 L2_Forecast=369.55819038257386 L2_Test=257.3155775741901 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_vehicles' Min=699 Max=3480046 Mean=1731871.9982238011 StdDev=1018880.8500892685 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' +INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_vehicles_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [Lag1Trend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL 'CumSum_vehicles_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL 'CumSum_vehicles_Lag1Trend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_vehicles_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1704 MAPE_Forecast=0.1904 MAPE_Test=0.1559 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1529 SMAPE_Forecast=0.1729 SMAPE_Test=0.2044 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5014 MASE_Forecast=0.5465 MASE_Test=0.1588 +INFO:pyaf.std:MODEL_L1 L1_Fit=225.03765846383297 L1_Forecast=247.23214285714286 L1_Test=85.91666666666667 +INFO:pyaf.std:MODEL_L2 L2_Fit=415.211373310598 L2_Forecast=422.38771049379557 L2_Test=119.2701764901855 +INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 699 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_vehicles_Lag1Trend_residue_bestCycle_byMAPE 24 2336.0 {0: 793.0, 1: 395.0, 2: 245.5, 3: 213.0, 4: 211.0, 5: 361.0, 6: 1134.5, 7: 3741.0, 8: 5038.5, 9: 4378.0, 10: 3328.5, 11: 2944.0, 12: 2811.5, 13: 2765.0, 14: 2682.0, 15: 2551.5, 16: 2691.5, 17: 3003.0, 18: 3743.5, 19: 3049.0, 20: 2312.5, 21: 1597.0, 22: 1375.0, 23: 1157.0} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -PERFORMANCE MAPE_FORECAST vehicles 0.1899 +PERFORMANCE MAPE_FORECAST vehicles 0.1904 diff --git a/tests/references/expsmooth_expsmooth_dataset_visitors.log b/tests/references/expsmooth_expsmooth_dataset_visitors.log index e92040ec4..3334ac5fc 100644 --- a/tests/references/expsmooth_expsmooth_dataset_visitors.log +++ b/tests/references/expsmooth_expsmooth_dataset_visitors.log @@ -1,63 +1,81 @@ INFO:pyaf.std:START_TRAINING 'visitors' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'visitors' 4.20222806930542 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['visitors']' 3.111377477645874 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1985.33333333333 TimeMax=2001.08333333333 TimeDelta=0.08333333333333333 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='visitors' Length=240 Min=75.4 Max=593.1 Mean=288.18583333333333 StdDev=115.11710544472625 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_visitors' Min=75.4 Max=593.1 Mean=288.18583333333333 StdDev=115.11710544472625 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_visitors_ConstantTrend_residue_bestCycle_byL2_residue_AR(60)' [ConstantTrend + Cycle + AR] -INFO:pyaf.std:TREND_DETAIL '_visitors_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_visitors_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_visitors_ConstantTrend_residue_bestCycle_byL2_residue_AR(60)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0476 MAPE_Forecast=0.058 MAPE_Test=0.0823 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0471 SMAPE_Forecast=0.057 SMAPE_Test=0.0803 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3067 MASE_Forecast=0.4023 MASE_Test=0.3817 -INFO:pyaf.std:MODEL_L1 L1_Fit=10.08092756072331 L1_Forecast=23.317808879902913 L1_Test=36.33575292867519 -INFO:pyaf.std:MODEL_L2 L2_Fit=13.320553521027735 L2_Forecast=28.395934734904703 L2_Test=37.28736982231412 -INFO:pyaf.std:MODEL_COMPLEXITY 55 +INFO:pyaf.std:BEST_DECOMPOSITION '_visitors_LinearTrend_residue_zeroCycle_residue_AR(60)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_visitors_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_visitors_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_visitors_LinearTrend_residue_zeroCycle_residue_AR(60)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0484 MAPE_Forecast=0.049 MAPE_Test=0.109 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0482 SMAPE_Forecast=0.0476 SMAPE_Test=0.105 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3219 MASE_Forecast=0.3313 MASE_Test=0.5032 +INFO:pyaf.std:MODEL_L1 L1_Fit=10.580619018762086 L1_Forecast=19.20269373091323 L1_Test=47.90683850436858 +INFO:pyaf.std:MODEL_L2 L2_Fit=13.448033155022078 L2_Forecast=25.356935789214884 L2_Test=49.75504595713966 +INFO:pyaf.std:MODEL_COMPLEXITY 63 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (102.1913963106164, array([305.87404948])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _visitors_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 0.5371114779427906 -INFO:pyaf.std:AR_MODEL_COEFF 2 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag24 0.3220993475561859 -INFO:pyaf.std:AR_MODEL_COEFF 3 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag34 -0.2808785027321557 -INFO:pyaf.std:AR_MODEL_COEFF 4 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag25 -0.25960773929313213 -INFO:pyaf.std:AR_MODEL_COEFF 5 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag35 0.25092721625901765 -INFO:pyaf.std:AR_MODEL_COEFF 6 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag12 0.23597504246565992 -INFO:pyaf.std:AR_MODEL_COEFF 7 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag36 0.19205571310987934 -INFO:pyaf.std:AR_MODEL_COEFF 8 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag27 0.18161997397224697 -INFO:pyaf.std:AR_MODEL_COEFF 9 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag2 0.17364270806183277 -INFO:pyaf.std:AR_MODEL_COEFF 10 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag31 -0.17059068390110743 +INFO:pyaf.std:AR_MODEL_COEFF 1 _visitors_LinearTrend_residue_zeroCycle_residue_Lag1 0.5537119625353657 +INFO:pyaf.std:AR_MODEL_COEFF 2 _visitors_LinearTrend_residue_zeroCycle_residue_Lag25 -0.4275940586694503 +INFO:pyaf.std:AR_MODEL_COEFF 3 _visitors_LinearTrend_residue_zeroCycle_residue_Lag24 0.3494084970792042 +INFO:pyaf.std:AR_MODEL_COEFF 4 _visitors_LinearTrend_residue_zeroCycle_residue_Lag12 0.2905769042089222 +INFO:pyaf.std:AR_MODEL_COEFF 5 _visitors_LinearTrend_residue_zeroCycle_residue_Lag59 -0.27507325021833484 +INFO:pyaf.std:AR_MODEL_COEFF 6 _visitors_LinearTrend_residue_zeroCycle_residue_Lag60 0.2573493761015776 +INFO:pyaf.std:AR_MODEL_COEFF 7 _visitors_LinearTrend_residue_zeroCycle_residue_Lag35 0.2425342576819946 +INFO:pyaf.std:AR_MODEL_COEFF 8 _visitors_LinearTrend_residue_zeroCycle_residue_Lag37 -0.23896690498181372 +INFO:pyaf.std:AR_MODEL_COEFF 9 _visitors_LinearTrend_residue_zeroCycle_residue_Lag36 0.2202283626987735 +INFO:pyaf.std:AR_MODEL_COEFF 10 _visitors_LinearTrend_residue_zeroCycle_residue_Lag49 0.20280442265867948 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'visitors' -PERFORMANCE MAPE_FORECAST visitors 0.058 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'visitors' 3.995065927505493 +PERFORMANCE MAPE_FORECAST visitors 0.049 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['visitors']' 2.8990585803985596 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1985.33333333333 TimeMax=2000.91666666667 TimeDelta=0.08333333333336819 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='visitors' Length=240 Min=75.4 Max=593.1 Mean=288.18583333333333 StdDev=115.11710544472625 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_visitors' Min=75.4 Max=593.1 Mean=288.18583333333333 StdDev=115.11710544472625 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_visitors_ConstantTrend_residue_bestCycle_byL2_residue_AR(60)' [ConstantTrend + Cycle + AR] -INFO:pyaf.std:TREND_DETAIL '_visitors_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_visitors_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_visitors_ConstantTrend_residue_bestCycle_byL2_residue_AR(60)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0469 MAPE_Forecast=0.0586 MAPE_Test=0.059 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0465 SMAPE_Forecast=0.0573 SMAPE_Test=0.0577 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3054 MASE_Forecast=0.428 MASE_Test=0.5808 -INFO:pyaf.std:MODEL_L1 L1_Fit=9.892780413986802 L1_Forecast=23.38383459565411 L1_Test=26.622127002213787 -INFO:pyaf.std:MODEL_L2 L2_Fit=13.245905147276034 L2_Forecast=28.445078737726604 L2_Test=29.46936212141488 -INFO:pyaf.std:MODEL_COMPLEXITY 55 +INFO:pyaf.std:BEST_DECOMPOSITION '_visitors_LinearTrend_residue_zeroCycle_residue_AR(60)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_visitors_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_visitors_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_visitors_LinearTrend_residue_zeroCycle_residue_AR(60)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0474 MAPE_Forecast=0.0499 MAPE_Test=0.0755 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0473 SMAPE_Forecast=0.0485 SMAPE_Test=0.0733 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.319 MASE_Forecast=0.3588 MASE_Test=0.737 +INFO:pyaf.std:MODEL_L1 L1_Fit=10.332958086953694 L1_Forecast=19.60398667036227 L1_Test=33.779566863833466 +INFO:pyaf.std:MODEL_L2 L2_Fit=13.273778049966388 L2_Forecast=25.504837789214243 L2_Test=39.66884570228658 +INFO:pyaf.std:MODEL_COMPLEXITY 63 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (102.30463989288126, array([302.07795426])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _visitors_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 0.5241162552382816 -INFO:pyaf.std:AR_MODEL_COEFF 2 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag24 0.30189379694177754 -INFO:pyaf.std:AR_MODEL_COEFF 3 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag34 -0.28975277015853174 -INFO:pyaf.std:AR_MODEL_COEFF 4 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag12 0.24363833452267317 -INFO:pyaf.std:AR_MODEL_COEFF 5 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag25 -0.22860436682529822 -INFO:pyaf.std:AR_MODEL_COEFF 6 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag2 0.20967385485228157 -INFO:pyaf.std:AR_MODEL_COEFF 7 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag35 0.20222342077608327 -INFO:pyaf.std:AR_MODEL_COEFF 8 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag31 -0.16971119469264773 -INFO:pyaf.std:AR_MODEL_COEFF 9 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag28 -0.155497554642761 -INFO:pyaf.std:AR_MODEL_COEFF 10 _visitors_ConstantTrend_residue_bestCycle_byL2_residue_Lag36 0.1517365000440176 +INFO:pyaf.std:AR_MODEL_COEFF 1 _visitors_LinearTrend_residue_zeroCycle_residue_Lag1 0.5313390320601286 +INFO:pyaf.std:AR_MODEL_COEFF 2 _visitors_LinearTrend_residue_zeroCycle_residue_Lag25 -0.39871874544798885 +INFO:pyaf.std:AR_MODEL_COEFF 3 _visitors_LinearTrend_residue_zeroCycle_residue_Lag24 0.32683958622624 +INFO:pyaf.std:AR_MODEL_COEFF 4 _visitors_LinearTrend_residue_zeroCycle_residue_Lag12 0.3159371681862824 +INFO:pyaf.std:AR_MODEL_COEFF 5 _visitors_LinearTrend_residue_zeroCycle_residue_Lag60 0.3039348649480089 +INFO:pyaf.std:AR_MODEL_COEFF 6 _visitors_LinearTrend_residue_zeroCycle_residue_Lag59 -0.2948779878394097 +INFO:pyaf.std:AR_MODEL_COEFF 7 _visitors_LinearTrend_residue_zeroCycle_residue_Lag35 0.21417415209202062 +INFO:pyaf.std:AR_MODEL_COEFF 8 _visitors_LinearTrend_residue_zeroCycle_residue_Lag49 0.1869802684106658 +INFO:pyaf.std:AR_MODEL_COEFF 9 _visitors_LinearTrend_residue_zeroCycle_residue_Lag37 -0.18327218174713608 +INFO:pyaf.std:AR_MODEL_COEFF 10 _visitors_LinearTrend_residue_zeroCycle_residue_Lag8 0.16380934635129527 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'visitors' -PERFORMANCE MAPE_FORECAST visitors 0.0586 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'visitors' 4.414485931396484 +PERFORMANCE MAPE_FORECAST visitors 0.0499 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['visitors']' 4.190096139907837 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1985.33333333333 TimeMax=2000.66666666667 TimeDelta=0.08333333333336876 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='visitors' Length=240 Min=75.4 Max=593.1 Mean=288.18583333333333 StdDev=115.11710544472625 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_visitors' Min=75.4 Max=593.1 Mean=288.18583333333333 StdDev=115.11710544472625 @@ -69,24 +87,33 @@ INFO:pyaf.std:AUTOREG_DETAIL '_visitors_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0479 MAPE_Forecast=0.0603 MAPE_Test=0.0544 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0476 SMAPE_Forecast=0.0583 SMAPE_Test=0.0535 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3093 MASE_Forecast=0.4178 MASE_Test=0.3711 -INFO:pyaf.std:MODEL_L1 L1_Fit=9.918185451187133 L1_Forecast=23.94759552277983 L1_Test=24.687708156624147 -INFO:pyaf.std:MODEL_L2 L2_Fit=12.674742992050941 L2_Forecast=30.40883281594605 L2_Test=27.388255337643095 +INFO:pyaf.std:MODEL_L1 L1_Fit=9.918185451187126 L1_Forecast=23.947595522779825 L1_Test=24.68770815662412 +INFO:pyaf.std:MODEL_L2 L2_Fit=12.674742992050941 L2_Forecast=30.40883281594603 L2_Test=27.38825533764305 INFO:pyaf.std:MODEL_COMPLEXITY 46 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 249.63405405405408 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _visitors_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5342267881675178 -INFO:pyaf.std:AR_MODEL_COEFF 2 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag24 0.42941136215775777 -INFO:pyaf.std:AR_MODEL_COEFF 3 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag12 0.31804070944357676 -INFO:pyaf.std:AR_MODEL_COEFF 4 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.30734114635370174 -INFO:pyaf.std:AR_MODEL_COEFF 5 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag34 -0.29883277949473736 -INFO:pyaf.std:AR_MODEL_COEFF 6 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag37 -0.24599893005814094 -INFO:pyaf.std:AR_MODEL_COEFF 7 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag59 -0.23103573079832068 -INFO:pyaf.std:AR_MODEL_COEFF 8 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag36 0.2161825440067451 -INFO:pyaf.std:AR_MODEL_COEFF 9 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag35 0.2161196013397787 -INFO:pyaf.std:AR_MODEL_COEFF 10 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag50 0.21414996852520127 +INFO:pyaf.std:AR_MODEL_COEFF 1 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag1 0.534226788167518 +INFO:pyaf.std:AR_MODEL_COEFF 2 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag24 0.4294113621577572 +INFO:pyaf.std:AR_MODEL_COEFF 3 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag12 0.3180407094435764 +INFO:pyaf.std:AR_MODEL_COEFF 4 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.30734114635370063 +INFO:pyaf.std:AR_MODEL_COEFF 5 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag34 -0.29883277949473724 +INFO:pyaf.std:AR_MODEL_COEFF 6 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag37 -0.24599893005814133 +INFO:pyaf.std:AR_MODEL_COEFF 7 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag59 -0.2310357307983193 +INFO:pyaf.std:AR_MODEL_COEFF 8 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag36 0.2161825440067457 +INFO:pyaf.std:AR_MODEL_COEFF 9 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag35 0.21611960133977776 +INFO:pyaf.std:AR_MODEL_COEFF 10 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag50 0.21414996852520157 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'visitors' PERFORMANCE MAPE_FORECAST visitors 0.0603 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'visitors' 5.041550636291504 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['visitors']' 5.7658209800720215 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1985.33333333333 TimeMax=2000.41666666667 TimeDelta=0.08333333333336934 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='visitors' Length=240 Min=75.4 Max=593.1 Mean=288.18583333333333 StdDev=115.11710544472625 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_visitors' Min=75.4 Max=593.1 Mean=288.18583333333333 StdDev=115.11710544472625 @@ -98,19 +125,28 @@ INFO:pyaf.std:AUTOREG_DETAIL '_visitors_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0481 MAPE_Forecast=0.0599 MAPE_Test=0.0482 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0478 SMAPE_Forecast=0.0583 SMAPE_Test=0.0475 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3104 MASE_Forecast=0.4317 MASE_Test=0.3497 -INFO:pyaf.std:MODEL_L1 L1_Fit=9.831714423116056 L1_Forecast=23.86836917391137 L1_Test=21.387784633596723 -INFO:pyaf.std:MODEL_L2 L2_Fit=12.523346245652963 L2_Forecast=29.698925996347096 L2_Test=26.392335246321416 +INFO:pyaf.std:MODEL_L1 L1_Fit=9.831714423116058 L1_Forecast=23.868369173911358 L1_Test=21.38778463359675 +INFO:pyaf.std:MODEL_L2 L2_Fit=12.523346245652965 L2_Forecast=29.698925996347114 L2_Test=26.39233524632145 INFO:pyaf.std:MODEL_COMPLEXITY 45 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 247.1285714285714 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _visitors_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5488352336842581 -INFO:pyaf.std:AR_MODEL_COEFF 2 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag24 0.4466000704836226 -INFO:pyaf.std:AR_MODEL_COEFF 3 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.3465818791779567 -INFO:pyaf.std:AR_MODEL_COEFF 4 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag12 0.34642397982059536 -INFO:pyaf.std:AR_MODEL_COEFF 5 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag34 -0.32880592414929466 +INFO:pyaf.std:AR_MODEL_COEFF 1 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5488352336842584 +INFO:pyaf.std:AR_MODEL_COEFF 2 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag24 0.446600070483623 +INFO:pyaf.std:AR_MODEL_COEFF 3 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.34658187917795713 +INFO:pyaf.std:AR_MODEL_COEFF 4 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag12 0.34642397982059514 +INFO:pyaf.std:AR_MODEL_COEFF 5 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag34 -0.32880592414929444 INFO:pyaf.std:AR_MODEL_COEFF 6 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag57 -0.311210869423322 -INFO:pyaf.std:AR_MODEL_COEFF 7 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag33 0.2566604081916811 -INFO:pyaf.std:AR_MODEL_COEFF 8 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag58 0.24495773734224408 -INFO:pyaf.std:AR_MODEL_COEFF 9 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag35 0.2403240319363025 -INFO:pyaf.std:AR_MODEL_COEFF 10 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag59 -0.23788011139040338 +INFO:pyaf.std:AR_MODEL_COEFF 7 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag33 0.25666040819168096 +INFO:pyaf.std:AR_MODEL_COEFF 8 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag58 0.24495773734224505 +INFO:pyaf.std:AR_MODEL_COEFF 9 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag35 0.24032403193630264 +INFO:pyaf.std:AR_MODEL_COEFF 10 _visitors_ConstantTrend_residue_zeroCycle_residue_Lag59 -0.23788011139040327 INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST visitors 0.0599 diff --git a/tests/references/expsmooth_expsmooth_dataset_xrates.log b/tests/references/expsmooth_expsmooth_dataset_xrates.log index 7cf6131b7..467cffd52 100644 --- a/tests/references/expsmooth_expsmooth_dataset_xrates.log +++ b/tests/references/expsmooth_expsmooth_dataset_xrates.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'xrates' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'xrates' 2.800854206085205 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['xrates']' 2.067636489868164 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000.0 TimeMax=2004.91666666667 TimeDelta=0.08333333333338858 Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='xrates' Length=77 Min=0.3345 Max=0.4327 Mean=0.38838961038961034 StdDev=0.026084904249744398 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_xrates' Min=0.3345 Max=0.4327 Mean=0.38838961038961034 StdDev=0.026084904249744398 @@ -14,30 +14,58 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9833 MASE_Forecast=0.9994 MASE_Test=0.8206 INFO:pyaf.std:MODEL_L1 L1_Fit=0.00868833333333333 L1_Forecast=0.00675333333333333 L1_Test=0.01075000000000001 INFO:pyaf.std:MODEL_L2 L2_Fit=0.01106422914922981 L2_Forecast=0.008373967598058482 L2_Test=0.011003862958070687 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 0.3938 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _xrates_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'xrates' PERFORMANCE MAPE_FORECAST xrates 0.016 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'xrates' 3.336872100830078 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['xrates']' 1.99220609664917 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000.0 TimeMax=2004.75 TimeDelta=0.08333333333333333 Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='xrates' Length=77 Min=0.3345 Max=0.4327 Mean=0.38838961038961034 StdDev=0.026084904249744398 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_xrates' Min=0.3345 Max=0.4327 Mean=0.38838961038961034 StdDev=0.026084904249744398 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_xrates_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_xrates_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_xrates_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_xrates_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0238 MAPE_Forecast=0.0154 MAPE_Test=0.0218 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0237 SMAPE_Forecast=0.0154 SMAPE_Test=0.0216 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9828 MASE_Forecast=0.9681 MASE_Test=0.7585 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.008806896551724133 L1_Forecast=0.006499999999999998 L1_Test=0.00895 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.011206355955131147 L2_Forecast=0.007830410802335559 L2_Test=0.010434797554337117 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:BEST_DECOMPOSITION '_xrates_PolyTrend_residue_zeroCycle_residue_AR(19)' [PolyTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_xrates_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_xrates_PolyTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_xrates_PolyTrend_residue_zeroCycle_residue_AR(19)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0385 MAPE_Forecast=0.0232 MAPE_Test=0.0963 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0384 SMAPE_Forecast=0.0229 SMAPE_Test=0.0916 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.6252 MASE_Forecast=1.4491 MASE_Test=3.3684 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.014563968980119722 L1_Forecast=0.0097297752169437 L1_Test=0.039747420513600126 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.016941816205097587 L2_Forecast=0.01157579207683038 L2_Test=0.040977263026806536 +INFO:pyaf.std:MODEL_COMPLEXITY 30 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (0.36814346268662107, array([-0.00700374, 0.02182827, 0.02658008])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _xrates_PolyTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _xrates_PolyTrend_residue_zeroCycle_residue_Lag1 0.013729281469686272 +INFO:pyaf.std:AR_MODEL_COEFF 2 _xrates_PolyTrend_residue_zeroCycle_residue_Lag2 0.011155406546246865 +INFO:pyaf.std:AR_MODEL_COEFF 3 _xrates_PolyTrend_residue_zeroCycle_residue_Lag3 0.009446186420302472 +INFO:pyaf.std:AR_MODEL_COEFF 4 _xrates_PolyTrend_residue_zeroCycle_residue_Lag4 0.007072318541346748 +INFO:pyaf.std:AR_MODEL_COEFF 5 _xrates_PolyTrend_residue_zeroCycle_residue_Lag5 0.005952624212566229 +INFO:pyaf.std:AR_MODEL_COEFF 6 _xrates_PolyTrend_residue_zeroCycle_residue_Lag6 0.004956388048193947 +INFO:pyaf.std:AR_MODEL_COEFF 7 _xrates_PolyTrend_residue_zeroCycle_residue_Lag7 0.0024279964878111833 +INFO:pyaf.std:AR_MODEL_COEFF 8 _xrates_PolyTrend_residue_zeroCycle_residue_Lag14 -0.0015736759342885984 +INFO:pyaf.std:AR_MODEL_COEFF 9 _xrates_PolyTrend_residue_zeroCycle_residue_Lag13 -0.0013867474133055606 +INFO:pyaf.std:AR_MODEL_COEFF 10 _xrates_PolyTrend_residue_zeroCycle_residue_Lag9 0.0008902042554044072 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'xrates' -PERFORMANCE MAPE_FORECAST xrates 0.0154 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'xrates' 3.318103313446045 +PERFORMANCE MAPE_FORECAST xrates 0.0232 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['xrates']' 1.7526485919952393 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000.0 TimeMax=2004.5 TimeDelta=0.08333333333333333 Horizon=8 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='xrates' Length=77 Min=0.3345 Max=0.4327 Mean=0.38838961038961034 StdDev=0.026084904249744398 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_xrates' Min=0.3345 Max=0.4327 Mean=0.38838961038961034 StdDev=0.026084904249744398 @@ -52,11 +80,20 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9818 MASE_Forecast=0.9814 MASE_Test=1.0574 INFO:pyaf.std:MODEL_L1 L1_Fit=0.008869090909090903 L1_Forecast=0.006899999999999997 L1_Test=0.007462500000000004 INFO:pyaf.std:MODEL_L2 L2_Fit=0.011353413583587973 L2_Forecast=0.00807845459054073 L2_Test=0.008915506155008813 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 0.3938 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _xrates_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'xrates' PERFORMANCE MAPE_FORECAST xrates 0.0166 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'xrates' 3.3158650398254395 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['xrates']' 2.1913557052612305 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000.0 TimeMax=2004.25 TimeDelta=0.08333333333333333 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='xrates' Length=77 Min=0.3345 Max=0.4327 Mean=0.38838961038961034 StdDev=0.026084904249744398 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_xrates' Min=0.3345 Max=0.4327 Mean=0.38838961038961034 StdDev=0.026084904249744398 @@ -68,9 +105,18 @@ INFO:pyaf.std:AUTOREG_DETAIL '_xrates_LinearTrend_residue_zeroCycle_residue_NoAR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0475 MAPE_Forecast=0.0248 MAPE_Test=0.0441 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0473 SMAPE_Forecast=0.025 SMAPE_Test=0.0453 INFO:pyaf.std:MODEL_MASE MASE_Fit=1.9828 MASE_Forecast=1.9069 MASE_Test=2.1064 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.01784913802475873 L1_Forecast=0.010011091243482957 L1_Test=0.018785497656954586 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.020450396713492726 L2_Forecast=0.010937362957415814 L2_Test=0.020753654723463574 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.01784913802475873 L1_Forecast=0.010011091243482949 L1_Test=0.018785497656954583 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.02045039671349273 L2_Forecast=0.010937362957415809 L2_Test=0.02075365472346357 INFO:pyaf.std:MODEL_COMPLEXITY 16 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.3623409601681059, array([0.02982577])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _xrates_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END PERFORMANCE MAPE_FORECAST xrates 0.0248 diff --git a/tests/references/func_test_air_passengers.log b/tests/references/func_test_air_passengers.log index 58d946717..99310df32 100644 --- a/tests/references/func_test_air_passengers.log +++ b/tests/references/func_test_air_passengers.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 3.3015007972717285 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.679551839828491 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -11,34 +11,44 @@ INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle_resid INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0349 MAPE_Forecast=0.0217 MAPE_Test=0.0541 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0346 SMAPE_Forecast=0.022 SMAPE_Test=0.0558 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3563 MASE_Forecast=0.2287 MASE_Test=0.495 -INFO:pyaf.std:MODEL_L1 L1_Fit=6.661608657426588 L1_Forecast=8.471185219451277 L1_Test=22.27708217077865 -INFO:pyaf.std:MODEL_L2 L2_Fit=8.553917012084606 L2_Forecast=11.97176290466267 L2_Test=23.591231598022006 +INFO:pyaf.std:MODEL_L1 L1_Fit=6.6616086574266005 L1_Forecast=8.471185219451263 L1_Test=22.27708217077866 +INFO:pyaf.std:MODEL_L2 L2_Fit=8.553917012084607 L2_Forecast=11.971762904662638 L2_Test=23.59123159802204 INFO:pyaf.std:MODEL_COMPLEXITY 40 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (114.90523344816273, array([197.60619977])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (114.90523344816275, array([197.60619977])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag1 0.7786311442771898 -INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag12 0.739375705368332 -INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag13 -0.5331389013991887 -INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag25 -0.2876891887152456 -INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag24 0.2430278567579437 -INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag33 -0.18406731004063498 -INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag5 0.1716184438514903 -INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag29 -0.16896686902773136 -INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16816626466278783 -INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag16 -0.16495284212978784 +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag1 0.7786311442771912 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag12 0.7393757053683319 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag13 -0.5331389013991886 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag25 -0.2876891887152461 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag24 0.24302785675794508 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag33 -0.18406731004063412 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag5 0.17161844385149028 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag29 -0.16896686902773 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16816626466278906 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag16 -0.16495284212978803 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.238086700439453 -INFO:pyaf.std:START_FORECASTING 'AirPassengers' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'AirPassengers' 0.2702820301055908 +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 14.843138456344604 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.6323308944702148 Split Transformation ... ForecastMAPE TestMAPE 0 None Diff_AirPassengers ... 0.0205 0.0402 1 None _AirPassengers ... 0.0217 0.0541 @@ -107,31 +117,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "40", - "MAE": "8.471185219451277", - "MAPE": "0.0217", - "MASE": "0.2287", - "RMSE": "11.97176290466267" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "40", + "MAE": "8.471185219451263", + "MAPE": "0.0217", + "MASE": "0.2287", + "RMSE": "11.971762904662638" + } } } @@ -140,7 +152,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":336.76433931,"121":322.8158768976,"122":370.1970011391,"123":378.032857026,"124":398.9335945668,"125":490.9916896704,"126":527.6050111738,"127":547.2692898967,"128":447.2569355178,"129":389.1498397716,"130":336.5874463251,"131":365.0545119965,"132":404.1927658782,"133":362.2723575753,"134":407.3659989041,"135":392.3773701384,"136":426.0082038065,"137":494.6325800918,"138":561.1241745773,"139":558.3950905274,"140":437.9865222534,"141":374.1309595368,"142":319.9068836751,"143":355.980727},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":380.728110585,"133":325.1207093518,"134":357.9663450975,"135":272.4070322436,"136":98.7776096737,"137":-219.4948761306,"138":-928.2586710993,"139":-2259.4796258236,"140":-4091.7357686501,"141":-5051.1682697063,"142":-4712.3577817419,"143":-7009.1109734824},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":427.6574211713,"133":399.4240057988,"134":456.7656527106,"135":512.3477080331,"136":753.2387979394,"137":1208.7600363143,"138":2050.507020254,"139":3376.2698068784,"140":4967.708813157,"141":5799.4301887799,"142":5352.171549092,"143":7721.0724274824}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":336.76433931,"121":322.8158768976,"122":370.1970011391,"123":378.032857026,"124":398.9335945668,"125":490.9916896704,"126":527.6050111738,"127":547.2692898967,"128":447.2569355178,"129":389.1498397716,"130":336.5874463251,"131":365.0545119965,"132":404.1927658782,"133":362.2723575753,"134":407.3659989041,"135":392.3773701384,"136":426.0082038065,"137":494.6325800918,"138":561.1241745773,"139":558.3950905274,"140":437.9865222534,"141":374.1309595368,"142":319.9068836751,"143":355.980727},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":380.728110585,"133":325.1207093518,"134":357.9663450975,"135":272.4070322436,"136":98.7776096737,"137":-219.4948761306,"138":-928.2586710993,"139":-2259.4796258236,"140":-4091.7357686502,"141":-5051.1682697064,"142":-4712.357781742,"143":-7009.1109734824},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":427.6574211713,"133":399.4240057988,"134":456.7656527106,"135":512.3477080331,"136":753.2387979394,"137":1208.7600363143,"138":2050.507020254,"139":3376.2698068785,"140":4967.708813157,"141":5799.43018878,"142":5352.1715490921,"143":7721.0724274824}} diff --git a/tests/references/func_test_ar.log b/tests/references/func_test_ar.log index af7d9894f..5a9c961bb 100644 --- a/tests/references/func_test_ar.log +++ b/tests/references/func_test_ar.log @@ -1,7 +1,5 @@ INFO:pyaf.std:START_TRAINING 'Signal' -GENERATING_RANDOM_DATASET Signal_320_D_0_constant_0_None_0.1_20 -TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 1.0200026035308838 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 1.8963022232055664 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-27T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=310 Min=1.0 Max=2.3961078426834614 Mean=1.6786427448426395 StdDev=0.33316895719268297 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=2.3961078426834614 Mean=1.6786427448426395 StdDev=0.33316895719268297 @@ -13,8 +11,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_zeroCycle_residue_AR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0483 MAPE_Forecast=0.0412 MAPE_Test=0.0491 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0479 SMAPE_Forecast=0.0413 SMAPE_Test=0.0472 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6579 MASE_Forecast=0.7438 MASE_Test=1.0549 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07272168960930346 L1_Forecast=0.08658212172977552 L1_Test=0.10299212721593247 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.0894367486754245 L2_Forecast=0.10607718327030441 L2_Test=0.13169366144593037 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07272168960930347 L1_Forecast=0.0865821217297755 L1_Test=0.10299212721593225 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.0894367486754245 L2_Forecast=0.10607718327030437 L2_Test=0.13169366144593014 INFO:pyaf.std:MODEL_COMPLEXITY 60 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -26,21 +24,33 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag12 0.12230707909911151 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag24 0.11200036288006332 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag2 0.10920207789493457 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag30 0.08550565294419284 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.08233850688685541 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag9 0.07853352430759519 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag33 0.07815828245187742 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag44 -0.07567659183479983 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag19 0.07453893923867934 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag1 0.06933563468048724 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1223070790991117 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag24 0.11200036288006338 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag2 0.10920207789493436 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag30 0.08550565294419274 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag49 -0.08233850688685543 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag9 0.07853352430759522 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag33 0.07815828245187723 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag44 -0.07567659183479963 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag19 0.07453893923867927 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag1 0.06933563468048728 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.898416042327881 -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 0.5050721168518066 +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 17.147313833236694 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.42399263381958 +GENERATING_RANDOM_DATASET Signal_320_D_0_constant_0_None_0.1_20 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 Split Transformation ... ForecastMAPE TestMAPE 0 None _Signal ... 0.0412 0.0491 @@ -69,45 +79,47 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 7.6 KB None Forecasts - [[Timestamp('2000-11-06 00:00:00') nan 2.2230232710347306] - [Timestamp('2000-11-07 00:00:00') nan 2.231936966163537] - [Timestamp('2000-11-08 00:00:00') nan 2.242516996509312] - [Timestamp('2000-11-09 00:00:00') nan 2.2747290102390076] - [Timestamp('2000-11-10 00:00:00') nan 2.2361303366652745] - [Timestamp('2000-11-11 00:00:00') nan 2.2571073333055702] - [Timestamp('2000-11-12 00:00:00') nan 2.2409994383835747] - [Timestamp('2000-11-13 00:00:00') nan 2.2395427969937836] - [Timestamp('2000-11-14 00:00:00') nan 2.200301596897818] - [Timestamp('2000-11-15 00:00:00') nan 2.237292233291693]] + [[Timestamp('2000-11-06 00:00:00') nan 2.2230232710347297] + [Timestamp('2000-11-07 00:00:00') nan 2.2319369661635364] + [Timestamp('2000-11-08 00:00:00') nan 2.2425169965093117] + [Timestamp('2000-11-09 00:00:00') nan 2.274729010239007] + [Timestamp('2000-11-10 00:00:00') nan 2.236130336665274] + [Timestamp('2000-11-11 00:00:00') nan 2.2571073333055685] + [Timestamp('2000-11-12 00:00:00') nan 2.240999438383574] + [Timestamp('2000-11-13 00:00:00') nan 2.239542796993783] + [Timestamp('2000-11-14 00:00:00') nan 2.200301596897817] + [Timestamp('2000-11-15 00:00:00') nan 2.2372922332916927]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-05 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-05 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 310 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 310 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "60", - "MAE": "0.08658212172977552", - "MAPE": "0.0412", - "MASE": "0.7438", - "RMSE": "0.10607718327030441" + "Model_Performance": { + "COMPLEXITY": "60", + "MAE": "0.0865821217297755", + "MAPE": "0.0412", + "MASE": "0.7438", + "RMSE": "0.10607718327030437" + } } } diff --git a/tests/references/func_test_const_signal.log b/tests/references/func_test_const_signal.log index edde93f54..dfb30e75a 100644 --- a/tests/references/func_test_const_signal.log +++ b/tests/references/func_test_const_signal.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal' GENERATING_RANDOM_DATASET Signal_320_D_0_constant_0_None_0.0_20 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 4.178771734237671 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 4.675165891647339 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-27T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=310 Min=1.0 Max=1.0 Mean=1.0 StdDev=0.0 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.0 Mean=1.0 StdDev=0.0 @@ -27,8 +27,8 @@ INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0. INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 0.12195777893066406 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.33632874488830566 Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', '_Signal_ConstantTrend_residue_zeroCycle', @@ -67,31 +67,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-05 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-05 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 310 }, - "Training_Signal_Length": 310 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.0", - "MAPE": "0.0", - "MASE": "0.0", - "RMSE": "0.0" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.0", + "MAPE": "0.0", + "MASE": "0.0", + "RMSE": "0.0" + } } } diff --git a/tests/references/func_test_cycle.log b/tests/references/func_test_cycle.log index ae006da9a..cf21eccdf 100644 --- a/tests/references/func_test_cycle.log +++ b/tests/references/func_test_cycle.log @@ -1,7 +1,7 @@ INFO:pyaf.std:START_TRAINING 'Signal' GENERATING_RANDOM_DATASET Signal_320_D_0_constant_12_None_0.0_20 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 9.072446346282959 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 5.939788341522217 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-27T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=310 Min=1.0 Max=7.666666666666668 Mean=4.317204301075269 StdDev=2.065181064514396 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=7.666666666666668 Mean=4.317204301075269 StdDev=2.065181064514396 @@ -27,8 +27,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_b INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 0.13199496269226074 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.3715379238128662 Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', @@ -67,31 +67,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-05 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-05 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 310 }, - "Training_Signal_Length": 310 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.0", - "MAPE": "0.0", - "MASE": "0.0", - "RMSE": "0.0" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.0", + "MAPE": "0.0", + "MASE": "0.0", + "RMSE": "0.0" + } } } diff --git a/tests/references/func_test_cycles_full.log b/tests/references/func_test_cycles_full.log index e90d1a61e..c350d6938 100644 --- a/tests/references/func_test_cycles_full.log +++ b/tests/references/func_test_cycles_full.log @@ -2,7 +2,7 @@ INFO:pyaf.std:START_TRAINING 'Signal' TEST_CYCLES_START 2 GENERATING_RANDOM_DATASET Signal_320_D_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 10.064685344696045 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 7.568289279937744 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-27T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=310 Min=1.0 Max=1.5155737531290594 Mean=1.277121765684618 StdDev=0.09903880513569467 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.5155737531290594 Mean=1.277121765684618 StdDev=0.09903880513569467 @@ -28,8 +28,8 @@ INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0. INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 0.12284564971923828 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.32939815521240234 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -69,31 +69,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-05 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-05 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 310 }, - "Training_Signal_Length": 310 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.08333241474043424", - "MAPE": "0.0667", - "MASE": "0.6273", - "RMSE": "0.10455424282182894" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.08333241474043424", + "MAPE": "0.0667", + "MASE": "0.6273", + "RMSE": "0.10455424282182894" + } } } @@ -110,7 +112,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 6 GENERATING_RANDOM_DATASET Signal_320_D_0_constant_6_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 9.541827917098999 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 6.199337720870972 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-27T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=310 Min=1.0 Max=8.17904746208633 Mean=4.63612900761778 StdDev=2.153499462270964 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.17904746208633 Mean=4.63612900761778 StdDev=2.153499462270964 @@ -136,8 +138,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_b INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 0.1335902214050293 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.3482332229614258 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -177,31 +179,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-05 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-05 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 310 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 310 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08751798041179075", - "MAPE": "0.0303", - "MASE": "0.026", - "RMSE": "0.114616794488263" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08751798041179075", + "MAPE": "0.0303", + "MASE": "0.026", + "RMSE": "0.114616794488263" + } } } @@ -218,7 +222,7 @@ TEST_CYCLES_END 6 TEST_CYCLES_START 10 GENERATING_RANDOM_DATASET Signal_320_D_0_constant_10_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 9.26254940032959 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 5.5872132778167725 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-27T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=310 Min=1.0 Max=8.410655384928068 Mean=4.621458096173091 StdDev=2.2468706968183425 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.410655384928068 Mean=4.621458096173091 StdDev=2.2468706968183425 @@ -244,8 +248,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_b INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 0.14250564575195312 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.29526495933532715 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -285,31 +289,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-05 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-05 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 310 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 310 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08261352724752732", - "MAPE": "0.0262", - "MASE": "0.0275", - "RMSE": "0.10413973384371474" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08261352724752732", + "MAPE": "0.0262", + "MASE": "0.0275", + "RMSE": "0.10413973384371474" + } } } @@ -326,7 +332,7 @@ TEST_CYCLES_END 10 TEST_CYCLES_START 14 GENERATING_RANDOM_DATASET Signal_320_D_0_constant_14_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 9.865206956863403 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 5.3243491649627686 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-27T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=310 Min=1.0 Max=9.213200580138869 Mean=4.778171257303403 StdDev=2.2152438020438554 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=9.213200580138869 Mean=4.778171257303403 StdDev=2.2152438020438554 @@ -352,8 +358,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_b INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 0.13463950157165527 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.3443641662597656 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -393,31 +399,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-05 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-05 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 310 }, - "Training_Signal_Length": 310 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.0808774175221037", - "MAPE": "0.0218", - "MASE": "0.0368", - "RMSE": "0.10167283346839702" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.0808774175221037", + "MAPE": "0.0218", + "MASE": "0.0368", + "RMSE": "0.10167283346839702" + } } } @@ -434,7 +442,7 @@ TEST_CYCLES_END 14 TEST_CYCLES_START 18 GENERATING_RANDOM_DATASET Signal_320_D_0_constant_18_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 9.677199363708496 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 9.640143394470215 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-27T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=310 Min=1.0 Max=10.178704413589598 Mean=5.374294452910538 StdDev=2.552622094143388 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.178704413589598 Mean=5.374294452910538 StdDev=2.552622094143388 @@ -460,8 +468,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_b INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 0.1412651538848877 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.183312177658081 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -501,31 +509,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-05 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-05 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 310 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 310 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08152884487923115", - "MAPE": "0.0202", - "MASE": "0.0238", - "RMSE": "0.10076162390840163" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08152884487923115", + "MAPE": "0.0202", + "MASE": "0.0238", + "RMSE": "0.10076162390840163" + } } } @@ -542,7 +552,7 @@ TEST_CYCLES_END 18 TEST_CYCLES_START 22 GENERATING_RANDOM_DATASET Signal_320_D_0_constant_22_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 9.943585872650146 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 19.865132808685303 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-27T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=310 Min=1.0 Max=10.378765998816458 Mean=5.8595090592225825 StdDev=2.8217253313077646 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.378765998816458 Mean=5.8595090592225825 StdDev=2.8217253313077646 @@ -568,8 +578,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_b INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Signal' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal' 0.13298606872558594 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.0376954078674316 Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', @@ -608,31 +618,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-05 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-05 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 310 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 310 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.09129673016047941", - "MAPE": "0.0209", - "MASE": "0.0335", - "RMSE": "0.11670563497370497" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.09129673016047941", + "MAPE": "0.0209", + "MASE": "0.0335", + "RMSE": "0.11670563497370497" + } } } diff --git a/tests/references/func_test_ozone.log b/tests/references/func_test_ozone.log index e5c6b67f3..f55500e25 100644 --- a/tests/references/func_test_ozone.log +++ b/tests/references/func_test_ozone.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.963961601257324 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 6.358185052871704 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,8 +17,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6135978624272591 L1_Forecast=0.5265316758013037 L1_Test=0.42992050508902385 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507152 L2_Test=0.551943269559555 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -30,21 +30,31 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033754 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533328 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225498 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.1612820579538937 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046368 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481863 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102252 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045825 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947768 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.3232128620147705 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.35144662857055664 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 17.079187154769897 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.613811731338501 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1595 0.1740 1 None _Ozone ... 0.1595 0.1740 @@ -94,31 +104,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013037", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507152" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/func_test_ozone_bench_mode.log b/tests/references/func_test_ozone_bench_mode.log index a1002cde2..cc4dcf904 100644 --- a/tests/references/func_test_ozone_bench_mode.log +++ b/tests/references/func_test_ozone_bench_mode.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 2.616697072982788 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.812144041061401 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_Ozone' Min=1.224744871391589 Max=2.345207879911715 Mean=1.6888656389128833 StdDev=0.23126713490313816 @@ -11,22 +11,22 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthO INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1759 MAPE_Forecast=0.1585 MAPE_Test=0.2047 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1714 SMAPE_Forecast=0.1685 SMAPE_Test=0.193 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7753 MASE_Forecast=0.6486 MASE_Test=0.9934 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.681476429825786 L1_Forecast=0.5035179972671759 L1_Test=0.46960080650823227 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.91735651790531 L2_Forecast=0.6130274976434766 L2_Test=0.5391782564913219 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6814764298257862 L1_Forecast=0.503517997267176 L1_Test=0.4696008065082326 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9173565179053101 L2_Forecast=0.6130274976434767 L2_Test=0.5391782564913223 INFO:pyaf.std:MODEL_COMPLEXITY 52 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.869444283726548, array([-0.27591835])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.8694442837265477, array([-0.27591835])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.013865913705672472 {1: -0.2589459421687994, 2: -0.22701184518897222, 3: -0.13146648891791957, 4: -0.024979341754752804, 5: -0.014911552358440439, 6: 0.12268433729086037, 7: 0.1979712827533111, 8: 0.21725579179141197, 9: 0.16141904674613694, 10: 0.13693172036372137, 11: -0.06294584860636898, 12: -0.22908603285792528} +INFO:pyaf.std:SEASONAL_MODEL_VALUES Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.013865913705672694 {1: -0.2589459421687992, 2: -0.22701184518897222, 3: -0.13146648891791934, 4: -0.024979341754752804, 5: -0.014911552358440217, 6: 0.12268433729086059, 7: 0.1979712827533111, 8: 0.21725579179141197, 9: 0.16141904674613716, 10: 0.13693172036372148, 11: -0.06294584860636876, 12: -0.22908603285792517} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.18739581108093262 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5217502117156982 Month Ozone Time 0 1955-01 2.7 1955-01-01 1 1955-02 2.0 1955-02-01 diff --git a/tests/references/func_test_ozone_with_logging.log b/tests/references/func_test_ozone_with_logging.log index b4b637ee3..d78abcdc4 100644 --- a/tests/references/func_test_ozone_with_logging.log +++ b/tests/references/func_test_ozone_with_logging.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.887260913848877 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 6.081360340118408 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,8 +17,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6135978624272591 L1_Forecast=0.5265316758013037 L1_Test=0.42992050508902385 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507152 L2_Test=0.551943269559555 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -30,21 +30,31 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033754 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533328 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225498 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.1612820579538937 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046368 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481863 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102252 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045825 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947768 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.3005194664001465 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3293776512145996 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 18.28645920753479 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.8077387809753418 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1595 0.1740 1 None _Ozone ... 0.1595 0.1740 @@ -94,31 +104,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013037", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507152" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/heroku_test_air_passengers_heroku.log b/tests/references/heroku_test_air_passengers_heroku.log index d25ab9b12..16793747c 100644 --- a/tests/references/heroku_test_air_passengers_heroku.log +++ b/tests/references/heroku_test_air_passengers_heroku.log @@ -4,11 +4,11 @@ None DATASET_DETECTED_COLUMNS Index(['Unnamed: 0', 'time', 'value'], dtype='object') DATASET_FINAL_COLUMNS Index(['Unnamed: 0', 'time', 'value', 'AirPassengers'], dtype='object') TRAIN_PARAMS (144, 4) Index(['Unnamed: 0', 'time', 'value', 'AirPassengers'], dtype='object') time AirPassengers 7 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 3.1623833179473877 -INFO:pyaf.std:START_FORECASTING 'AirPassengers' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'AirPassengers' 0.1602632999420166 -INFO:pyaf.std:START_FORECASTING 'AirPassengers' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'AirPassengers' 0.1598508358001709 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 2.8843560218811035 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.42217373847961426 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.4493381977081299 Split Transformation ... ForecastMAPE TestMAPE 0 None _AirPassengers ... 0.0305 0.0267 1 None _AirPassengers ... 0.0313 0.0292 @@ -56,31 +56,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 7, - "TimeMinMax": [ - "1949.0", - "1960.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 7, + "TimeMinMax": [ + "1949.0", + "1960.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 144 }, - "Training_Signal_Length": 144 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(36)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "27", - "MAE": "12.711938744752953", - "MAPE": "0.0313", - "MASE": "0.3196", - "RMSE": "15.640940381333348" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(36)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "27", + "MAE": "12.711938744752958", + "MAPE": "0.0313", + "MASE": "0.3196", + "RMSE": "15.640940381333357" + } } } diff --git a/tests/references/heroku_test_ozone_exog_heroku.log b/tests/references/heroku_test_ozone_exog_heroku.log index 85a638ee8..8c350f4de 100644 --- a/tests/references/heroku_test_ozone_exog_heroku.log +++ b/tests/references/heroku_test_ozone_exog_heroku.log @@ -6,11 +6,11 @@ DATASET_DETECTED_COLUMNS Index(['Month', 'Ozone'], dtype='object') DATASET_FINAL_COLUMNS Index(['Month', 'Ozone'], dtype='object') https://raw.githubusercontent.com/antoinecarme/pyaf/master/data/ozone-la-exogenous-3.csv TRAIN_PARAMS (216, 2) Index(['Month', 'Ozone'], dtype='object') Month Ozone 12 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 18.866985082626343 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.42421627044677734 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.4129221439361572 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 28.27467179298401 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 2.9740407466888428 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 2.887812614440918 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1230 0.0995 1 None _Ozone ... 0.1402 0.1598 @@ -62,31 +62,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1972-12-01 00:00:00" - ], - "TimeVariable": "Month" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1972-12-01 00:00:00" + ], + "TimeVariable": "Month" + }, + "Training_Signal_Length": 216 }, - "Training_Signal_Length": 216 - }, - "Model": { - "AR_Model": "ARX", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_ARX(54)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "40", - "MAE": "0.3228910894470966", - "MAPE": "0.123", - "MASE": "0.4646", - "RMSE": "0.40299738451311923" + "Model": { + "AR_Model": "ARX", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_ARX(54)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "40", + "MAE": "0.32289108944709716", + "MAPE": "0.123", + "MASE": "0.4646", + "RMSE": "0.40299738451312" + } } } diff --git a/tests/references/heroku_test_ozone_heroku.log b/tests/references/heroku_test_ozone_heroku.log index 8973e3574..e395717a1 100644 --- a/tests/references/heroku_test_ozone_heroku.log +++ b/tests/references/heroku_test_ozone_heroku.log @@ -4,11 +4,11 @@ None DATASET_DETECTED_COLUMNS Index(['Month', 'Ozone'], dtype='object') DATASET_FINAL_COLUMNS Index(['Month', 'Ozone'], dtype='object') TRAIN_PARAMS (216, 2) Index(['Month', 'Ozone'], dtype='object') Month Ozone 12 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 5.568573713302612 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.33522629737854004 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.34258317947387695 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.514277696609497 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.6615269184112549 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.6114733219146729 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1911 0.1412 1 None _Ozone ... 0.1928 0.1521 @@ -58,31 +58,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1972-12-01 00:00:00" - ], - "TimeVariable": "Month" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1972-12-01 00:00:00" + ], + "TimeVariable": "Month" + }, + "Training_Signal_Length": 216 }, - "Training_Signal_Length": 216 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(54)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "56", - "MAE": "0.5259990223234977", - "MAPE": "0.1911", - "MASE": "0.7568", - "RMSE": "0.6332578057427529" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(54)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "56", + "MAE": "0.5259990223234978", + "MAPE": "0.1911", + "MASE": "0.7568", + "RMSE": "0.6332578057427527" + } } } diff --git a/tests/references/heroku_test_seasonal_week_of_year.log b/tests/references/heroku_test_seasonal_week_of_year.log index 6b5d01df2..838c00252 100644 --- a/tests/references/heroku_test_seasonal_week_of_year.log +++ b/tests/references/heroku_test_seasonal_week_of_year.log @@ -10,11 +10,11 @@ DATASET_FINAL_COLUMNS Index(['Unnamed: 0', 'Symbol', 'Date', 'Close', 'Low', 'Vo TRAIN_PARAMS (857, 9) Index(['Unnamed: 0', 'Symbol', 'Date', 'Close', 'Low', 'Volume', 'Open', 'Adj_Close', 'High'], dtype='object') Date Close 21 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Close' 14.905033826828003 -INFO:pyaf.std:START_FORECASTING 'Close' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Close' 0.26789116859436035 -INFO:pyaf.std:START_FORECASTING 'Close' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Close' 0.25316715240478516 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Close']' 8.145004749298096 +INFO:pyaf.std:START_FORECASTING '['Close']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Close']' 0.7274196147918701 +INFO:pyaf.std:START_FORECASTING '['Close']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Close']' 0.5461690425872803 Split Transformation ... ForecastMAPE TestMAPE 0 None _Close ... 0.0121 0.0102 1 None _Close ... 0.0121 0.0106 @@ -73,31 +73,33 @@ Forecasts { - "Dataset": { - "Signal": "Close", - "Time": { - "Horizon": 21, - "TimeMinMax": [ - "2012-08-06 00:00:00", - "2015-12-31 00:00:00" - ], - "TimeVariable": "Date" + "Close": { + "Dataset": { + "Signal": "Close", + "Time": { + "Horizon": 21, + "TimeMinMax": [ + "2012-08-06 00:00:00", + "2015-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 857 }, - "Training_Signal_Length": 857 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Close_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "7.500913732142857", - "MAPE": "0.0121", - "MASE": "0.995", - "RMSE": "12.512385900527176" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Close_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "7.500913732142857", + "MAPE": "0.0121", + "MASE": "0.995", + "RMSE": "12.512385900527176" + } } } diff --git a/tests/references/hierarchical_test_grouped.log b/tests/references/hierarchical_test_grouped.log index 344d57943..b70c4f863 100644 --- a/tests/references/hierarchical_test_grouped.log +++ b/tests/references/hierarchical_test_grouped.log @@ -1,35 +1,9 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 7.489851236343384 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 7.447438716888428 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 7.527235984802246 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 7.526321172714233 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 7.513495683670044 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 7.540259122848511 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 7.562231540679932 +INFO:pyaf.std:START_TRAINING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 19.294182062149048 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.8942334651947021 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.862663745880127 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.8589720726013184 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.7390711307525635 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.6202609539031982 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.5611510276794434 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.4253883361816406 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.8939132690429688 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD @@ -50,26 +24,23 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female '_male_BU_Forecast', '__BU_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['_female', '_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.962506218113037} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.962506218113037} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731268} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731268} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711864, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163617} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711864, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163617} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.610169491525426, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830509, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072746} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.610169491525426, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830509, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072746} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '__BU_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556231} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '__BU_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556231} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.967922899960135} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.967922899960135} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253134} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253134} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 9.639837265014648 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731265} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731265} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711864, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163619} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711864, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163619} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.610169491525426, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830509, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072747} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.610169491525426, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830509, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072747} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '__BU_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556235} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '__BU_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556235} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.9679228999601347} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.9679228999601347} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 23.00075054168701 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_female' Length=59 Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_NSW_female' Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 @@ -246,21 +217,8 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.673581600189209 -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.6879870891571045 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.7642879486083984 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.5184500217437744 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.749276876449585 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.48658323287963867 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.5552201271057129 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.889796733856201 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 1.5183334350585938 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.95469069480896 diff --git a/tests/references/hierarchical_test_grouped_signals_AllMethods.log b/tests/references/hierarchical_test_grouped_signals_AllMethods.log index a7971f4c0..72905b67f 100644 --- a/tests/references/hierarchical_test_grouped_signals_AllMethods.log +++ b/tests/references/hierarchical_test_grouped_signals_AllMethods.log @@ -1,35 +1,9 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 1.9705755710601807 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 1.9712097644805908 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 1.9828181266784668 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 1.9914360046386719 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 1.9992716312408447 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 2.0021235942840576 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 2.0339488983154297 +INFO:pyaf.std:START_TRAINING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 19.190720319747925 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.2480623722076416 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.2569093704223633 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.23530268669128418 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.23760771751403809 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.2353513240814209 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.23436880111694336 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.23476004600524902 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.467617988586426 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -66,11 +40,8 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female '__OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['_female', '_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['_']) INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0782, 'RMSE': 61.88312394961418, 'MAE': 47.33891833501301, 'SMAPE': 0.0765, 'ErrorMean': 3.1970833853646603, 'ErrorStdDev': 61.800482907420204, 'R2': 0.911769621603849, 'Pearson': 0.957496276648925} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0782, 'RMSE': 61.88312394961418, 'MAE': 47.33891833501301, 'SMAPE': 0.0765, 'ErrorMean': 3.1970833853646603, 'ErrorStdDev': 61.800482907420204, 'R2': 0.911769621603849, 'Pearson': 0.957496276648925} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.9625062181130374} @@ -141,7 +112,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_OC_F INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.975820255065713} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.975820255065713} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 3.199518918991089 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 22.954190492630005 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_female' Length=59 Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_NSW_female' Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 @@ -318,32 +289,242 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.2669956684112549 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.2369070053100586 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.23719215393066406 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.23960089683532715 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.23110079765319824 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.23541808128356934 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.23513484001159668 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.6903316974639893 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 0.6733498573303223 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.7619833946228027 -{'Structure': {0: {'NSW_female': [], 'NSW_male': [], 'VIC_female': [], 'VIC_male': []}, 1: {'_female': ['NSW_female', 'VIC_female'], '_male': ['NSW_male', 'VIC_male']}, 2: {'_': ['_female', '_male']}}, 'Models': {'NSW_female': {'Dataset': {'Time': {'TimeVariable': 'Index', 'TimeMinMax': ['1933', '1991'], 'Horizon': 12}, 'Signal': 'NSW_female', 'Training_Signal_Length': 59}, 'Model': {'Best_Decomposition': '_NSW_female_Lag1Trend_residue_zeroCycle_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'Lag1Trend', 'Cycle': 'NoCycle', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0694', 'MASE': '0.9831', 'MAE': '43.25423728813559', 'RMSE': '57.10145594629301', 'COMPLEXITY': '32'}}, 'NSW_male': {'Dataset': {'Time': {'TimeVariable': 'Index', 'TimeMinMax': ['1933', '1991'], 'Horizon': 12}, 'Signal': 'NSW_male', 'Training_Signal_Length': 59}, 'Model': {'Best_Decomposition': '_NSW_male_Lag1Trend_residue_zeroCycle_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'Lag1Trend', 'Cycle': 'NoCycle', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0666', 'MASE': '0.9831', 'MAE': '54.45762711864407', 'RMSE': '71.16643181268225', 'COMPLEXITY': '32'}}, 'VIC_female': {'Dataset': {'Time': {'TimeVariable': 'Index', 'TimeMinMax': ['1933', '1991'], 'Horizon': 12}, 'Signal': 'VIC_female', 'Training_Signal_Length': 59}, 'Model': {'Best_Decomposition': '_VIC_female_Lag1Trend_residue_zeroCycle_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'Lag1Trend', 'Cycle': 'NoCycle', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0908', 'MASE': '0.9831', 'MAE': '35.932203389830505', 'RMSE': '44.6959597006186', 'COMPLEXITY': '32'}}, 'VIC_male': {'Dataset': {'Time': {'TimeVariable': 'Index', 'TimeMinMax': ['1933', '1991'], 'Horizon': 12}, 'Signal': 'VIC_male', 'Training_Signal_Length': 59}, 'Model': {'Best_Decomposition': '_VIC_male_Lag1Trend_residue_zeroCycle_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'Lag1Trend', 'Cycle': 'NoCycle', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0852', 'MASE': '0.9831', 'MAE': '43.610169491525426', 'RMSE': '53.68662996769434', 'COMPLEXITY': '32'}}, '_female': {'Dataset': {'Time': {'TimeVariable': 'Index', 'TimeMinMax': ['1933', '1991'], 'Horizon': 12}, 'Signal': '_female', 'Training_Signal_Length': 59}, 'Model': {'Best_Decomposition': '__female_Lag1Trend_residue_zeroCycle_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'Lag1Trend', 'Cycle': 'NoCycle', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0683', 'MASE': '0.9831', 'MAE': '67.86440677966101', 'RMSE': '82.58390699715808', 'COMPLEXITY': '32'}}, '_male': {'Dataset': {'Time': {'TimeVariable': 'Index', 'TimeMinMax': ['1933', '1991'], 'Horizon': 12}, 'Signal': '_male', 'Training_Signal_Length': 59}, 'Model': {'Best_Decomposition': '__male_Lag1Trend_residue_zeroCycle_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'Lag1Trend', 'Cycle': 'NoCycle', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0588', 'MASE': '0.9831', 'MAE': '77.49152542372882', 'RMSE': '97.29737746217495', 'COMPLEXITY': '32'}}, '_': {'Dataset': {'Time': {'TimeVariable': 'Index', 'TimeMinMax': ['1933', '1991'], 'Horizon': 12}, 'Signal': '_', 'Training_Signal_Length': 59}, 'Model': {'Best_Decomposition': '___Lag1Trend_residue_zeroCycle_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'Lag1Trend', 'Cycle': 'NoCycle', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0581', 'MASE': '0.9831', 'MAE': '132.03389830508473', 'RMSE': '168.23812824865286', 'COMPLEXITY': '32'}}}} +{ + "Models": { + "NSW_female": { + "Dataset": { + "Signal": "NSW_female", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1933", + "1991" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 59 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_NSW_female_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "43.25423728813559", + "MAPE": "0.0694", + "MASE": "0.9831", + "RMSE": "57.10145594629301" + } + }, + "NSW_male": { + "Dataset": { + "Signal": "NSW_male", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1933", + "1991" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 59 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_NSW_male_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "54.45762711864407", + "MAPE": "0.0666", + "MASE": "0.9831", + "RMSE": "71.16643181268225" + } + }, + "VIC_female": { + "Dataset": { + "Signal": "VIC_female", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1933", + "1991" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 59 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_VIC_female_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "35.932203389830505", + "MAPE": "0.0908", + "MASE": "0.9831", + "RMSE": "44.6959597006186" + } + }, + "VIC_male": { + "Dataset": { + "Signal": "VIC_male", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1933", + "1991" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 59 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_VIC_male_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "43.610169491525426", + "MAPE": "0.0852", + "MASE": "0.9831", + "RMSE": "53.68662996769434" + } + }, + "_": { + "Dataset": { + "Signal": "_", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1933", + "1991" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 59 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "___Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "132.03389830508473", + "MAPE": "0.0581", + "MASE": "0.9831", + "RMSE": "168.23812824865286" + } + }, + "_female": { + "Dataset": { + "Signal": "_female", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1933", + "1991" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 59 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "__female_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "67.86440677966101", + "MAPE": "0.0683", + "MASE": "0.9831", + "RMSE": "82.58390699715808" + } + }, + "_male": { + "Dataset": { + "Signal": "_male", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1933", + "1991" + ], + "TimeVariable": "Index" + }, + "Training_Signal_Length": 59 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "__male_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "77.49152542372882", + "MAPE": "0.0588", + "MASE": "0.9831", + "RMSE": "97.29737746217495" + } + } + }, + "Structure": { + "0": { + "NSW_female": [], + "NSW_male": [], + "VIC_female": [], + "VIC_male": [] + }, + "1": { + "_female": [ + "NSW_female", + "VIC_female" + ], + "_male": [ + "NSW_male", + "VIC_male" + ] + }, + "2": { + "_": [ + "_female", + "_male" + ] + } + } +} diff --git a/tests/references/hierarchical_test_grouped_signals_AllMethods_2.log b/tests/references/hierarchical_test_grouped_signals_AllMethods_2.log index 5b2da11cb..8ed5c6b8a 100644 --- a/tests/references/hierarchical_test_grouped_signals_AllMethods_2.log +++ b/tests/references/hierarchical_test_grouped_signals_AllMethods_2.log @@ -1,83 +1,9 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ACT_female'), (0, 'ACT_male'), (0, 'NSW_female'), (0, 'NSW_male'), (0, 'NT_female'), (0, 'NT_male'), (0, 'QLD_female'), (0, 'QLD_male'), (0, 'SA_female'), (0, 'SA_male'), (0, 'TAS_female'), (0, 'TAS_male'), (0, 'VIC_female'), (0, 'VIC_male'), (0, 'WA_female'), (0, 'WA_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'ACT_female' -INFO:pyaf.std:START_TRAINING 'NT_female' -INFO:pyaf.std:START_TRAINING 'ACT_male' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING 'QLD_female' -INFO:pyaf.std:START_TRAINING 'NT_male' -INFO:pyaf.std:START_TRAINING 'QLD_male' -INFO:pyaf.std:START_TRAINING 'WA_female' -INFO:pyaf.std:START_TRAINING 'SA_male' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING 'SA_female' -INFO:pyaf.std:START_TRAINING 'TAS_female' -INFO:pyaf.std:START_TRAINING 'TAS_male' -INFO:pyaf.std:START_TRAINING 'WA_male' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 19.527703523635864 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NT_male' 19.545143365859985 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 19.557766914367676 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 19.52426314353943 -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NT_female' 19.58724069595337 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ACT_female' 19.631566047668457 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 19.619211673736572 -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'SA_male' 19.69933795928955 -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_male' 19.72106695175171 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_female' 19.75697898864746 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ACT_male' 19.79999613761902 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'WA_male' 19.725048303604126 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'WA_female' 19.787595510482788 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'SA_female' 19.78502893447876 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'TAS_male' 19.823724031448364 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'TAS_female' 19.832335710525513 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 3.1434125900268555 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 3.3500993251800537 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 3.4654858112335205 +INFO:pyaf.std:START_TRAINING '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' 35.291667222976685 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ACT_female'), (0, 'ACT_male'), (0, 'NSW_female'), (0, 'NSW_male'), (0, 'NT_female'), (0, 'NT_male'), (0, 'QLD_female'), (0, 'QLD_male'), (0, 'SA_female'), (0, 'SA_male'), (0, 'TAS_female'), (0, 'TAS_male'), (0, 'VIC_female'), (0, 'VIC_male'), (0, 'WA_female'), (0, 'WA_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'ACT_female' -INFO:pyaf.std:START_FORECASTING 'ACT_male' -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_female' 0.7303922176361084 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_male' 0.7126893997192383 -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'NT_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.8648180961608887 -INFO:pyaf.std:START_FORECASTING 'NT_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.6706371307373047 -INFO:pyaf.std:START_FORECASTING 'QLD_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_female' 0.873115062713623 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_male' 0.7117776870727539 -INFO:pyaf.std:START_FORECASTING 'QLD_male' -INFO:pyaf.std:START_FORECASTING 'SA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_female' 0.8569226264953613 -INFO:pyaf.std:START_FORECASTING 'SA_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_male' 0.999243974685669 -INFO:pyaf.std:START_FORECASTING 'TAS_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_female' 0.8456461429595947 -INFO:pyaf.std:START_FORECASTING 'TAS_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_male' 0.9114589691162109 -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_female' 0.9818117618560791 -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_male' 0.9495959281921387 -INFO:pyaf.std:START_FORECASTING 'WA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.924713134765625 -INFO:pyaf.std:START_FORECASTING 'WA_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.8729984760284424 -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_female' 0.9282350540161133 -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_male' 0.6503305435180664 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.5325210094451904 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.3644387722015381 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.21208786964416504 +INFO:pyaf.std:START_FORECASTING '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' 7.777931451797485 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -97,202 +23,199 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'ACT_female', 'ACT_female '_male_OC_Forecast', '__OC_Forecast'], dtype='object', length=172) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['_female', '_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 9496109669.6778, 'RMSE': 12.005508319785962, 'MAE': 10.785388579131682, 'SMAPE': 0.8641, 'ErrorMean': 3.1684192116295113, 'ErrorStdDev': 11.579868294407623, 'R2': -0.7460754102652289, 'Pearson': -0.4955559488414573} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 9496109669.6778, 'RMSE': 12.005508319785962, 'MAE': 10.785388579131682, 'SMAPE': 0.8641, 'ErrorMean': 3.1684192116295113, 'ErrorStdDev': 11.579868294407623, 'R2': -0.7460754102652289, 'Pearson': -0.4955559488414573} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_BU_Forecast', 'Length': 59, 'MAPE': 2.3483, 'RMSE': 36.07089270265762, 'MAE': 16.60413674231543, 'SMAPE': 1.8975, 'ErrorMean': -8.107727664464235, 'ErrorStdDev': 35.14788830762839, 'R2': -14.762157812852113, 'Pearson': -0.1359495955150412} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_BU_Forecast', 'Length': 59, 'MAPE': 2.3483, 'RMSE': 36.07089270265762, 'MAE': 16.60413674231543, 'SMAPE': 1.8975, 'ErrorMean': -8.107727664464235, 'ErrorStdDev': 35.14788830762839, 'R2': -14.762157812852113, 'Pearson': -0.1359495955150412} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 9496109669.6778, 'RMSE': 12.005508319785962, 'MAE': 10.785388579131682, 'SMAPE': 0.8641, 'ErrorMean': 3.1684192116295113, 'ErrorStdDev': 11.579868294407623, 'R2': -0.7460754102652289, 'Pearson': -0.49555594884145737} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 9496109669.6778, 'RMSE': 12.005508319785962, 'MAE': 10.785388579131682, 'SMAPE': 0.8641, 'ErrorMean': 3.1684192116295113, 'ErrorStdDev': 11.579868294407623, 'R2': -0.7460754102652289, 'Pearson': -0.49555594884145737} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_BU_Forecast', 'Length': 59, 'MAPE': 2.3483, 'RMSE': 36.07089270265762, 'MAE': 16.60413674231543, 'SMAPE': 1.8975, 'ErrorMean': -8.107727664464235, 'ErrorStdDev': 35.14788830762839, 'R2': -14.762157812852113, 'Pearson': -0.13594959551504152} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_BU_Forecast', 'Length': 59, 'MAPE': 2.3483, 'RMSE': 36.07089270265762, 'MAE': 16.60413674231543, 'SMAPE': 1.8975, 'ErrorMean': -8.107727664464235, 'ErrorStdDev': 35.14788830762839, 'R2': -14.762157812852113, 'Pearson': -0.13594959551504152} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_MO_Forecast', 'Length': 59, 'MAPE': 7751515549.8263, 'RMSE': 11.120181282678216, 'MAE': 10.028282299468742, 'SMAPE': 0.8826, 'ErrorMean': 0.1357241018257971, 'ErrorStdDev': 11.11935298152777, 'R2': -0.4980476396070652, 'Pearson': -0.5204665629842873} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_MO_Forecast', 'Length': 59, 'MAPE': 7751515549.8263, 'RMSE': 11.120181282678216, 'MAE': 10.028282299468742, 'SMAPE': 0.8826, 'ErrorMean': 0.1357241018257971, 'ErrorStdDev': 11.11935298152777, 'R2': -0.4980476396070652, 'Pearson': -0.5204665629842873} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_OC_Forecast', 'Length': 59, 'MAPE': 2113021101.6057, 'RMSE': 31.420742574166052, 'MAE': 14.38748058070833, 'SMAPE': 1.2711, 'ErrorMean': -5.463794362974976, 'ErrorStdDev': 30.942042839333247, 'R2': -10.96009913371304, 'Pearson': -0.08723644716944806} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_OC_Forecast', 'Length': 59, 'MAPE': 2113021101.6057, 'RMSE': 31.420742574166052, 'MAE': 14.38748058070833, 'SMAPE': 1.2711, 'ErrorMean': -5.463794362974976, 'ErrorStdDev': 30.942042839333247, 'R2': -10.96009913371304, 'Pearson': -0.08723644716944806} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 7646148296.5754, 'RMSE': 11.026983274545621, 'MAE': 9.945975239173306, 'SMAPE': 0.8785, 'ErrorMean': 0.1341749479305963, 'ErrorStdDev': 11.026166932368504, 'R2': -0.473042651446584, 'Pearson': -0.4955559488414572} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 7646148296.5754, 'RMSE': 11.026983274545621, 'MAE': 9.945975239173306, 'SMAPE': 0.8785, 'ErrorMean': 0.1341749479305963, 'ErrorStdDev': 11.026166932368504, 'R2': -0.473042651446584, 'Pearson': -0.4955559488414572} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 3.239, 'RMSE': 17.468162446403102, 'MAE': 15.93875213023385, 'SMAPE': 0.8604, 'ErrorMean': 4.3179797900864045, 'ErrorStdDev': 16.926067168315654, 'R2': -0.6830733360739292, 'Pearson': -0.4574779827276607} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 3.239, 'RMSE': 17.468162446403102, 'MAE': 15.93875213023385, 'SMAPE': 0.8604, 'ErrorMean': 4.3179797900864045, 'ErrorStdDev': 16.926067168315654, 'R2': -0.6830733360739292, 'Pearson': -0.4574779827276607} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_BU_Forecast', 'Length': 59, 'MAPE': 0.4944, 'RMSE': 6.911057217561346, 'MAE': 4.983050847457627, 'SMAPE': 0.3977, 'ErrorMean': -0.4067796610169492, 'ErrorStdDev': 6.899075457754446, 'R2': 0.7365503821922521, 'Pearson': 0.8706261722445393} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_BU_Forecast', 'Length': 59, 'MAPE': 0.4944, 'RMSE': 6.911057217561346, 'MAE': 4.983050847457627, 'SMAPE': 0.3977, 'ErrorMean': -0.4067796610169492, 'ErrorStdDev': 6.899075457754446, 'R2': 0.7365503821922521, 'Pearson': 0.8706261722445393} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_OC_Forecast', 'Length': 59, 'MAPE': 2113021101.6058, 'RMSE': 31.420742574166137, 'MAE': 14.387480580708715, 'SMAPE': 1.2711, 'ErrorMean': -5.463794362975758, 'ErrorStdDev': 30.94204283933319, 'R2': -10.960099133713104, 'Pearson': -0.08723644716944487} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_OC_Forecast', 'Length': 59, 'MAPE': 2113021101.6058, 'RMSE': 31.420742574166137, 'MAE': 14.387480580708715, 'SMAPE': 1.2711, 'ErrorMean': -5.463794362975758, 'ErrorStdDev': 30.94204283933319, 'R2': -10.960099133713104, 'Pearson': -0.08723644716944487} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 7646148296.5754, 'RMSE': 11.026983274545621, 'MAE': 9.945975239173306, 'SMAPE': 0.8785, 'ErrorMean': 0.1341749479305963, 'ErrorStdDev': 11.026166932368504, 'R2': -0.473042651446584, 'Pearson': -0.4955559488414574} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 7646148296.5754, 'RMSE': 11.026983274545621, 'MAE': 9.945975239173306, 'SMAPE': 0.8785, 'ErrorMean': 0.1341749479305963, 'ErrorStdDev': 11.026166932368504, 'R2': -0.473042651446584, 'Pearson': -0.4955559488414574} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 3.239, 'RMSE': 17.468162446403102, 'MAE': 15.93875213023385, 'SMAPE': 0.8604, 'ErrorMean': 4.3179797900864045, 'ErrorStdDev': 16.926067168315654, 'R2': -0.6830733360739292, 'Pearson': -0.45747798272766066} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 3.239, 'RMSE': 17.468162446403102, 'MAE': 15.93875213023385, 'SMAPE': 0.8604, 'ErrorMean': 4.3179797900864045, 'ErrorStdDev': 16.926067168315654, 'R2': -0.6830733360739292, 'Pearson': -0.45747798272766066} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_BU_Forecast', 'Length': 59, 'MAPE': 0.4944, 'RMSE': 6.911057217561346, 'MAE': 4.983050847457627, 'SMAPE': 0.3977, 'ErrorMean': -0.4067796610169492, 'ErrorStdDev': 6.899075457754446, 'R2': 0.7365503821922521, 'Pearson': 0.8706261722445398} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_BU_Forecast', 'Length': 59, 'MAPE': 0.4944, 'RMSE': 6.911057217561346, 'MAE': 4.983050847457627, 'SMAPE': 0.3977, 'ErrorMean': -0.4067796610169492, 'ErrorStdDev': 6.899075457754446, 'R2': 0.7365503821922521, 'Pearson': 0.8706261722445398} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_MO_Forecast', 'Length': 59, 'MAPE': 2.6277, 'RMSE': 16.108866636172998, 'MAE': 14.827228788111647, 'SMAPE': 0.8741, 'ErrorMean': 0.19768705010916565, 'ErrorStdDev': 16.10765358865861, 'R2': -0.431326024812833, 'Pearson': -0.43908053847914663} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_MO_Forecast', 'Length': 59, 'MAPE': 2.6277, 'RMSE': 16.108866636172998, 'MAE': 14.827228788111647, 'SMAPE': 0.8741, 'ErrorMean': 0.19768705010916565, 'ErrorStdDev': 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0.7292620963975018, 'Pearson': 0.8660888039205638} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_OC_Forecast', 'Length': 59, 'MAPE': 0.573, 'RMSE': 7.006001613422229, 'MAE': 5.197655907373249, 'SMAPE': 0.4515, 'ErrorMean': -0.1623259030039343, 'ErrorStdDev': 7.00412085193344, 'R2': 0.7292620963975018, 'Pearson': 0.8660888039205638} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 2.6418, 'RMSE': 16.20190073722583, 'MAE': 14.906479086069929, 'SMAPE': 0.877, 'ErrorMean': 0.19942943080394723, 'ErrorStdDev': 16.20067330085537, 'R2': -0.44790654050053913, 'Pearson': -0.4574779827276606} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 2.6418, 'RMSE': 16.20190073722583, 'MAE': 14.906479086069929, 'SMAPE': 0.877, 'ErrorMean': 0.19942943080394723, 'ErrorStdDev': 16.20067330085537, 'R2': -0.44790654050053913, 'Pearson': 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-INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.962506218113037} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.1019, 'RMSE': 72.60043576531967, 'MAE': 58.628300543554786, 'SMAPE': 0.0978, 'ErrorMean': 7.120879959863065, 'ErrorStdDev': 72.25037260742349, 'R2': 0.8785626901512268, 'Pearson': 0.9471775489080939} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.1019, 'RMSE': 72.60043576531967, 'MAE': 58.628300543554786, 'SMAPE': 0.0978, 'ErrorMean': 7.120879959863065, 'ErrorStdDev': 72.25037260742349, 'R2': 0.8785626901512268, 'Pearson': 0.9471775489080939} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0729, 'RMSE': 59.78480397576071, 'MAE': 45.563505262005584, 'SMAPE': 0.0715, 'ErrorMean': 10.576136691316306, 'ErrorStdDev': 58.8418908525782, 'R2': 0.9176515740566428, 'Pearson': 0.9595069107530496} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0729, 'RMSE': 59.78480397576071, 'MAE': 45.563505262005584, 'SMAPE': 0.0715, 'ErrorMean': 10.576136691316306, 'ErrorStdDev': 58.8418908525782, 'R2': 0.9176515740566428, 'Pearson': 0.9595069107530496} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.1038, 'RMSE': 74.04619559774962, 'MAE': 59.967628380738525, 'SMAPE': 0.0999, 'ErrorMean': 7.039602288626458, 'ErrorStdDev': 73.71080709175664, 'R2': 0.8736779447940266, 'Pearson': 0.9457869018064564} 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INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'SA_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.1414, 'RMSE': 21.30836394004338, 'MAE': 17.076247863956002, 'SMAPE': 0.1337, 'ErrorMean': -0.24469618660036258, 'ErrorStdDev': 21.306958900265528, 'R2': 0.7801873133160435, 'Pearson': 0.8925063847055462} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'SA_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.1414, 'RMSE': 21.30836394004338, 'MAE': 17.076247863956002, 'SMAPE': 0.1337, 'ErrorMean': -0.24469618660036258, 'ErrorStdDev': 21.306958900265528, 'R2': 0.7801873133160435, 'Pearson': 0.8925063847055462} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'SA_female_BU_Forecast', 'Length': 59, 'MAPE': 0.1327, 'RMSE': 23.444143837822175, 'MAE': 17.68678476386571, 'SMAPE': 0.1347, 'ErrorMean': -8.335045853939627, 'ErrorStdDev': 21.91243690010811, 'R2': 0.7339144457180877, 'Pearson': 0.8971404027472314} 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INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'SA_female_MO_Forecast', 'Length': 59, 'MAPE': 0.1466, 'RMSE': 21.40775912566637, 'MAE': 17.55980986489461, 'SMAPE': 0.1372, 'ErrorMean': 1.4933359509630755, 'ErrorStdDev': 21.35561046938516, 'R2': 0.7781318500436749, 'Pearson': 0.8889899250392846} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'SA_female_MO_Forecast', 'Length': 59, 'MAPE': 0.1466, 'RMSE': 21.40775912566637, 'MAE': 17.55980986489461, 'SMAPE': 0.1372, 'ErrorMean': 1.4933359509630755, 'ErrorStdDev': 21.35561046938516, 'R2': 0.7781318500436749, 'Pearson': 0.8889899250392846} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'SA_female_OC_Forecast', 'Length': 59, 'MAPE': 0.1175, 'RMSE': 20.54863005926616, 'MAE': 14.915372375125084, 'SMAPE': 0.1171, 'ErrorMean': -5.691112552452527, 'ErrorStdDev': 19.74480780427842, 'R2': 0.7955823976826409, 'Pearson': 0.9142068380380873} 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INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'SA_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.1447, 'RMSE': 21.242810314364153, 'MAE': 17.188937532161876, 'SMAPE': 0.1353, 'ErrorMean': 1.4762910254549502, 'ErrorStdDev': 21.19145004147232, 'R2': 0.7815377082789844, 'Pearson': 0.8925063847055462} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'SA_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.1447, 'RMSE': 21.242810314364153, 'MAE': 17.188937532161876, 'SMAPE': 0.1353, 'ErrorMean': 1.4762910254549502, 'ErrorStdDev': 21.19145004147232, 'R2': 0.7815377082789844, 'Pearson': 0.8925063847055462} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'SA_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.1279, 'RMSE': 27.037234238070397, 'MAE': 21.54771453468833, 'SMAPE': 0.1207, 'ErrorMean': 0.8645064843562755, 'ErrorStdDev': 27.02340955140177, 'R2': 0.781357943384058, 'Pearson': 0.8865675626456004} 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INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'SA_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.1284, 'RMSE': 27.057809674635514, 'MAE': 21.418818461772382, 'SMAPE': 0.1203, 'ErrorMean': 2.0014429732980554, 'ErrorStdDev': 26.983685634350156, 'R2': 0.7810250419139084, 'Pearson': 0.8865675626456004} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'SA_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.1284, 'RMSE': 27.057809674635514, 'MAE': 21.418818461772382, 'SMAPE': 0.1203, 'ErrorMean': 2.0014429732980554, 'ErrorStdDev': 26.983685634350156, 'R2': 0.7810250419139084, 'Pearson': 0.8865675626456004} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'TAS_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.1582, 'RMSE': 16.073460206357332, 'MAE': 12.337611565284753, 'SMAPE': 0.1504, 'ErrorMean': -0.43732862058953426, 'ErrorStdDev': 16.067509660272993, 'R2': 0.7185768828456337, 'Pearson': 0.8619126219361088} 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INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'TAS_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.1587, 'RMSE': 16.05963007927827, 'MAE': 12.344418712521977, 'SMAPE': 0.1506, 'ErrorMean': -0.29501359428997126, 'ErrorStdDev': 16.056920167405814, 'R2': 0.7190609656717527, 'Pearson': 0.8619126219361086} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'TAS_male_BU_Forecast', 'Length': 59, 'MAPE': 0.1152, 'RMSE': 11.67439223952609, 'MAE': 8.825584463985871, 'SMAPE': 0.1091, 'ErrorMean': -7.105427357601002e-15, 'ErrorStdDev': 11.67439223952609, 'R2': 0.8515399604343774, 'Pearson': 0.9247501485103832} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'TAS_male_BU_Forecast', 'Length': 59, 'MAPE': 0.1152, 'RMSE': 11.67439223952609, 'MAE': 8.825584463985871, 'SMAPE': 0.1091, 'ErrorMean': -7.105427357601002e-15, 'ErrorStdDev': 11.67439223952609, 'R2': 0.8515399604343774, 'Pearson': 0.9247501485103832} 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+INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0506, 'RMSE': 131.4425718125144, 'MAE': 107.06085277327496, 'SMAPE': 0.0496, 'ErrorMean': 23.38983050847458, 'ErrorStdDev': 129.3447544876592, 'R2': 0.953623103577251, 'Pearson': 0.977437389750995} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0506, 'RMSE': 131.4425718125144, 'MAE': 107.06085277327496, 'SMAPE': 0.0496, 'ErrorMean': 23.38983050847458, 'ErrorStdDev': 129.3447544876592, 'R2': 0.953623103577251, 'Pearson': 0.977437389750995} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0507, 'RMSE': 133.58873138308624, 'MAE': 107.88135593220339, 'SMAPE': 0.0497, 'ErrorMean': 24.627118644067796, 'ErrorStdDev': 131.2991019764925, 'R2': 0.9520962803171982, 'Pearson': 0.9766696539238354} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0507, 'RMSE': 133.58873138308624, 'MAE': 107.88135593220339, 'SMAPE': 0.0497, 'ErrorMean': 24.627118644067796, 'ErrorStdDev': 131.2991019764925, 'R2': 0.9520962803171982, 'Pearson': 0.9766696539238354} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0509, 'RMSE': 134.16417049500774, 'MAE': 108.4053893615551, 'SMAPE': 0.0499, 'ErrorMean': 25.345460572557585, 'ErrorStdDev': 131.74836724976305, 'R2': 0.951682696869264, 'Pearson': 0.9764932615707318} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0509, 'RMSE': 134.16417049500774, 'MAE': 108.4053893615551, 'SMAPE': 0.0499, 'ErrorMean': 25.345460572557585, 'ErrorStdDev': 131.74836724976305, 'R2': 0.951682696869264, 'Pearson': 0.9764932615707318} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0508, 'RMSE': 131.4771242919769, 'MAE': 108.07986153549697, 'SMAPE': 0.0498, 'ErrorMean': 24.844177961154166, 'ErrorStdDev': 129.10848552098523, 'R2': 0.9535987180626282, 'Pearson': 0.9773976998188207} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0508, 'RMSE': 131.4771242919769, 'MAE': 108.07986153549697, 'SMAPE': 0.0498, 'ErrorMean': 24.844177961154166, 'ErrorStdDev': 129.10848552098523, 'R2': 0.9535987180626282, 'Pearson': 0.9773976998188207} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 44.9196355342865 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ACT_female' Length=59 Min=0 Max=36 Mean=12.40677966101695 StdDev=9.08550464829349 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_ACT_female' Min=3 Max=732 Mean=253.64406779661016 StdDev=242.26696038033666 @@ -454,8 +377,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_QLD_female_PolyTrend_residue_zeroCycle_residue_No INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0936 MAPE_Forecast=0.0936 MAPE_Test=0.0936 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0919 SMAPE_Forecast=0.0919 SMAPE_Test=0.0919 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8091 MASE_Forecast=0.8091 MASE_Test=0.8091 -INFO:pyaf.std:MODEL_L1 L1_Fit=24.287199542002263 L1_Forecast=24.287199542002263 L1_Test=24.287199542002263 -INFO:pyaf.std:MODEL_L2 L2_Fit=30.91781020777869 L2_Forecast=30.91781020777869 L2_Test=30.91781020777869 +INFO:pyaf.std:MODEL_L1 L1_Fit=24.287199542002256 L1_Forecast=24.287199542002256 L1_Test=24.287199542002256 +INFO:pyaf.std:MODEL_L2 L2_Fit=30.917810207778686 L2_Forecast=30.917810207778686 L2_Test=30.917810207778686 INFO:pyaf.std:MODEL_COMPLEXITY 16 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -504,14 +427,14 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Diff_SA_female_LinearTrend_residue_zeroCycle_resid INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1327 MAPE_Forecast=0.1327 MAPE_Test=0.1327 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1347 SMAPE_Forecast=0.1347 SMAPE_Test=0.1347 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9835 MASE_Forecast=0.9835 MASE_Test=0.9835 -INFO:pyaf.std:MODEL_L1 L1_Fit=17.68678476386571 L1_Forecast=17.68678476386571 L1_Test=17.68678476386571 -INFO:pyaf.std:MODEL_L2 L2_Fit=23.444143837822175 L2_Forecast=23.444143837822175 L2_Test=23.444143837822175 +INFO:pyaf.std:MODEL_L1 L1_Fit=17.686784763865706 L1_Forecast=17.686784763865706 L1_Test=17.686784763865706 +INFO:pyaf.std:MODEL_L2 L2_Fit=23.44414383782218 L2_Forecast=23.44414383782218 L2_Test=23.44414383782218 INFO:pyaf.std:MODEL_COMPLEXITY 48 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 129 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (2.7986988044365466, array([-8.47875354])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (2.7986988044365475, array([-8.47875354])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_SA_female_LinearTrend_residue_zeroCycle 0.0 {} @@ -769,49 +692,12 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ACT_female'), (0, 'ACT_male'), (0, 'NSW_female'), (0, 'NSW_male'), (0, 'NT_female'), (0, 'NT_male'), (0, 'QLD_female'), (0, 'QLD_male'), (0, 'SA_female'), (0, 'SA_male'), (0, 'TAS_female'), (0, 'TAS_male'), (0, 'VIC_female'), (0, 'VIC_male'), (0, 'WA_female'), (0, 'WA_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'ACT_female' -INFO:pyaf.std:START_FORECASTING 'ACT_male' -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_female' 0.8790206909179688 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_male' 0.8435342311859131 -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'NT_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.9474689960479736 -INFO:pyaf.std:START_FORECASTING 'NT_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 1.06292724609375 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_female' 0.945859432220459 -INFO:pyaf.std:START_FORECASTING 'QLD_female' -INFO:pyaf.std:START_FORECASTING 'QLD_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_male' 0.8006293773651123 -INFO:pyaf.std:START_FORECASTING 'SA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_female' 1.0219554901123047 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_male' 0.8723580837249756 -INFO:pyaf.std:START_FORECASTING 'SA_male' -INFO:pyaf.std:START_FORECASTING 'TAS_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_female' 1.004608392715454 -INFO:pyaf.std:START_FORECASTING 'TAS_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_male' 0.9387280941009521 -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_female' 1.0406041145324707 -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_male' 1.0021378993988037 -INFO:pyaf.std:START_FORECASTING 'WA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.9105844497680664 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.8692140579223633 -INFO:pyaf.std:START_FORECASTING 'WA_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_female' 0.8667910099029541 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_male' 0.6473915576934814 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.5610349178314209 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.4060986042022705 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.18082857131958008 +INFO:pyaf.std:START_FORECASTING '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' 4.867640256881714 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 8.089523553848267 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.993767261505127 diff --git a/tests/references/hierarchical_test_grouped_signals_AllMethods_Exogenous_all_nodes.log b/tests/references/hierarchical_test_grouped_signals_AllMethods_Exogenous_all_nodes.log index 9e3597552..1a958fb8b 100644 --- a/tests/references/hierarchical_test_grouped_signals_AllMethods_Exogenous_all_nodes.log +++ b/tests/references/hierarchical_test_grouped_signals_AllMethods_Exogenous_all_nodes.log @@ -1,83 +1,9 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ACT_female'), (0, 'ACT_male'), (0, 'NSW_female'), (0, 'NSW_male'), (0, 'NT_female'), (0, 'NT_male'), (0, 'QLD_female'), (0, 'QLD_male'), (0, 'SA_female'), (0, 'SA_male'), (0, 'TAS_female'), (0, 'TAS_male'), (0, 'VIC_female'), (0, 'VIC_male'), (0, 'WA_female'), (0, 'WA_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'ACT_male' -INFO:pyaf.std:START_TRAINING 'ACT_female' -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING 'SA_female' -INFO:pyaf.std:START_TRAINING 'NT_female' -INFO:pyaf.std:START_TRAINING 'QLD_female' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING 'SA_male' -INFO:pyaf.std:START_TRAINING 'TAS_female' -INFO:pyaf.std:START_TRAINING 'TAS_male' -INFO:pyaf.std:START_TRAINING 'QLD_male' -INFO:pyaf.std:START_TRAINING 'WA_male' -INFO:pyaf.std:START_TRAINING 'NT_male' -INFO:pyaf.std:START_TRAINING 'WA_female' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NT_male' 48.95932626724243 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ACT_female' 49.215774059295654 -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ACT_male' 49.25884532928467 -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NT_female' 49.50380349159241 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 50.097296953201294 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_male' 50.15251851081848 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'TAS_female' 50.247511863708496 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'WA_male' 50.24989128112793 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'TAS_male' 50.33355975151062 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'SA_female' 50.3639018535614 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'SA_male' 50.35729193687439 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_female' 50.731276988983154 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 50.93378520011902 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'WA_female' 50.80882716178894 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 50.967018842697144 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 50.88525438308716 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 9.293376684188843 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 9.877650737762451 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 10.076511859893799 +INFO:pyaf.std:START_TRAINING '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' 50.09645867347717 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ACT_female'), (0, 'ACT_male'), (0, 'NSW_female'), (0, 'NSW_male'), (0, 'NT_female'), (0, 'NT_male'), (0, 'QLD_female'), (0, 'QLD_male'), (0, 'SA_female'), (0, 'SA_male'), (0, 'TAS_female'), (0, 'TAS_male'), (0, 'VIC_female'), (0, 'VIC_male'), (0, 'WA_female'), (0, 'WA_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'ACT_female' -INFO:pyaf.std:START_FORECASTING 'ACT_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_female' 0.48482394218444824 -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_male' 0.6105804443359375 -INFO:pyaf.std:START_FORECASTING 'NT_female' -INFO:pyaf.std:START_FORECASTING 'NT_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 1.6053333282470703 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_female' 0.8476028442382812 -INFO:pyaf.std:START_FORECASTING 'QLD_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 1.3891575336456299 -INFO:pyaf.std:START_FORECASTING 'QLD_male' -INFO:pyaf.std:START_FORECASTING 'SA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_male' 1.024113416671753 -INFO:pyaf.std:START_FORECASTING 'SA_male' -INFO:pyaf.std:START_FORECASTING 'TAS_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_male' 1.451603651046753 -INFO:pyaf.std:START_FORECASTING 'TAS_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_female' 1.8783214092254639 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_female' 1.4401493072509766 -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_male' 1.2971045970916748 -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_female' 1.4902620315551758 -INFO:pyaf.std:START_FORECASTING 'WA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_male' 1.4174654483795166 -INFO:pyaf.std:START_FORECASTING 'WA_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 1.3164973258972168 -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 1.6990678310394287 -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_female' 1.5059328079223633 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_male' 1.089409589767456 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.8785552978515625 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.7141740322113037 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.4406702518463135 +INFO:pyaf.std:START_FORECASTING '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' 5.266618728637695 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -97,202 +23,199 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'ACT_female', 'ACT_female '_male_OC_Forecast', '__OC_Forecast'], dtype='object', length=172) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['_female', '_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 9434359302.0175, 'RMSE': 11.984091108883767, 'MAE': 10.802232415565705, 'SMAPE': 0.87, 'ErrorMean': 3.0017810598372217, 'ErrorStdDev': 11.602058014629538, 'R2': -0.739851149203288, 'Pearson': -0.48809664220945836} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 9434359302.0175, 'RMSE': 11.984091108883767, 'MAE': 10.802232415565705, 'SMAPE': 0.87, 'ErrorMean': 3.0017810598372217, 'ErrorStdDev': 11.602058014629538, 'R2': -0.739851149203288, 'Pearson': -0.48809664220945836} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_BU_Forecast', 'Length': 59, 'MAPE': 2.3483, 'RMSE': 36.07089270265762, 'MAE': 16.60413674231543, 'SMAPE': 1.8975, 'ErrorMean': -8.107727664464235, 'ErrorStdDev': 35.14788830762839, 'R2': -14.762157812852113, 'Pearson': -0.1359495955150412} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_BU_Forecast', 'Length': 59, 'MAPE': 2.3483, 'RMSE': 36.07089270265762, 'MAE': 16.60413674231543, 'SMAPE': 1.8975, 'ErrorMean': -8.107727664464235, 'ErrorStdDev': 35.14788830762839, 'R2': -14.762157812852113, 'Pearson': -0.1359495955150412} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_MO_Forecast', 'Length': 59, 'MAPE': 7652485341.7589, 'RMSE': 11.109136830520407, 'MAE': 10.040709649727075, 'SMAPE': 0.888, 'ErrorMean': 6.774242184153497e-16, 'ErrorStdDev': 11.109136830520407, 'R2': -0.4950734256362179, 'Pearson': -0.5103140991479994} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_MO_Forecast', 'Length': 59, 'MAPE': 7652485341.7589, 'RMSE': 11.109136830520407, 'MAE': 10.040709649727075, 'SMAPE': 0.888, 'ErrorMean': 6.774242184153497e-16, 'ErrorStdDev': 11.109136830520407, 'R2': -0.4950734256362179, 'Pearson': -0.5103140991479994} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_OC_Forecast', 'Length': 59, 'MAPE': 1624667245.7625, 'RMSE': 31.051186198009344, 'MAE': 14.823182843949692, 'SMAPE': 1.3267, 'ErrorMean': -7.122548437547021, 'ErrorStdDev': 30.22326038100857, 'R2': -10.680415209436168, 'Pearson': 0.015662517465273824} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_OC_Forecast', 'Length': 59, 'MAPE': 1624667245.7625, 'RMSE': 31.051186198009344, 'MAE': 14.823182843949692, 'SMAPE': 1.3267, 'ErrorMean': -7.122548437547021, 'ErrorStdDev': 30.22326038100857, 'R2': -10.680415209436168, 'Pearson': 0.015662517465273824} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 7596427675.7192, 'RMSE': 11.041385512052488, 'MAE': 9.966652192052766, 'SMAPE': 0.8841, 'ErrorMean': 4.2150840256955097e-16, 'ErrorStdDev': 11.041385512052488, 'R2': -0.4768930181373918, 'Pearson': -0.48809664220945836} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 7596427675.7192, 'RMSE': 11.041385512052488, 'MAE': 9.966652192052766, 'SMAPE': 0.8841, 'ErrorMean': 4.2150840256955097e-16, 'ErrorStdDev': 11.041385512052488, 'R2': -0.4768930181373918, 'Pearson': -0.48809664220945836} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 3.2052, 'RMSE': 17.47612542284065, 'MAE': 15.95462070192566, 'SMAPE': 0.8655, 'ErrorMean': 4.0744862272257185, 'ErrorStdDev': 16.994514461407988, 'R2': -0.6846081658668226, 'Pearson': -0.45831100182114964} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 3.2052, 'RMSE': 17.47612542284065, 'MAE': 15.95462070192566, 'SMAPE': 0.8655, 'ErrorMean': 4.0744862272257185, 'ErrorStdDev': 16.994514461407988, 'R2': -0.6846081658668226, 'Pearson': -0.45831100182114964} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_BU_Forecast', 'Length': 59, 'MAPE': 0.4944, 'RMSE': 6.911057217561346, 'MAE': 4.983050847457627, 'SMAPE': 0.3977, 'ErrorMean': -0.4067796610169492, 'ErrorStdDev': 6.899075457754446, 'R2': 0.7365503821922521, 'Pearson': 0.8706261722445393} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_BU_Forecast', 'Length': 59, 'MAPE': 0.4944, 'RMSE': 6.911057217561346, 'MAE': 4.983050847457627, 'SMAPE': 0.3977, 'ErrorMean': -0.4067796610169492, 'ErrorStdDev': 6.899075457754446, 'R2': 0.7365503821922521, 'Pearson': 0.8706261722445393} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_MO_Forecast', 'Length': 59, 'MAPE': 2.606, 'RMSE': 16.16836943641302, 'MAE': 14.890379680196547, 'SMAPE': 0.881, 'ErrorMean': 2.4989426723766236e-15, 'ErrorStdDev': 16.16836943641302, 'R2': -0.44191959476648357, 'Pearson': -0.43991121218379786} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_MO_Forecast', 'Length': 59, 'MAPE': 2.606, 'RMSE': 16.16836943641302, 'MAE': 14.890379680196547, 'SMAPE': 0.881, 'ErrorMean': 2.4989426723766236e-15, 'ErrorStdDev': 16.16836943641302, 'R2': -0.44191959476648357, 'Pearson': -0.43991121218379786} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_OC_Forecast', 'Length': 59, 'MAPE': 1.0133, 'RMSE': 10.743609831029222, 'MAE': 8.526048025256056, 'SMAPE': 0.6857, 'ErrorMean': -0.2847942800613066, 'ErrorStdDev': 10.739834468902774, 'R2': 0.36333782053280794, 'Pearson': 0.7654683227613968} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_OC_Forecast', 'Length': 59, 'MAPE': 1.0133, 'RMSE': 10.743609831029222, 'MAE': 8.526048025256056, 'SMAPE': 0.6857, 'ErrorMean': -0.2847942800613066, 'ErrorStdDev': 10.739834468902774, 'R2': 0.36333782053280794, 'Pearson': 0.7654683227613968} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 2.6155, 'RMSE': 16.254394803649692, 'MAE': 14.953217497710401, 'SMAPE': 0.8831, 'ErrorMean': 1.294632950749335e-15, 'ErrorStdDev': 16.254394803649692, 'R2': -0.4573041579362973, 'Pearson': -0.45831100182114964} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 2.6155, 'RMSE': 16.254394803649692, 'MAE': 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'SMAPE': 0.0574, 'ErrorMean': -1.9268955546036615e-15, 'ErrorStdDev': 47.947772338332626, 'R2': 0.9470323686573305, 'Pearson': 0.9731559166423152} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0577, 'RMSE': 47.947772338332626, 'MAE': 35.93260333101251, 'SMAPE': 0.0574, 'ErrorMean': -1.9268955546036615e-15, 'ErrorStdDev': 47.947772338332626, 'R2': 0.9470323686573305, 'Pearson': 0.9731559166423152} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0956, 'RMSE': 70.70423683711752, 'MAE': 57.74286145991784, 'SMAPE': 0.0933, 'ErrorMean': 4.624549331048788e-14, 'ErrorStdDev': 70.7042368371175, 'R2': 0.8848233186098543, 'Pearson': 0.9493498864158294} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0956, 'RMSE': 70.70423683711752, 'MAE': 57.74286145991784, 'SMAPE': 0.0933, 'ErrorMean': 4.624549331048788e-14, 'ErrorStdDev': 70.7042368371175, 'R2': 0.8848233186098543, 'Pearson': 0.9493498864158294} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0569, 'RMSE': 47.477061146644566, 'MAE': 35.697452858389006, 'SMAPE': 0.0565, 'ErrorMean': 0.9851792269137795, 'ErrorStdDev': 47.46683849818823, 'R2': 0.94806724776178, 'Pearson': 0.9737091221881718} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0569, 'RMSE': 47.477061146644566, 'MAE': 35.697452858389006, 'SMAPE': 0.0565, 'ErrorMean': 0.9851792269137795, 'ErrorStdDev': 47.46683849818823, 'R2': 0.94806724776178, 'Pearson': 0.9737091221881718} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0974, 'RMSE': 71.22419175856687, 'MAE': 58.615204622167916, 'SMAPE': 0.0953, 'ErrorMean': 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'ErrorStdDev': 78.27696323270679, 'R2': 0.9140019053343114, 'Pearson': 0.9644721222159928} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0551, 'RMSE': 61.51835606812159, 'MAE': 45.407981529272014, 'SMAPE': 0.0549, 'ErrorMean': -7.707582218414646e-15, 'ErrorStdDev': 61.51835606812159, 'R2': 0.9480136220774447, 'Pearson': 0.9736599578228081} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0551, 'RMSE': 61.51835606812159, 'MAE': 45.407981529272014, 'SMAPE': 0.0549, 'ErrorMean': -7.707582218414646e-15, 'ErrorStdDev': 61.51835606812159, 'R2': 0.9480136220774447, 'Pearson': 0.9736599578228081} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0779, 'RMSE': 77.4997645852685, 'MAE': 62.15367178914257, 'SMAPE': 0.0761, 'ErrorMean': 8.671029995716477e-15, 'ErrorStdDev': 77.4997645852685, 'R2': 0.9174948832306273, 'Pearson': 0.9643693565763027} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0779, 'RMSE': 77.4997645852685, 'MAE': 62.15367178914257, 'SMAPE': 0.0761, 'ErrorMean': 8.671029995716477e-15, 'ErrorStdDev': 77.4997645852685, 'R2': 0.9174948832306273, 'Pearson': 0.9643693565763027} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0529, 'RMSE': 59.900782198270555, 'MAE': 44.1385404160879, 'SMAPE': 0.0529, 'ErrorMean': 0.12198538095553674, 'ErrorStdDev': 59.90065798913632, 'R2': 0.9507115564780864, 'Pearson': 0.9750499078554643} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0529, 'RMSE': 59.900782198270555, 'MAE': 44.1385404160879, 'SMAPE': 0.0529, 'ErrorMean': 0.12198538095553674, 'ErrorStdDev': 59.90065798913632, 'R2': 0.9507115564780864, 'Pearson': 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-INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'TAS_male_OC_Forecast', 'Length': 59, 'MAPE': 0.1238, 'RMSE': 11.291305879772107, 'MAE': 8.63165155873183, 'SMAPE': 0.1258, 'ErrorMean': 0.12198538095695283, 'ErrorStdDev': 11.290646927320372, 'R2': 0.8611233104630458, 'Pearson': 0.929314107941252} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'TAS_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.1559, 'RMSE': 15.804559835932187, 'MAE': 12.127404484800367, 'SMAPE': 0.1472, 'ErrorMean': -1.144094235545924e-14, 'ErrorStdDev': 15.804559835932187, 'R2': 0.7279142352558932, 'Pearson': 0.8637028482803419} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'TAS_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.1559, 'RMSE': 15.804559835932187, 'MAE': 12.127404484800367, 'SMAPE': 0.1472, 'ErrorMean': -1.144094235545924e-14, 'ErrorStdDev': 15.804559835932187, 'R2': 0.7279142352558932, 'Pearson': 0.8637028482803419} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0804, 'RMSE': 41.466531087315786, 'MAE': 33.07618805009323, 'SMAPE': 0.0798, 'ErrorMean': -1.7920249777655808, 'ErrorStdDev': 41.427790755655685, 'R2': 0.8851354728912615, 'Pearson': 0.9415482828153523} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0804, 'RMSE': 41.466531087315786, 'MAE': 33.07618805009323, 'SMAPE': 0.0798, 'ErrorMean': -1.7920249777655808, 'ErrorStdDev': 41.427790755655685, 'R2': 0.8851354728912615, 'Pearson': 0.9415482828153523} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0806, 'RMSE': 40.44413460761881, 'MAE': 32.35037985502796, 'SMAPE': 0.0797, 'ErrorMean': 9.634477773018308e-16, 'ErrorStdDev': 40.44413460761881, 'R2': 0.8907298317134726, 'Pearson': 0.9437852282966261} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0806, 'RMSE': 40.44413460761881, 'MAE': 32.35037985502796, 'SMAPE': 0.0797, 'ErrorMean': 9.634477773018308e-16, 'ErrorStdDev': 40.44413460761881, 'R2': 0.8907298317134726, 'Pearson': 0.9437852282966261} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0822, 'RMSE': 41.763137878769015, 'MAE': 33.55819895030829, 'SMAPE': 0.0813, 'ErrorMean': -1.3006544993574715e-14, 'ErrorStdDev': 41.763137878769015, 'R2': 0.8834863623192742, 'Pearson': 0.9402894135412002} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0822, 'RMSE': 41.763137878769015, 'MAE': 33.55819895030829, 'SMAPE': 0.0813, 'ErrorMean': -1.3006544993574715e-14, 'ErrorStdDev': 41.763137878769015, 'R2': 0.8834863623192742, 'Pearson': 0.9402894135412002} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0802, 'RMSE': 40.0482603318187, 'MAE': 32.44150304563892, 'SMAPE': 0.0793, 'ErrorMean': 0.9851792269137077, 'ErrorStdDev': 40.03614089164915, 'R2': 0.8928584738213932, 'Pearson': 0.9449486655295081} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0802, 'RMSE': 40.0482603318187, 'MAE': 32.44150304563892, 'SMAPE': 0.0793, 'ErrorMean': 0.9851792269137077, 'ErrorStdDev': 40.03614089164915, 'R2': 0.8928584738213932, 'Pearson': 0.9449486655295081} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0812, 'RMSE': 41.38316169806692, 'MAE': 33.231830437186474, 'SMAPE': 0.0803, 'ErrorMean': -7.22585832976373e-15, 'ErrorStdDev': 41.38316169806692, 'R2': 0.8855968839933193, 'Pearson': 0.9415482828153523} 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L2_Test=47.947772338332626 +INFO:pyaf.std:MODEL_L1 L1_Fit=35.9326033310125 L1_Forecast=35.9326033310125 L1_Test=35.9326033310125 +INFO:pyaf.std:MODEL_L2 L2_Fit=47.94777233833262 L2_Forecast=47.94777233833262 L2_Test=47.94777233833262 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -368,16 +291,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _NSW_female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -15.63466970142157 -INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -10.936582346474259 -INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag8 -10.906642804004694 -INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag7 -8.151592965020514 -INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag9 -6.2424275945569585 -INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag12 -5.757512048285289 -INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag6 -3.8711556210898572 +INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -15.634669701421604 +INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -10.936582346474204 +INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag8 -10.906642804004552 +INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag7 -8.151592965020615 +INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag9 -6.242427594556911 +INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag12 -5.7575120482852995 +INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag6 -3.8711556210899314 INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag10 -3.0787487237180438 -INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag3 -2.8682311101621556 -INFO:pyaf.std:AR_MODEL_COEFF 10 Index_ex1_Lag4 -1.0498659254061606 +INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag3 -2.868231110162103 +INFO:pyaf.std:AR_MODEL_COEFF 10 Index_ex1_Lag4 -1.0498659254062543 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_male' Length=59 Min=349 Max=1264 Mean=881.8305084745763 StdDev=269.81118021227 @@ -391,8 +314,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_NSW_male_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0551 MAPE_Forecast=0.0551 MAPE_Test=0.0551 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0549 SMAPE_Forecast=0.0549 SMAPE_Test=0.0549 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8197 MASE_Forecast=0.8197 MASE_Test=0.8197 -INFO:pyaf.std:MODEL_L1 L1_Fit=45.407981529272014 L1_Forecast=45.407981529272014 L1_Test=45.407981529272014 -INFO:pyaf.std:MODEL_L2 L2_Fit=61.51835606812159 L2_Forecast=61.51835606812159 L2_Test=61.51835606812159 +INFO:pyaf.std:MODEL_L1 L1_Fit=45.40798152927201 L1_Forecast=45.40798152927201 L1_Test=45.40798152927201 +INFO:pyaf.std:MODEL_L2 L2_Fit=61.5183560681216 L2_Forecast=61.5183560681216 L2_Test=61.5183560681216 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -404,16 +327,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _NSW_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -24.148581310687305 -INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -21.169661621016974 -INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag12 -11.27364181077605 -INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag8 -7.701242584764591 -INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag9 -4.882457268554943 -INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag11 -3.9266345808900627 -INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag10 -2.2820077608745604 -INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag5 0.8178688320970016 -INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag6 0.6245536238592564 -INFO:pyaf.std:AR_MODEL_COEFF 10 _NSW_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6066959330079077 +INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -24.148581310687227 +INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -21.169661621016946 +INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag12 -11.273641810776045 +INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag8 -7.701242584764632 +INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag9 -4.882457268554938 +INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag11 -3.926634580890075 +INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag10 -2.2820077608745293 +INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag5 0.8178688320970373 +INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag6 0.6245536238593346 +INFO:pyaf.std:AR_MODEL_COEFF 10 _NSW_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6066959330079078 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NT_female' Length=59 Min=1 Max=82 Mean=18.305084745762713 StdDev=17.728894578030516 @@ -477,8 +400,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_QLD_female_ConstantTrend_residue_zeroCycle_residu INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0828 MAPE_Forecast=0.0828 MAPE_Test=0.0828 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0823 SMAPE_Forecast=0.0823 SMAPE_Test=0.0823 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7534 MASE_Forecast=0.7534 MASE_Test=0.7534 -INFO:pyaf.std:MODEL_L1 L1_Fit=22.61386594315741 L1_Forecast=22.61386594315741 L1_Test=22.61386594315741 -INFO:pyaf.std:MODEL_L2 L2_Fit=28.868927255460548 L2_Forecast=28.868927255460548 L2_Test=28.868927255460548 +INFO:pyaf.std:MODEL_L1 L1_Fit=22.613865943157418 L1_Forecast=22.613865943157418 L1_Test=22.613865943157418 +INFO:pyaf.std:MODEL_L2 L2_Fit=28.868927255460555 L2_Forecast=28.868927255460555 L2_Test=28.868927255460555 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -490,16 +413,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _QLD_female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -20.563230833086664 -INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -16.897388644133553 -INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag3 15.936955831144719 -INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag12 -13.417986292717217 -INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag4 11.319830322607729 -INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag11 -10.542883688111413 -INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag10 -9.126832526695605 -INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag5 7.20782913573324 -INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag6 5.251171315575856 -INFO:pyaf.std:AR_MODEL_COEFF 10 Index_ex1_Lag9 -4.792179622854173 +INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -20.563230833086653 +INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -16.89738864413351 +INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag3 15.936955831144706 +INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag12 -13.417986292717277 +INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag4 11.319830322607743 +INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag11 -10.542883688111406 +INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag10 -9.126832526695658 +INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag5 7.207829135733248 +INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag6 5.251171315575942 +INFO:pyaf.std:AR_MODEL_COEFF 10 Index_ex1_Lag9 -4.792179622854163 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='QLD_male' Length=59 Min=182 Max=493 Mean=360.6271186440678 StdDev=90.19881962192916 @@ -513,7 +436,7 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0646 MAPE_Forecast=0.0646 MAPE_Test=0.0646 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0641 SMAPE_Forecast=0.0641 SMAPE_Test=0.0641 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9026 MASE_Forecast=0.9026 MASE_Test=0.9026 INFO:pyaf.std:MODEL_L1 L1_Fit=21.614584201956077 L1_Forecast=21.614584201956077 L1_Test=21.614584201956077 -INFO:pyaf.std:MODEL_L2 L2_Fit=27.688951982547675 L2_Forecast=27.688951982547675 L2_Test=27.688951982547675 +INFO:pyaf.std:MODEL_L2 L2_Fit=27.688951982547678 L2_Forecast=27.688951982547678 L2_Test=27.688951982547678 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -525,16 +448,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _QLD_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8648492231952413 -INFO:pyaf.std:AR_MODEL_COEFF 2 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag9 0.23708437703604907 -INFO:pyaf.std:AR_MODEL_COEFF 3 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.21071148770757236 -INFO:pyaf.std:AR_MODEL_COEFF 4 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.16218172139942713 -INFO:pyaf.std:AR_MODEL_COEFF 5 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag7 0.14907363231129328 -INFO:pyaf.std:AR_MODEL_COEFF 6 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.1366169523636399 -INFO:pyaf.std:AR_MODEL_COEFF 7 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1211880748980526 -INFO:pyaf.std:AR_MODEL_COEFF 8 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.11380825652716393 -INFO:pyaf.std:AR_MODEL_COEFF 9 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.095741941756509 -INFO:pyaf.std:AR_MODEL_COEFF 10 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag10 0.07366132033171875 +INFO:pyaf.std:AR_MODEL_COEFF 1 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.864849223195242 +INFO:pyaf.std:AR_MODEL_COEFF 2 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag9 0.23708437703604882 +INFO:pyaf.std:AR_MODEL_COEFF 3 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.21071148770757234 +INFO:pyaf.std:AR_MODEL_COEFF 4 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.16218172139942705 +INFO:pyaf.std:AR_MODEL_COEFF 5 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag7 0.14907363231129314 +INFO:pyaf.std:AR_MODEL_COEFF 6 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.1366169523636397 +INFO:pyaf.std:AR_MODEL_COEFF 7 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.12118807489805285 +INFO:pyaf.std:AR_MODEL_COEFF 8 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.11380825652716445 +INFO:pyaf.std:AR_MODEL_COEFF 9 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.0957419417565096 +INFO:pyaf.std:AR_MODEL_COEFF 10 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag10 0.07366132033171885 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='SA_female' Length=59 Min=44 Max=228 Mean=136.5084745762712 StdDev=45.44893684548569 @@ -547,7 +470,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '_SA_female_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.104 MAPE_Forecast=0.104 MAPE_Test=0.104 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1013 SMAPE_Forecast=0.1013 SMAPE_Test=0.1013 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7186 MASE_Forecast=0.7186 MASE_Test=0.7186 -INFO:pyaf.std:MODEL_L1 L1_Fit=12.92176973795644 L1_Forecast=12.92176973795644 L1_Test=12.92176973795644 +INFO:pyaf.std:MODEL_L1 L1_Fit=12.921769737956442 L1_Forecast=12.921769737956442 L1_Test=12.921769737956442 INFO:pyaf.std:MODEL_L2 L2_Fit=17.370820628842115 L2_Forecast=17.370820628842115 L2_Test=17.370820628842115 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -561,15 +484,15 @@ INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _SA_female_ConstantTrend_residue_zeroCycle INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_COEFF 1 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5453673575811728 -INFO:pyaf.std:AR_MODEL_COEFF 2 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag13 0.4017295404277731 -INFO:pyaf.std:AR_MODEL_COEFF 3 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2856147369742006 -INFO:pyaf.std:AR_MODEL_COEFF 4 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.2803989193136852 -INFO:pyaf.std:AR_MODEL_COEFF 5 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.26790264216672766 -INFO:pyaf.std:AR_MODEL_COEFF 6 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag3 0.22277580975372685 -INFO:pyaf.std:AR_MODEL_COEFF 7 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.17775814359413541 -INFO:pyaf.std:AR_MODEL_COEFF 8 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.17488459291577818 -INFO:pyaf.std:AR_MODEL_COEFF 9 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.15925222207723772 -INFO:pyaf.std:AR_MODEL_COEFF 10 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15769089703537997 +INFO:pyaf.std:AR_MODEL_COEFF 2 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag13 0.40172954042777265 +INFO:pyaf.std:AR_MODEL_COEFF 3 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2856147369742007 +INFO:pyaf.std:AR_MODEL_COEFF 4 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.28039891931368505 +INFO:pyaf.std:AR_MODEL_COEFF 5 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.2679026421667279 +INFO:pyaf.std:AR_MODEL_COEFF 6 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag3 0.22277580975372663 +INFO:pyaf.std:AR_MODEL_COEFF 7 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.17775814359413544 +INFO:pyaf.std:AR_MODEL_COEFF 8 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.1748845929157778 +INFO:pyaf.std:AR_MODEL_COEFF 9 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.1592522220772378 +INFO:pyaf.std:AR_MODEL_COEFF 10 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15769089703537958 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='SA_male' Length=59 Min=66 Max=272 Mean=185.0677966101695 StdDev=57.82230652986103 @@ -582,8 +505,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_SA_male_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0928 MAPE_Forecast=0.0928 MAPE_Test=0.0928 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0901 SMAPE_Forecast=0.0901 SMAPE_Test=0.0901 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7726 MASE_Forecast=0.7726 MASE_Test=0.7726 -INFO:pyaf.std:MODEL_L1 L1_Fit=14.986417254763131 L1_Forecast=14.986417254763131 L1_Test=14.986417254763131 -INFO:pyaf.std:MODEL_L2 L2_Fit=19.770518466923754 L2_Forecast=19.770518466923754 L2_Test=19.770518466923754 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.98641725476313 L1_Forecast=14.98641725476313 L1_Test=14.98641725476313 +INFO:pyaf.std:MODEL_L2 L2_Fit=19.77051846692376 L2_Forecast=19.77051846692376 L2_Test=19.77051846692376 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -595,16 +518,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _SA_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.604707348002017 -INFO:pyaf.std:AR_MODEL_COEFF 2 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.34195613383262735 -INFO:pyaf.std:AR_MODEL_COEFF 3 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.2848939210087596 -INFO:pyaf.std:AR_MODEL_COEFF 4 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag6 0.27611740019096764 -INFO:pyaf.std:AR_MODEL_COEFF 5 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag12 0.2400440546073953 -INFO:pyaf.std:AR_MODEL_COEFF 6 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.19507066501850545 -INFO:pyaf.std:AR_MODEL_COEFF 7 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.1930852122328221 -INFO:pyaf.std:AR_MODEL_COEFF 8 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag9 0.19272405571306916 +INFO:pyaf.std:AR_MODEL_COEFF 1 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6047073480020169 +INFO:pyaf.std:AR_MODEL_COEFF 2 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.34195613383262746 +INFO:pyaf.std:AR_MODEL_COEFF 3 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.2848939210087595 +INFO:pyaf.std:AR_MODEL_COEFF 4 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag6 0.2761174001909678 +INFO:pyaf.std:AR_MODEL_COEFF 5 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag12 0.24004405460739547 +INFO:pyaf.std:AR_MODEL_COEFF 6 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.19507066501850584 +INFO:pyaf.std:AR_MODEL_COEFF 7 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19308521223282227 +INFO:pyaf.std:AR_MODEL_COEFF 8 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag9 0.19272405571306941 INFO:pyaf.std:AR_MODEL_COEFF 9 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.1912679241101395 -INFO:pyaf.std:AR_MODEL_COEFF 10 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.16885727457642066 +INFO:pyaf.std:AR_MODEL_COEFF 10 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.16885727457642036 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='TAS_female' Length=59 Min=29 Max=151 Mean=84.38983050847457 StdDev=30.29908369658443 @@ -618,7 +541,7 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1063 MAPE_Forecast=0.1063 MAPE_Test=0.1063 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1029 SMAPE_Forecast=0.1029 SMAPE_Test=0.1029 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7058 MASE_Forecast=0.7058 MASE_Test=0.7058 INFO:pyaf.std:MODEL_L1 L1_Fit=8.092932844154943 L1_Forecast=8.092932844154943 L1_Test=8.092932844154943 -INFO:pyaf.std:MODEL_L2 L2_Fit=11.425753177213439 L2_Forecast=11.425753177213439 L2_Test=11.425753177213439 +INFO:pyaf.std:MODEL_L2 L2_Fit=11.425753177213437 L2_Forecast=11.425753177213437 L2_Test=11.425753177213437 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -630,16 +553,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _TAS_female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227048404991295 -INFO:pyaf.std:AR_MODEL_COEFF 2 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.32113163141839896 -INFO:pyaf.std:AR_MODEL_COEFF 3 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag8 0.24969045944455115 -INFO:pyaf.std:AR_MODEL_COEFF 4 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag11 0.2342381505268491 +INFO:pyaf.std:AR_MODEL_COEFF 1 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227048404991296 +INFO:pyaf.std:AR_MODEL_COEFF 2 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.3211316314183985 +INFO:pyaf.std:AR_MODEL_COEFF 3 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag8 0.24969045944455107 +INFO:pyaf.std:AR_MODEL_COEFF 4 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag11 0.23423815052684888 INFO:pyaf.std:AR_MODEL_COEFF 5 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag4 0.2167050880675857 -INFO:pyaf.std:AR_MODEL_COEFF 6 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.19315737228095625 -INFO:pyaf.std:AR_MODEL_COEFF 7 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.18530510150962975 -INFO:pyaf.std:AR_MODEL_COEFF 8 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.15746739400714707 -INFO:pyaf.std:AR_MODEL_COEFF 9 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06932740432823306 -INFO:pyaf.std:AR_MODEL_COEFF 10 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.06892795192428368 +INFO:pyaf.std:AR_MODEL_COEFF 6 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.19315737228095586 +INFO:pyaf.std:AR_MODEL_COEFF 7 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1853051015096298 +INFO:pyaf.std:AR_MODEL_COEFF 8 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.1574673940071472 +INFO:pyaf.std:AR_MODEL_COEFF 9 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06932740432823326 +INFO:pyaf.std:AR_MODEL_COEFF 10 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.06892795192428333 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='TAS_male' Length=59 Min=29 Max=151 Mean=84.38983050847457 StdDev=30.29908369658443 @@ -653,7 +576,7 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1063 MAPE_Forecast=0.1063 MAPE_Test=0.1063 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1029 SMAPE_Forecast=0.1029 SMAPE_Test=0.1029 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7058 MASE_Forecast=0.7058 MASE_Test=0.7058 INFO:pyaf.std:MODEL_L1 L1_Fit=8.092932844154943 L1_Forecast=8.092932844154943 L1_Test=8.092932844154943 -INFO:pyaf.std:MODEL_L2 L2_Fit=11.425753177213439 L2_Forecast=11.425753177213439 L2_Test=11.425753177213439 +INFO:pyaf.std:MODEL_L2 L2_Fit=11.425753177213437 L2_Forecast=11.425753177213437 L2_Test=11.425753177213437 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -665,16 +588,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _TAS_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227048404991295 -INFO:pyaf.std:AR_MODEL_COEFF 2 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.32113163141839896 -INFO:pyaf.std:AR_MODEL_COEFF 3 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.24969045944455115 -INFO:pyaf.std:AR_MODEL_COEFF 4 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.2342381505268491 +INFO:pyaf.std:AR_MODEL_COEFF 1 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227048404991296 +INFO:pyaf.std:AR_MODEL_COEFF 2 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.3211316314183985 +INFO:pyaf.std:AR_MODEL_COEFF 3 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.24969045944455107 +INFO:pyaf.std:AR_MODEL_COEFF 4 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.23423815052684888 INFO:pyaf.std:AR_MODEL_COEFF 5 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.2167050880675857 -INFO:pyaf.std:AR_MODEL_COEFF 6 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.19315737228095625 -INFO:pyaf.std:AR_MODEL_COEFF 7 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.18530510150962975 -INFO:pyaf.std:AR_MODEL_COEFF 8 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.15746739400714707 -INFO:pyaf.std:AR_MODEL_COEFF 9 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06932740432823306 -INFO:pyaf.std:AR_MODEL_COEFF 10 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.06892795192428368 +INFO:pyaf.std:AR_MODEL_COEFF 6 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.19315737228095586 +INFO:pyaf.std:AR_MODEL_COEFF 7 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1853051015096298 +INFO:pyaf.std:AR_MODEL_COEFF 8 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.1574673940071472 +INFO:pyaf.std:AR_MODEL_COEFF 9 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06932740432823326 +INFO:pyaf.std:AR_MODEL_COEFF 10 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.06892795192428333 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='VIC_female' Length=59 Min=174 Max=656 Mean=425.8135593220339 StdDev=122.35021622116214 @@ -688,8 +611,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_VIC_female_ConstantTrend_residue_zeroCycle_residu INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0806 MAPE_Forecast=0.0806 MAPE_Test=0.0806 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0797 SMAPE_Forecast=0.0797 SMAPE_Test=0.0797 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8851 MASE_Forecast=0.8851 MASE_Test=0.8851 -INFO:pyaf.std:MODEL_L1 L1_Fit=32.35037985502796 L1_Forecast=32.35037985502796 L1_Test=32.35037985502796 -INFO:pyaf.std:MODEL_L2 L2_Fit=40.44413460761881 L2_Forecast=40.44413460761881 L2_Test=40.44413460761881 +INFO:pyaf.std:MODEL_L1 L1_Fit=32.35037985502795 L1_Forecast=32.35037985502795 L1_Test=32.35037985502795 +INFO:pyaf.std:MODEL_L2 L2_Fit=40.4441346076188 L2_Forecast=40.4441346076188 L2_Test=40.4441346076188 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -701,16 +624,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _VIC_female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag4 11.785246305849155 -INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag14 -11.27046522768216 -INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag13 -10.22753210014253 -INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag9 -10.090418538739225 -INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag5 8.953602953218365 -INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag10 -6.897979907328973 -INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag8 -6.373687822537115 -INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag12 -5.455881843882843 -INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag11 -4.711377683711328 -INFO:pyaf.std:AR_MODEL_COEFF 10 Index_ex1_Lag7 -3.099604434705839 +INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag4 11.785246305849148 +INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag14 -11.270465227682191 +INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag13 -10.227532100142597 +INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag9 -10.090418538739234 +INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag5 8.953602953218416 +INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag10 -6.897979907328959 +INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag8 -6.3736878225371205 +INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag12 -5.455881843882838 +INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag11 -4.711377683711345 +INFO:pyaf.std:AR_MODEL_COEFF 10 Index_ex1_Lag7 -3.0996044347058143 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='VIC_male' Length=59 Min=253 Max=841 Mean=560.271186440678 StdDev=158.16756085042223 @@ -723,8 +646,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_VIC_male_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0688 MAPE_Forecast=0.0688 MAPE_Test=0.0688 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.068 SMAPE_Forecast=0.068 SMAPE_Test=0.068 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8329 MASE_Forecast=0.8329 MASE_Test=0.8329 -INFO:pyaf.std:MODEL_L1 L1_Fit=36.94765351012386 L1_Forecast=36.94765351012386 L1_Test=36.94765351012386 -INFO:pyaf.std:MODEL_L2 L2_Fit=48.8376764851016 L2_Forecast=48.8376764851016 L2_Test=48.8376764851016 +INFO:pyaf.std:MODEL_L1 L1_Fit=36.94765351012385 L1_Forecast=36.94765351012385 L1_Test=36.94765351012385 +INFO:pyaf.std:MODEL_L2 L2_Fit=48.837676485101596 L2_Forecast=48.837676485101596 L2_Test=48.837676485101596 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -736,16 +659,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _VIC_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7521611446403602 -INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.2152519372602766 -INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.1896132378195106 -INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.18039210983774107 -INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.13698326164447133 +INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7521611446403604 +INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.2152519372602765 +INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.18961323781951078 +INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.18039210983774068 +INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.136983261644471 INFO:pyaf.std:AR_MODEL_COEFF 6 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.13079176706466694 -INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.10710782159578139 -INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.10446377424332748 -INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.10423150622570512 -INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag10 0.08746134284394676 +INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.10710782159578129 +INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.10446377424332774 +INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1042315062257052 +INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag10 0.08746134284394719 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='WA_female' Length=59 Min=74 Max=210 Mean=137.76271186440678 StdDev=34.12556027133081 @@ -759,7 +682,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '_WA_female_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1095 MAPE_Forecast=0.1095 MAPE_Test=0.1095 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1079 SMAPE_Forecast=0.1079 SMAPE_Test=0.1079 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8512 MASE_Forecast=0.8512 MASE_Test=0.8512 -INFO:pyaf.std:MODEL_L1 L1_Fit=14.955443412842484 L1_Forecast=14.955443412842484 L1_Test=14.955443412842484 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.955443412842486 L1_Forecast=14.955443412842486 L1_Test=14.955443412842486 INFO:pyaf.std:MODEL_L2 L2_Fit=18.738341885187562 L2_Forecast=18.738341885187562 L2_Test=18.738341885187562 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -772,16 +695,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _WA_female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -8.291421494694491 -INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -6.110150680651397 -INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag5 2.994167887163168 -INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag12 -2.672195409746049 -INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag6 1.356390107204002 -INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag8 1.2213781650659774 -INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag7 1.1285188628749334 -INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag11 -1.045346324402304 -INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag10 0.7944244043631794 -INFO:pyaf.std:AR_MODEL_COEFF 10 _WA_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5850039700745221 +INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -8.291421494694521 +INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -6.110150680651417 +INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag5 2.994167887163127 +INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag12 -2.6721954097460157 +INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag6 1.3563901072039957 +INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag8 1.221378165065988 +INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag7 1.1285188628749574 +INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag11 -1.04534632440227 +INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag10 0.794424404363195 +INFO:pyaf.std:AR_MODEL_COEFF 10 _WA_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.585003970074522 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='WA_male' Length=59 Min=101 Max=265 Mean=183.864406779661 StdDev=42.12550160728105 @@ -794,7 +717,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '_WA_male_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0726 MAPE_Forecast=0.0726 MAPE_Test=0.0726 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0719 SMAPE_Forecast=0.0719 SMAPE_Test=0.0719 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7907 MASE_Test=0.7907 -INFO:pyaf.std:MODEL_L1 L1_Fit=13.23766848262696 L1_Forecast=13.23766848262696 L1_Test=13.23766848262696 +INFO:pyaf.std:MODEL_L1 L1_Fit=13.237668482626963 L1_Forecast=13.237668482626963 L1_Test=13.237668482626963 INFO:pyaf.std:MODEL_L2 L2_Fit=16.238015085935594 L2_Forecast=16.238015085935594 L2_Test=16.238015085935594 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -807,16 +730,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _WA_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7996235232042755 -INFO:pyaf.std:AR_MODEL_COEFF 2 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.5588385080467538 -INFO:pyaf.std:AR_MODEL_COEFF 3 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.41861748163626805 -INFO:pyaf.std:AR_MODEL_COEFF 4 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag6 0.4081675178697879 -INFO:pyaf.std:AR_MODEL_COEFF 5 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.3116477307385535 -INFO:pyaf.std:AR_MODEL_COEFF 6 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.29665681240386343 -INFO:pyaf.std:AR_MODEL_COEFF 7 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.2730666311154444 -INFO:pyaf.std:AR_MODEL_COEFF 8 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.2071665281706015 -INFO:pyaf.std:AR_MODEL_COEFF 9 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.07498943285909025 -INFO:pyaf.std:AR_MODEL_COEFF 10 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag14 0.07494965524931987 +INFO:pyaf.std:AR_MODEL_COEFF 1 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7996235232042754 +INFO:pyaf.std:AR_MODEL_COEFF 2 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.5588385080467542 +INFO:pyaf.std:AR_MODEL_COEFF 3 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.4186174816362683 +INFO:pyaf.std:AR_MODEL_COEFF 4 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag6 0.408167517869788 +INFO:pyaf.std:AR_MODEL_COEFF 5 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.3116477307385538 +INFO:pyaf.std:AR_MODEL_COEFF 6 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.296656812403863 +INFO:pyaf.std:AR_MODEL_COEFF 7 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.2730666311154446 +INFO:pyaf.std:AR_MODEL_COEFF 8 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.20716652817060094 +INFO:pyaf.std:AR_MODEL_COEFF 9 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.07498943285909018 +INFO:pyaf.std:AR_MODEL_COEFF 10 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag14 0.07494965524932029 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='_female' Length=59 Min=802 Max=2387 Mean=1738.3728813559321 StdDev=469.42741531319837 @@ -829,7 +752,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '__female_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0514 MAPE_Forecast=0.0514 MAPE_Test=0.0514 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0513 SMAPE_Forecast=0.0513 SMAPE_Test=0.0513 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.921 MASE_Forecast=0.921 MASE_Test=0.921 -INFO:pyaf.std:MODEL_L1 L1_Fit=86.98718002042585 L1_Forecast=86.98718002042585 L1_Test=86.98718002042585 +INFO:pyaf.std:MODEL_L1 L1_Fit=86.98718002042588 L1_Forecast=86.98718002042588 L1_Test=86.98718002042588 INFO:pyaf.std:MODEL_L2 L2_Fit=107.9116048164168 L2_Forecast=107.9116048164168 L2_Test=107.9116048164168 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -842,16 +765,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES __female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 __female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9213203396536638 -INFO:pyaf.std:AR_MODEL_COEFF 2 __female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.2858209060602198 -INFO:pyaf.std:AR_MODEL_COEFF 3 __female_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.22135280821648448 -INFO:pyaf.std:AR_MODEL_COEFF 4 __female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.20586177528831912 -INFO:pyaf.std:AR_MODEL_COEFF 5 __female_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.17094489575852562 -INFO:pyaf.std:AR_MODEL_COEFF 6 __female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1408492153777583 -INFO:pyaf.std:AR_MODEL_COEFF 7 __female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.12352902588521011 -INFO:pyaf.std:AR_MODEL_COEFF 8 __female_ConstantTrend_residue_zeroCycle_residue_Lag14 0.0886704953512847 -INFO:pyaf.std:AR_MODEL_COEFF 9 __female_ConstantTrend_residue_zeroCycle_residue_Lag12 0.0777468168089054 -INFO:pyaf.std:AR_MODEL_COEFF 10 __female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.06635748168640254 +INFO:pyaf.std:AR_MODEL_COEFF 1 __female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9213203396536637 +INFO:pyaf.std:AR_MODEL_COEFF 2 __female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.2858209060602196 +INFO:pyaf.std:AR_MODEL_COEFF 3 __female_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.22135280821648465 +INFO:pyaf.std:AR_MODEL_COEFF 4 __female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.20586177528831962 +INFO:pyaf.std:AR_MODEL_COEFF 5 __female_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.17094489575852512 +INFO:pyaf.std:AR_MODEL_COEFF 6 __female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.14084921537775785 +INFO:pyaf.std:AR_MODEL_COEFF 7 __female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.12352902588520995 +INFO:pyaf.std:AR_MODEL_COEFF 8 __female_ConstantTrend_residue_zeroCycle_residue_Lag14 0.08867049535128513 +INFO:pyaf.std:AR_MODEL_COEFF 9 __female_ConstantTrend_residue_zeroCycle_residue_Lag12 0.07774681680890559 +INFO:pyaf.std:AR_MODEL_COEFF 10 __female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.06635748168640271 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='_male' Length=59 Min=1049 Max=3096 Mean=2297.271186440678 StdDev=610.3587950224886 @@ -864,8 +787,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '__male_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0424 MAPE_Forecast=0.0424 MAPE_Test=0.0424 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0422 SMAPE_Forecast=0.0422 SMAPE_Test=0.0422 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8265 MASE_Forecast=0.8265 MASE_Test=0.8265 -INFO:pyaf.std:MODEL_L1 L1_Fit=90.69885284059069 L1_Forecast=90.69885284059069 L1_Test=90.69885284059069 -INFO:pyaf.std:MODEL_L2 L2_Fit=116.08593586971591 L2_Forecast=116.08593586971591 L2_Test=116.08593586971591 +INFO:pyaf.std:MODEL_L1 L1_Fit=90.69885284059066 L1_Forecast=90.69885284059066 L1_Test=90.69885284059066 +INFO:pyaf.std:MODEL_L2 L2_Fit=116.08593586971588 L2_Forecast=116.08593586971588 L2_Test=116.08593586971588 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -877,16 +800,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES __male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 __male_ConstantTrend_residue_zeroCycle_residue_Lag1 1.1036312001347572 -INFO:pyaf.std:AR_MODEL_COEFF 2 __male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.7142791160229571 -INFO:pyaf.std:AR_MODEL_COEFF 3 __male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.4095631444640226 +INFO:pyaf.std:AR_MODEL_COEFF 1 __male_ConstantTrend_residue_zeroCycle_residue_Lag1 1.103631200134757 +INFO:pyaf.std:AR_MODEL_COEFF 2 __male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.7142791160229567 +INFO:pyaf.std:AR_MODEL_COEFF 3 __male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.4095631444640224 INFO:pyaf.std:AR_MODEL_COEFF 4 __male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.38246023653185773 -INFO:pyaf.std:AR_MODEL_COEFF 5 __male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.32541893587583104 +INFO:pyaf.std:AR_MODEL_COEFF 5 __male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.3254189358758301 INFO:pyaf.std:AR_MODEL_COEFF 6 __male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.3052699089937121 -INFO:pyaf.std:AR_MODEL_COEFF 7 __male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.29975532955819195 -INFO:pyaf.std:AR_MODEL_COEFF 8 __male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.2735114915064893 -INFO:pyaf.std:AR_MODEL_COEFF 9 __male_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.2650821046838131 -INFO:pyaf.std:AR_MODEL_COEFF 10 __male_ConstantTrend_residue_zeroCycle_residue_Lag7 0.2445551571357526 +INFO:pyaf.std:AR_MODEL_COEFF 7 __male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.29975532955819073 +INFO:pyaf.std:AR_MODEL_COEFF 8 __male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.2735114915064889 +INFO:pyaf.std:AR_MODEL_COEFF 9 __male_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.2650821046838129 +INFO:pyaf.std:AR_MODEL_COEFF 10 __male_ConstantTrend_residue_zeroCycle_residue_Lag7 0.24455515713575224 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='_' Length=59 Min=1851 Max=5458 Mean=4035.64406779661 StdDev=1078.2170587848439 @@ -899,8 +822,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '___ConstantTrend_residue_zeroCycle_residue_AR(14)' INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0422 MAPE_Forecast=0.0422 MAPE_Test=0.0422 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0421 SMAPE_Forecast=0.0421 SMAPE_Test=0.0421 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8393 MASE_Forecast=0.8393 MASE_Test=0.8393 -INFO:pyaf.std:MODEL_L1 L1_Fit=163.73171242584746 L1_Forecast=163.73171242584746 L1_Test=163.73171242584746 -INFO:pyaf.std:MODEL_L2 L2_Fit=214.36140356957702 L2_Forecast=214.36140356957702 L2_Test=214.36140356957702 +INFO:pyaf.std:MODEL_L1 L1_Fit=163.73171242584732 L1_Forecast=163.73171242584732 L1_Test=163.73171242584732 +INFO:pyaf.std:MODEL_L2 L2_Fit=214.3614035695769 L2_Forecast=214.3614035695769 L2_Test=214.3614035695769 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -912,61 +835,24 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES ___ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 ___ConstantTrend_residue_zeroCycle_residue_Lag1 1.0764495872616955 -INFO:pyaf.std:AR_MODEL_COEFF 2 ___ConstantTrend_residue_zeroCycle_residue_Lag6 -0.42126843929652724 -INFO:pyaf.std:AR_MODEL_COEFF 3 ___ConstantTrend_residue_zeroCycle_residue_Lag13 -0.32104330281177346 -INFO:pyaf.std:AR_MODEL_COEFF 4 ___ConstantTrend_residue_zeroCycle_residue_Lag5 0.29158385125791114 +INFO:pyaf.std:AR_MODEL_COEFF 1 ___ConstantTrend_residue_zeroCycle_residue_Lag1 1.0764495872616957 +INFO:pyaf.std:AR_MODEL_COEFF 2 ___ConstantTrend_residue_zeroCycle_residue_Lag6 -0.4212684392965277 +INFO:pyaf.std:AR_MODEL_COEFF 3 ___ConstantTrend_residue_zeroCycle_residue_Lag13 -0.3210433028117729 +INFO:pyaf.std:AR_MODEL_COEFF 4 ___ConstantTrend_residue_zeroCycle_residue_Lag5 0.29158385125791153 INFO:pyaf.std:AR_MODEL_COEFF 5 ___ConstantTrend_residue_zeroCycle_residue_Lag11 0.2582448862969711 -INFO:pyaf.std:AR_MODEL_COEFF 6 ___ConstantTrend_residue_zeroCycle_residue_Lag3 -0.23905173873845012 -INFO:pyaf.std:AR_MODEL_COEFF 7 ___ConstantTrend_residue_zeroCycle_residue_Lag10 -0.19523028489993477 -INFO:pyaf.std:AR_MODEL_COEFF 8 ___ConstantTrend_residue_zeroCycle_residue_Lag14 0.16332353405682767 -INFO:pyaf.std:AR_MODEL_COEFF 9 ___ConstantTrend_residue_zeroCycle_residue_Lag8 0.14937478147165387 -INFO:pyaf.std:AR_MODEL_COEFF 10 ___ConstantTrend_residue_zeroCycle_residue_Lag4 0.09760913222680831 +INFO:pyaf.std:AR_MODEL_COEFF 6 ___ConstantTrend_residue_zeroCycle_residue_Lag3 -0.23905173873845098 +INFO:pyaf.std:AR_MODEL_COEFF 7 ___ConstantTrend_residue_zeroCycle_residue_Lag10 -0.19523028489993416 +INFO:pyaf.std:AR_MODEL_COEFF 8 ___ConstantTrend_residue_zeroCycle_residue_Lag14 0.1633235340568281 +INFO:pyaf.std:AR_MODEL_COEFF 9 ___ConstantTrend_residue_zeroCycle_residue_Lag8 0.14937478147165384 +INFO:pyaf.std:AR_MODEL_COEFF 10 ___ConstantTrend_residue_zeroCycle_residue_Lag4 0.0976091322268083 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ACT_female'), (0, 'ACT_male'), (0, 'NSW_female'), (0, 'NSW_male'), (0, 'NT_female'), (0, 'NT_male'), (0, 'QLD_female'), (0, 'QLD_male'), (0, 'SA_female'), (0, 'SA_male'), (0, 'TAS_female'), (0, 'TAS_male'), (0, 'VIC_female'), (0, 'VIC_male'), (0, 'WA_female'), (0, 'WA_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'ACT_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_female' 0.5592153072357178 -INFO:pyaf.std:START_FORECASTING 'ACT_male' -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_male' 0.7005176544189453 -INFO:pyaf.std:START_FORECASTING 'NT_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 1.200251817703247 -INFO:pyaf.std:START_FORECASTING 'NT_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_female' 0.7845804691314697 -INFO:pyaf.std:START_FORECASTING 'QLD_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 1.3611915111541748 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_male' 0.6937787532806396 -INFO:pyaf.std:START_FORECASTING 'QLD_male' -INFO:pyaf.std:START_FORECASTING 'SA_female' -INFO:pyaf.std:START_FORECASTING 'SA_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_female' 1.418828010559082 -INFO:pyaf.std:START_FORECASTING 'TAS_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_male' 1.3045635223388672 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_female' 1.3854694366455078 -INFO:pyaf.std:START_FORECASTING 'TAS_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_male' 1.4122705459594727 -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_female' 1.3311290740966797 -INFO:pyaf.std:START_FORECASTING 'WA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_male' 1.2635776996612549 -INFO:pyaf.std:START_FORECASTING 'WA_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 1.201568603515625 -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 1.7011828422546387 -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_female' 1.427929401397705 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_male' 0.9699132442474365 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.8366072177886963 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.6797337532043457 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.43824219703674316 +INFO:pyaf.std:START_FORECASTING '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' 6.38163423538208 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 8.250052213668823 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 6.5204176902771 diff --git a/tests/references/hierarchical_test_grouped_signals_AllMethods_Exogenous_per_node.log b/tests/references/hierarchical_test_grouped_signals_AllMethods_Exogenous_per_node.log index c11e95f96..2a096708a 100644 --- a/tests/references/hierarchical_test_grouped_signals_AllMethods_Exogenous_per_node.log +++ b/tests/references/hierarchical_test_grouped_signals_AllMethods_Exogenous_per_node.log @@ -1,83 +1,9 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ACT_female'), (0, 'ACT_male'), (0, 'NSW_female'), (0, 'NSW_male'), (0, 'NT_female'), (0, 'NT_male'), (0, 'QLD_female'), (0, 'QLD_male'), (0, 'SA_female'), (0, 'SA_male'), (0, 'TAS_female'), (0, 'TAS_male'), (0, 'VIC_female'), (0, 'VIC_male'), (0, 'WA_female'), (0, 'WA_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'ACT_female' -INFO:pyaf.std:START_TRAINING 'ACT_male' -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'NT_male' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING 'NT_female' -INFO:pyaf.std:START_TRAINING 'TAS_female' -INFO:pyaf.std:START_TRAINING 'TAS_male' -INFO:pyaf.std:START_TRAINING 'QLD_female' -INFO:pyaf.std:START_TRAINING 'QLD_male' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING 'WA_female' -INFO:pyaf.std:START_TRAINING 'SA_female' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING 'WA_male' -INFO:pyaf.std:START_TRAINING 'SA_male' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ACT_female' 31.56381583213806 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NT_female' 31.593610763549805 -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'ACT_male' 31.793534994125366 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NT_male' 31.748886108398438 -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_male' 32.56051445007324 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 32.564730644226074 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'TAS_male' 32.556069135665894 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 32.63465428352356 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'SA_male' 32.597249031066895 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 32.66356635093689 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_female' 32.77221155166626 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'SA_female' 32.765615463256836 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'WA_male' 32.793556213378906 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'TAS_female' 32.834388971328735 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'WA_female' 38.21149277687073 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 38.97556185722351 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 8.663975238800049 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 9.057955026626587 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 8.883427381515503 +INFO:pyaf.std:START_TRAINING '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' 45.12369465827942 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ACT_female'), (0, 'ACT_male'), (0, 'NSW_female'), (0, 'NSW_male'), (0, 'NT_female'), (0, 'NT_male'), (0, 'QLD_female'), (0, 'QLD_male'), (0, 'SA_female'), (0, 'SA_male'), (0, 'TAS_female'), (0, 'TAS_male'), (0, 'VIC_female'), (0, 'VIC_male'), (0, 'WA_female'), (0, 'WA_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'ACT_female' -INFO:pyaf.std:START_FORECASTING 'ACT_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_female' 0.38568925857543945 -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_male' 0.7278954982757568 -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'NT_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 1.3017003536224365 -INFO:pyaf.std:START_FORECASTING 'NT_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_female' 0.8541197776794434 -INFO:pyaf.std:START_FORECASTING 'QLD_female' -INFO:pyaf.std:START_FORECASTING 'QLD_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 1.7496612071990967 -INFO:pyaf.std:START_FORECASTING 'SA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_male' 0.9368925094604492 -INFO:pyaf.std:START_FORECASTING 'SA_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_female' 1.2506706714630127 -INFO:pyaf.std:START_FORECASTING 'TAS_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_male' 1.3011467456817627 -INFO:pyaf.std:START_FORECASTING 'TAS_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_female' 1.2607719898223877 -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_male' 1.3512694835662842 -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_female' 1.3499171733856201 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_male' 1.32478928565979 -INFO:pyaf.std:START_FORECASTING 'WA_female' -INFO:pyaf.std:START_FORECASTING 'WA_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 1.3253381252288818 -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 1.274109125137329 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_female' 1.3704750537872314 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_male' 1.0666460990905762 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.9064686298370361 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.7004311084747314 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.42853689193725586 +INFO:pyaf.std:START_FORECASTING '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' 5.658659219741821 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -97,202 +23,199 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'ACT_female', 'ACT_female '_male_OC_Forecast', '__OC_Forecast'], dtype='object', length=172) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['_female', '_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 9434359302.0175, 'RMSE': 11.984091108883767, 'MAE': 10.802232415565705, 'SMAPE': 0.87, 'ErrorMean': 3.0017810598372217, 'ErrorStdDev': 11.602058014629538, 'R2': -0.739851149203288, 'Pearson': -0.48809664220945836} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 9434359302.0175, 'RMSE': 11.984091108883767, 'MAE': 10.802232415565705, 'SMAPE': 0.87, 'ErrorMean': 3.0017810598372217, 'ErrorStdDev': 11.602058014629538, 'R2': -0.739851149203288, 'Pearson': -0.48809664220945836} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_BU_Forecast', 'Length': 59, 'MAPE': 2.3483, 'RMSE': 36.07089270265762, 'MAE': 16.60413674231543, 'SMAPE': 1.8975, 'ErrorMean': -8.107727664464235, 'ErrorStdDev': 35.14788830762839, 'R2': -14.762157812852113, 'Pearson': -0.1359495955150412} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_BU_Forecast', 'Length': 59, 'MAPE': 2.3483, 'RMSE': 36.07089270265762, 'MAE': 16.60413674231543, 'SMAPE': 1.8975, 'ErrorMean': -8.107727664464235, 'ErrorStdDev': 35.14788830762839, 'R2': -14.762157812852113, 'Pearson': -0.1359495955150412} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_MO_Forecast', 'Length': 59, 'MAPE': 7652485341.7589, 'RMSE': 11.109136830520407, 'MAE': 10.040709649727075, 'SMAPE': 0.888, 'ErrorMean': 6.774242184153497e-16, 'ErrorStdDev': 11.109136830520407, 'R2': -0.4950734256362179, 'Pearson': -0.5103140991479994} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_MO_Forecast', 'Length': 59, 'MAPE': 7652485341.7589, 'RMSE': 11.109136830520407, 'MAE': 10.040709649727075, 'SMAPE': 0.888, 'ErrorMean': 6.774242184153497e-16, 'ErrorStdDev': 11.109136830520407, 'R2': -0.4950734256362179, 'Pearson': -0.5103140991479994} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_OC_Forecast', 'Length': 59, 'MAPE': 1879071248.0498, 'RMSE': 32.437505632302994, 'MAE': 15.747071773748528, 'SMAPE': 1.3987, 'ErrorMean': -7.1225484375470165, 'ErrorStdDev': 31.645869800030543, 'R2': -11.746671435974186, 'Pearson': -0.10828785993140833} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_OC_Forecast', 'Length': 59, 'MAPE': 1879071248.0498, 'RMSE': 32.437505632302994, 'MAE': 15.747071773748528, 'SMAPE': 1.3987, 'ErrorMean': -7.1225484375470165, 'ErrorStdDev': 31.645869800030543, 'R2': -11.746671435974186, 'Pearson': -0.10828785993140833} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 7596427675.7192, 'RMSE': 11.041385512052488, 'MAE': 9.966652192052766, 'SMAPE': 0.8841, 'ErrorMean': 4.2150840256955097e-16, 'ErrorStdDev': 11.041385512052488, 'R2': -0.4768930181373918, 'Pearson': -0.48809664220945836} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 7596427675.7192, 'RMSE': 11.041385512052488, 'MAE': 9.966652192052766, 'SMAPE': 0.8841, 'ErrorMean': 4.2150840256955097e-16, 'ErrorStdDev': 11.041385512052488, 'R2': -0.4768930181373918, 'Pearson': -0.48809664220945836} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 3.2052, 'RMSE': 17.47612542284065, 'MAE': 15.95462070192566, 'SMAPE': 0.8655, 'ErrorMean': 4.0744862272257185, 'ErrorStdDev': 16.994514461407988, 'R2': -0.6846081658668226, 'Pearson': -0.45831100182114964} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 3.2052, 'RMSE': 17.47612542284065, 'MAE': 15.95462070192566, 'SMAPE': 0.8655, 'ErrorMean': 4.0744862272257185, 'ErrorStdDev': 16.994514461407988, 'R2': -0.6846081658668226, 'Pearson': -0.45831100182114964} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_BU_Forecast', 'Length': 59, 'MAPE': 0.4944, 'RMSE': 6.911057217561346, 'MAE': 4.983050847457627, 'SMAPE': 0.3977, 'ErrorMean': -0.4067796610169492, 'ErrorStdDev': 6.899075457754446, 'R2': 0.7365503821922521, 'Pearson': 0.8706261722445393} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_BU_Forecast', 'Length': 59, 'MAPE': 0.4944, 'RMSE': 6.911057217561346, 'MAE': 4.983050847457627, 'SMAPE': 0.3977, 'ErrorMean': -0.4067796610169492, 'ErrorStdDev': 6.899075457754446, 'R2': 0.7365503821922521, 'Pearson': 0.8706261722445393} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'ACT_male_MO_Forecast', 'Length': 59, 'MAPE': 2.606, 'RMSE': 16.16836943641302, 'MAE': 14.890379680196547, 'SMAPE': 0.881, 'ErrorMean': 2.4989426723766236e-15, 'ErrorStdDev': 16.16836943641302, 'R2': -0.44191959476648357, 'Pearson': -0.43991121218379786} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'ACT_male_MO_Forecast', 'Length': 59, 'MAPE': 2.606, 'RMSE': 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+INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 52.49081349372864 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ACT_female' Length=59 Min=0 Max=36 Mean=12.40677966101695 StdDev=9.08550464829349 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_ACT_female' Min=3 Max=732 Mean=253.64406779661016 StdDev=242.26696038033666 @@ -355,7 +278,7 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0625 MAPE_Forecast=0.0625 MAPE_Test=0.0625 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0625 SMAPE_Forecast=0.0625 SMAPE_Test=0.0625 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8882 MASE_Forecast=0.8882 MASE_Test=0.8882 INFO:pyaf.std:MODEL_L1 L1_Fit=39.082334566071665 L1_Forecast=39.082334566071665 L1_Test=39.082334566071665 -INFO:pyaf.std:MODEL_L2 L2_Fit=50.17389226712832 L2_Forecast=50.17389226712832 L2_Test=50.17389226712832 +INFO:pyaf.std:MODEL_L2 L2_Fit=50.17389226712833 L2_Forecast=50.17389226712833 L2_Test=50.17389226712833 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -367,16 +290,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _NSW_female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7381012420858866 -INFO:pyaf.std:AR_MODEL_COEFF 2 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.43576178342597155 -INFO:pyaf.std:AR_MODEL_COEFF 3 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.3257610930360297 -INFO:pyaf.std:AR_MODEL_COEFF 4 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.24580289146706674 -INFO:pyaf.std:AR_MODEL_COEFF 5 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.21161195273954414 -INFO:pyaf.std:AR_MODEL_COEFF 6 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.17480763666877155 -INFO:pyaf.std:AR_MODEL_COEFF 7 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.14610555137622688 -INFO:pyaf.std:AR_MODEL_COEFF 8 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag12 0.08341233385621413 -INFO:pyaf.std:AR_MODEL_COEFF 9 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.053102285172972025 -INFO:pyaf.std:AR_MODEL_COEFF 10 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.04725448593564335 +INFO:pyaf.std:AR_MODEL_COEFF 1 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.738101242085887 +INFO:pyaf.std:AR_MODEL_COEFF 2 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.43576178342597083 +INFO:pyaf.std:AR_MODEL_COEFF 3 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.3257610930360296 +INFO:pyaf.std:AR_MODEL_COEFF 4 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.24580289146706663 +INFO:pyaf.std:AR_MODEL_COEFF 5 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.2116119527395436 +INFO:pyaf.std:AR_MODEL_COEFF 6 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.1748076366687712 +INFO:pyaf.std:AR_MODEL_COEFF 7 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.14610555137622655 +INFO:pyaf.std:AR_MODEL_COEFF 8 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag12 0.08341233385621395 +INFO:pyaf.std:AR_MODEL_COEFF 9 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.05310228517297272 +INFO:pyaf.std:AR_MODEL_COEFF 10 _NSW_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.047254485935642576 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_male' Length=59 Min=349 Max=1264 Mean=881.8305084745763 StdDev=269.81118021227 @@ -390,8 +313,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_NSW_male_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0551 MAPE_Forecast=0.0551 MAPE_Test=0.0551 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0549 SMAPE_Forecast=0.0549 SMAPE_Test=0.0549 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8223 MASE_Forecast=0.8223 MASE_Test=0.8223 -INFO:pyaf.std:MODEL_L1 L1_Fit=45.5503583900817 L1_Forecast=45.5503583900817 L1_Test=45.5503583900817 -INFO:pyaf.std:MODEL_L2 L2_Fit=61.425911028651036 L2_Forecast=61.425911028651036 L2_Test=61.425911028651036 +INFO:pyaf.std:MODEL_L1 L1_Fit=45.550358390081705 L1_Forecast=45.550358390081705 L1_Test=45.550358390081705 +INFO:pyaf.std:MODEL_L2 L2_Fit=61.42591102865104 L2_Forecast=61.42591102865104 L2_Test=61.42591102865104 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -403,16 +326,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _NSW_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex4_Lag13 -29.292105840604634 -INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex4_Lag14 -18.525258647270707 -INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex4_Lag9 -17.98566374032633 -INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex4_Lag11 -13.483825687369155 -INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex4_Lag10 6.2888501125700635 -INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex4_Lag6 3.4169693532778673 -INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex4_Lag7 -2.6613773740837976 -INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex4_Lag12 2.2672888101101867 -INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex4_Lag5 -2.023390988609671 -INFO:pyaf.std:AR_MODEL_COEFF 10 Index_ex4_Lag8 1.0421861432384132 +INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex4_Lag13 -29.292105840604506 +INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex4_Lag14 -18.525258647270682 +INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex4_Lag9 -17.985663740326345 +INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex4_Lag11 -13.483825687369114 +INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex4_Lag10 6.288850112569968 +INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex4_Lag6 3.4169693532777434 +INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex4_Lag7 -2.6613773740837754 +INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex4_Lag12 2.267288810110095 +INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex4_Lag5 -2.023390988609438 +INFO:pyaf.std:AR_MODEL_COEFF 10 Index_ex4_Lag8 1.0421861432383848 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NT_female' Length=59 Min=1 Max=82 Mean=18.305084745762713 StdDev=17.728894578030516 @@ -475,7 +398,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '_QLD_female_ConstantTrend_residue_zeroCycle_residu INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0838 MAPE_Forecast=0.0838 MAPE_Test=0.0838 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0834 SMAPE_Forecast=0.0834 SMAPE_Test=0.0834 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7508 MASE_Forecast=0.7508 MASE_Test=0.7508 -INFO:pyaf.std:MODEL_L1 L1_Fit=22.53699462921294 L1_Forecast=22.53699462921294 L1_Test=22.53699462921294 +INFO:pyaf.std:MODEL_L1 L1_Fit=22.536994629212934 L1_Forecast=22.536994629212934 L1_Test=22.536994629212934 INFO:pyaf.std:MODEL_L2 L2_Fit=28.98059669689698 L2_Forecast=28.98059669689698 L2_Test=28.98059669689698 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -489,15 +412,15 @@ INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _QLD_female_ConstantTrend_residue_zeroCycl INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_COEFF 1 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.38519716687834765 -INFO:pyaf.std:AR_MODEL_COEFF 2 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.34542274944585805 -INFO:pyaf.std:AR_MODEL_COEFF 3 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2658985584549923 -INFO:pyaf.std:AR_MODEL_COEFF 4 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag4 0.14795940972751212 -INFO:pyaf.std:AR_MODEL_COEFF 5 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag3 0.14566374926553405 -INFO:pyaf.std:AR_MODEL_COEFF 6 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag14 0.1443025749378152 -INFO:pyaf.std:AR_MODEL_COEFF 7 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.09361729940604877 -INFO:pyaf.std:AR_MODEL_COEFF 8 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.0892421645842573 -INFO:pyaf.std:AR_MODEL_COEFF 9 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.08430231500682009 -INFO:pyaf.std:AR_MODEL_COEFF 10 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06523118927527724 +INFO:pyaf.std:AR_MODEL_COEFF 2 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.3454227494458583 +INFO:pyaf.std:AR_MODEL_COEFF 3 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2658985584549925 +INFO:pyaf.std:AR_MODEL_COEFF 4 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag4 0.14795940972751292 +INFO:pyaf.std:AR_MODEL_COEFF 5 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag3 0.14566374926553394 +INFO:pyaf.std:AR_MODEL_COEFF 6 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag14 0.144302574937815 +INFO:pyaf.std:AR_MODEL_COEFF 7 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.0936172994060487 +INFO:pyaf.std:AR_MODEL_COEFF 8 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.08924216458425711 +INFO:pyaf.std:AR_MODEL_COEFF 9 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.08430231500682041 +INFO:pyaf.std:AR_MODEL_COEFF 10 _QLD_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06523118927527745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='QLD_male' Length=59 Min=182 Max=493 Mean=360.6271186440678 StdDev=90.19881962192916 @@ -511,7 +434,7 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0646 MAPE_Forecast=0.0646 MAPE_Test=0.0646 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0641 SMAPE_Forecast=0.0641 SMAPE_Test=0.0641 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9026 MASE_Forecast=0.9026 MASE_Test=0.9026 INFO:pyaf.std:MODEL_L1 L1_Fit=21.614584201956077 L1_Forecast=21.614584201956077 L1_Test=21.614584201956077 -INFO:pyaf.std:MODEL_L2 L2_Fit=27.688951982547675 L2_Forecast=27.688951982547675 L2_Test=27.688951982547675 +INFO:pyaf.std:MODEL_L2 L2_Fit=27.688951982547678 L2_Forecast=27.688951982547678 L2_Test=27.688951982547678 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -523,16 +446,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _QLD_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8648492231952413 -INFO:pyaf.std:AR_MODEL_COEFF 2 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag9 0.23708437703604907 -INFO:pyaf.std:AR_MODEL_COEFF 3 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.21071148770757236 -INFO:pyaf.std:AR_MODEL_COEFF 4 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.16218172139942713 -INFO:pyaf.std:AR_MODEL_COEFF 5 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag7 0.14907363231129328 -INFO:pyaf.std:AR_MODEL_COEFF 6 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.1366169523636399 -INFO:pyaf.std:AR_MODEL_COEFF 7 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1211880748980526 -INFO:pyaf.std:AR_MODEL_COEFF 8 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.11380825652716393 -INFO:pyaf.std:AR_MODEL_COEFF 9 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.095741941756509 -INFO:pyaf.std:AR_MODEL_COEFF 10 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag10 0.07366132033171875 +INFO:pyaf.std:AR_MODEL_COEFF 1 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.864849223195242 +INFO:pyaf.std:AR_MODEL_COEFF 2 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag9 0.23708437703604882 +INFO:pyaf.std:AR_MODEL_COEFF 3 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.21071148770757234 +INFO:pyaf.std:AR_MODEL_COEFF 4 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.16218172139942705 +INFO:pyaf.std:AR_MODEL_COEFF 5 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag7 0.14907363231129314 +INFO:pyaf.std:AR_MODEL_COEFF 6 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.1366169523636397 +INFO:pyaf.std:AR_MODEL_COEFF 7 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.12118807489805285 +INFO:pyaf.std:AR_MODEL_COEFF 8 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.11380825652716445 +INFO:pyaf.std:AR_MODEL_COEFF 9 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.0957419417565096 +INFO:pyaf.std:AR_MODEL_COEFF 10 _QLD_male_ConstantTrend_residue_zeroCycle_residue_Lag10 0.07366132033171885 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='SA_female' Length=59 Min=44 Max=228 Mean=136.5084745762712 StdDev=45.44893684548569 @@ -545,7 +468,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '_SA_female_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.104 MAPE_Forecast=0.104 MAPE_Test=0.104 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1013 SMAPE_Forecast=0.1013 SMAPE_Test=0.1013 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7186 MASE_Forecast=0.7186 MASE_Test=0.7186 -INFO:pyaf.std:MODEL_L1 L1_Fit=12.92176973795644 L1_Forecast=12.92176973795644 L1_Test=12.92176973795644 +INFO:pyaf.std:MODEL_L1 L1_Fit=12.921769737956442 L1_Forecast=12.921769737956442 L1_Test=12.921769737956442 INFO:pyaf.std:MODEL_L2 L2_Fit=17.370820628842115 L2_Forecast=17.370820628842115 L2_Test=17.370820628842115 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -559,15 +482,15 @@ INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _SA_female_ConstantTrend_residue_zeroCycle INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_COEFF 1 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5453673575811728 -INFO:pyaf.std:AR_MODEL_COEFF 2 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag13 0.4017295404277731 -INFO:pyaf.std:AR_MODEL_COEFF 3 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2856147369742006 -INFO:pyaf.std:AR_MODEL_COEFF 4 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.2803989193136852 -INFO:pyaf.std:AR_MODEL_COEFF 5 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.26790264216672766 -INFO:pyaf.std:AR_MODEL_COEFF 6 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag3 0.22277580975372685 -INFO:pyaf.std:AR_MODEL_COEFF 7 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.17775814359413541 -INFO:pyaf.std:AR_MODEL_COEFF 8 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.17488459291577818 -INFO:pyaf.std:AR_MODEL_COEFF 9 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.15925222207723772 -INFO:pyaf.std:AR_MODEL_COEFF 10 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15769089703537997 +INFO:pyaf.std:AR_MODEL_COEFF 2 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag13 0.40172954042777265 +INFO:pyaf.std:AR_MODEL_COEFF 3 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.2856147369742007 +INFO:pyaf.std:AR_MODEL_COEFF 4 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.28039891931368505 +INFO:pyaf.std:AR_MODEL_COEFF 5 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.2679026421667279 +INFO:pyaf.std:AR_MODEL_COEFF 6 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag3 0.22277580975372663 +INFO:pyaf.std:AR_MODEL_COEFF 7 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.17775814359413544 +INFO:pyaf.std:AR_MODEL_COEFF 8 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.1748845929157778 +INFO:pyaf.std:AR_MODEL_COEFF 9 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.1592522220772378 +INFO:pyaf.std:AR_MODEL_COEFF 10 _SA_female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15769089703537958 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='SA_male' Length=59 Min=66 Max=272 Mean=185.0677966101695 StdDev=57.82230652986103 @@ -580,8 +503,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_SA_male_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0928 MAPE_Forecast=0.0928 MAPE_Test=0.0928 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0901 SMAPE_Forecast=0.0901 SMAPE_Test=0.0901 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7726 MASE_Forecast=0.7726 MASE_Test=0.7726 -INFO:pyaf.std:MODEL_L1 L1_Fit=14.986417254763131 L1_Forecast=14.986417254763131 L1_Test=14.986417254763131 -INFO:pyaf.std:MODEL_L2 L2_Fit=19.770518466923754 L2_Forecast=19.770518466923754 L2_Test=19.770518466923754 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.98641725476313 L1_Forecast=14.98641725476313 L1_Test=14.98641725476313 +INFO:pyaf.std:MODEL_L2 L2_Fit=19.77051846692376 L2_Forecast=19.77051846692376 L2_Test=19.77051846692376 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -593,16 +516,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _SA_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.604707348002017 -INFO:pyaf.std:AR_MODEL_COEFF 2 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.34195613383262735 -INFO:pyaf.std:AR_MODEL_COEFF 3 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.2848939210087596 -INFO:pyaf.std:AR_MODEL_COEFF 4 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag6 0.27611740019096764 -INFO:pyaf.std:AR_MODEL_COEFF 5 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag12 0.2400440546073953 -INFO:pyaf.std:AR_MODEL_COEFF 6 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.19507066501850545 -INFO:pyaf.std:AR_MODEL_COEFF 7 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.1930852122328221 -INFO:pyaf.std:AR_MODEL_COEFF 8 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag9 0.19272405571306916 +INFO:pyaf.std:AR_MODEL_COEFF 1 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6047073480020169 +INFO:pyaf.std:AR_MODEL_COEFF 2 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.34195613383262746 +INFO:pyaf.std:AR_MODEL_COEFF 3 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.2848939210087595 +INFO:pyaf.std:AR_MODEL_COEFF 4 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag6 0.2761174001909678 +INFO:pyaf.std:AR_MODEL_COEFF 5 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag12 0.24004405460739547 +INFO:pyaf.std:AR_MODEL_COEFF 6 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.19507066501850584 +INFO:pyaf.std:AR_MODEL_COEFF 7 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19308521223282227 +INFO:pyaf.std:AR_MODEL_COEFF 8 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag9 0.19272405571306941 INFO:pyaf.std:AR_MODEL_COEFF 9 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.1912679241101395 -INFO:pyaf.std:AR_MODEL_COEFF 10 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.16885727457642066 +INFO:pyaf.std:AR_MODEL_COEFF 10 _SA_male_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.16885727457642036 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='TAS_female' Length=59 Min=29 Max=151 Mean=84.38983050847457 StdDev=30.29908369658443 @@ -616,7 +539,7 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1063 MAPE_Forecast=0.1063 MAPE_Test=0.1063 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1029 SMAPE_Forecast=0.1029 SMAPE_Test=0.1029 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7058 MASE_Forecast=0.7058 MASE_Test=0.7058 INFO:pyaf.std:MODEL_L1 L1_Fit=8.092932844154943 L1_Forecast=8.092932844154943 L1_Test=8.092932844154943 -INFO:pyaf.std:MODEL_L2 L2_Fit=11.425753177213439 L2_Forecast=11.425753177213439 L2_Test=11.425753177213439 +INFO:pyaf.std:MODEL_L2 L2_Fit=11.425753177213437 L2_Forecast=11.425753177213437 L2_Test=11.425753177213437 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -628,16 +551,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _TAS_female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227048404991295 -INFO:pyaf.std:AR_MODEL_COEFF 2 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.32113163141839896 -INFO:pyaf.std:AR_MODEL_COEFF 3 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag8 0.24969045944455115 -INFO:pyaf.std:AR_MODEL_COEFF 4 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag11 0.2342381505268491 +INFO:pyaf.std:AR_MODEL_COEFF 1 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227048404991296 +INFO:pyaf.std:AR_MODEL_COEFF 2 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.3211316314183985 +INFO:pyaf.std:AR_MODEL_COEFF 3 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag8 0.24969045944455107 +INFO:pyaf.std:AR_MODEL_COEFF 4 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag11 0.23423815052684888 INFO:pyaf.std:AR_MODEL_COEFF 5 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag4 0.2167050880675857 -INFO:pyaf.std:AR_MODEL_COEFF 6 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.19315737228095625 -INFO:pyaf.std:AR_MODEL_COEFF 7 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.18530510150962975 -INFO:pyaf.std:AR_MODEL_COEFF 8 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.15746739400714707 -INFO:pyaf.std:AR_MODEL_COEFF 9 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06932740432823306 -INFO:pyaf.std:AR_MODEL_COEFF 10 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.06892795192428368 +INFO:pyaf.std:AR_MODEL_COEFF 6 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.19315737228095586 +INFO:pyaf.std:AR_MODEL_COEFF 7 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1853051015096298 +INFO:pyaf.std:AR_MODEL_COEFF 8 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.1574673940071472 +INFO:pyaf.std:AR_MODEL_COEFF 9 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06932740432823326 +INFO:pyaf.std:AR_MODEL_COEFF 10 _TAS_female_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.06892795192428333 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='TAS_male' Length=59 Min=29 Max=151 Mean=84.38983050847457 StdDev=30.29908369658443 @@ -651,7 +574,7 @@ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1063 MAPE_Forecast=0.1063 MAPE_Test=0.1063 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1029 SMAPE_Forecast=0.1029 SMAPE_Test=0.1029 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7058 MASE_Forecast=0.7058 MASE_Test=0.7058 INFO:pyaf.std:MODEL_L1 L1_Fit=8.092932844154943 L1_Forecast=8.092932844154943 L1_Test=8.092932844154943 -INFO:pyaf.std:MODEL_L2 L2_Fit=11.425753177213439 L2_Forecast=11.425753177213439 L2_Test=11.425753177213439 +INFO:pyaf.std:MODEL_L2 L2_Fit=11.425753177213437 L2_Forecast=11.425753177213437 L2_Test=11.425753177213437 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -663,16 +586,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _TAS_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227048404991295 -INFO:pyaf.std:AR_MODEL_COEFF 2 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.32113163141839896 -INFO:pyaf.std:AR_MODEL_COEFF 3 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.24969045944455115 -INFO:pyaf.std:AR_MODEL_COEFF 4 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.2342381505268491 +INFO:pyaf.std:AR_MODEL_COEFF 1 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227048404991296 +INFO:pyaf.std:AR_MODEL_COEFF 2 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.3211316314183985 +INFO:pyaf.std:AR_MODEL_COEFF 3 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.24969045944455107 +INFO:pyaf.std:AR_MODEL_COEFF 4 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.23423815052684888 INFO:pyaf.std:AR_MODEL_COEFF 5 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.2167050880675857 -INFO:pyaf.std:AR_MODEL_COEFF 6 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.19315737228095625 -INFO:pyaf.std:AR_MODEL_COEFF 7 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.18530510150962975 -INFO:pyaf.std:AR_MODEL_COEFF 8 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.15746739400714707 -INFO:pyaf.std:AR_MODEL_COEFF 9 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06932740432823306 -INFO:pyaf.std:AR_MODEL_COEFF 10 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.06892795192428368 +INFO:pyaf.std:AR_MODEL_COEFF 6 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.19315737228095586 +INFO:pyaf.std:AR_MODEL_COEFF 7 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1853051015096298 +INFO:pyaf.std:AR_MODEL_COEFF 8 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.1574673940071472 +INFO:pyaf.std:AR_MODEL_COEFF 9 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06932740432823326 +INFO:pyaf.std:AR_MODEL_COEFF 10 _TAS_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.06892795192428333 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='VIC_female' Length=59 Min=174 Max=656 Mean=425.8135593220339 StdDev=122.35021622116214 @@ -685,8 +608,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_VIC_female_ConstantTrend_residue_zeroCycle_residu INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0819 MAPE_Forecast=0.0819 MAPE_Test=0.0819 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0815 SMAPE_Forecast=0.0815 SMAPE_Test=0.0815 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8675 MASE_Forecast=0.8675 MASE_Test=0.8675 -INFO:pyaf.std:MODEL_L1 L1_Fit=31.7068179186823 L1_Forecast=31.7068179186823 L1_Test=31.7068179186823 -INFO:pyaf.std:MODEL_L2 L2_Fit=41.2407101377218 L2_Forecast=41.2407101377218 L2_Test=41.2407101377218 +INFO:pyaf.std:MODEL_L1 L1_Fit=31.706817918682308 L1_Forecast=31.706817918682308 L1_Test=31.706817918682308 +INFO:pyaf.std:MODEL_L2 L2_Fit=41.240710137721805 L2_Forecast=41.240710137721805 L2_Test=41.240710137721805 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -698,16 +621,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _VIC_female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.794180370004508 -INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag4 0.2585472376094916 -INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.24278239843446212 -INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.18052073136099916 -INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1791039758505455 -INFO:pyaf.std:AR_MODEL_COEFF 6 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag10 0.16751082167717155 -INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.13525492026535246 -INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.12276056871702126 -INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag7 0.08083867161389931 -INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.07613183046300796 +INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7941803700045076 +INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag4 0.25854723760949216 +INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.24278239843446195 +INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.18052073136099936 +INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.17910397585054502 +INFO:pyaf.std:AR_MODEL_COEFF 6 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag10 0.16751082167717193 +INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.13525492026535277 +INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.12276056871702179 +INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag7 0.08083867161389882 +INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_female_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.07613183046300781 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='VIC_male' Length=59 Min=253 Max=841 Mean=560.271186440678 StdDev=158.16756085042223 @@ -720,8 +643,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_VIC_male_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0688 MAPE_Forecast=0.0688 MAPE_Test=0.0688 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.068 SMAPE_Forecast=0.068 SMAPE_Test=0.068 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8329 MASE_Forecast=0.8329 MASE_Test=0.8329 -INFO:pyaf.std:MODEL_L1 L1_Fit=36.94765351012386 L1_Forecast=36.94765351012386 L1_Test=36.94765351012386 -INFO:pyaf.std:MODEL_L2 L2_Fit=48.8376764851016 L2_Forecast=48.8376764851016 L2_Test=48.8376764851016 +INFO:pyaf.std:MODEL_L1 L1_Fit=36.94765351012385 L1_Forecast=36.94765351012385 L1_Test=36.94765351012385 +INFO:pyaf.std:MODEL_L2 L2_Fit=48.837676485101596 L2_Forecast=48.837676485101596 L2_Test=48.837676485101596 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -733,16 +656,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _VIC_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7521611446403602 -INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.2152519372602766 -INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.1896132378195106 -INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.18039210983774107 -INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.13698326164447133 +INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7521611446403604 +INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.2152519372602765 +INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.18961323781951078 +INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.18039210983774068 +INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.136983261644471 INFO:pyaf.std:AR_MODEL_COEFF 6 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.13079176706466694 -INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.10710782159578139 -INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.10446377424332748 -INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.10423150622570512 -INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag10 0.08746134284394676 +INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.10710782159578129 +INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.10446377424332774 +INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1042315062257052 +INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_male_ConstantTrend_residue_zeroCycle_residue_Lag10 0.08746134284394719 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='WA_female' Length=59 Min=74 Max=210 Mean=137.76271186440678 StdDev=34.12556027133081 @@ -756,7 +679,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '_WA_female_ConstantTrend_residue_zeroCycle_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1095 MAPE_Forecast=0.1095 MAPE_Test=0.1095 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1079 SMAPE_Forecast=0.1079 SMAPE_Test=0.1079 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8512 MASE_Forecast=0.8512 MASE_Test=0.8512 -INFO:pyaf.std:MODEL_L1 L1_Fit=14.955443412842484 L1_Forecast=14.955443412842484 L1_Test=14.955443412842484 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.955443412842486 L1_Forecast=14.955443412842486 L1_Test=14.955443412842486 INFO:pyaf.std:MODEL_L2 L2_Fit=18.738341885187562 L2_Forecast=18.738341885187562 L2_Test=18.738341885187562 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -769,16 +692,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _WA_female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -8.291421494694491 -INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -6.110150680651397 -INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag5 2.994167887163168 -INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag12 -2.672195409746049 -INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag6 1.356390107204002 -INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag8 1.2213781650659774 -INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag7 1.1285188628749334 -INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag11 -1.045346324402304 -INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag10 0.7944244043631794 -INFO:pyaf.std:AR_MODEL_COEFF 10 _WA_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5850039700745221 +INFO:pyaf.std:AR_MODEL_COEFF 1 Index_ex1_Lag14 -8.291421494694521 +INFO:pyaf.std:AR_MODEL_COEFF 2 Index_ex1_Lag13 -6.110150680651417 +INFO:pyaf.std:AR_MODEL_COEFF 3 Index_ex1_Lag5 2.994167887163127 +INFO:pyaf.std:AR_MODEL_COEFF 4 Index_ex1_Lag12 -2.6721954097460157 +INFO:pyaf.std:AR_MODEL_COEFF 5 Index_ex1_Lag6 1.3563901072039957 +INFO:pyaf.std:AR_MODEL_COEFF 6 Index_ex1_Lag8 1.221378165065988 +INFO:pyaf.std:AR_MODEL_COEFF 7 Index_ex1_Lag7 1.1285188628749574 +INFO:pyaf.std:AR_MODEL_COEFF 8 Index_ex1_Lag11 -1.04534632440227 +INFO:pyaf.std:AR_MODEL_COEFF 9 Index_ex1_Lag10 0.794424404363195 +INFO:pyaf.std:AR_MODEL_COEFF 10 _WA_female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.585003970074522 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='WA_male' Length=59 Min=101 Max=265 Mean=183.864406779661 StdDev=42.12550160728105 @@ -791,7 +714,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '_WA_male_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0726 MAPE_Forecast=0.0726 MAPE_Test=0.0726 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0719 SMAPE_Forecast=0.0719 SMAPE_Test=0.0719 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7907 MASE_Test=0.7907 -INFO:pyaf.std:MODEL_L1 L1_Fit=13.23766848262696 L1_Forecast=13.23766848262696 L1_Test=13.23766848262696 +INFO:pyaf.std:MODEL_L1 L1_Fit=13.237668482626963 L1_Forecast=13.237668482626963 L1_Test=13.237668482626963 INFO:pyaf.std:MODEL_L2 L2_Fit=16.238015085935594 L2_Forecast=16.238015085935594 L2_Test=16.238015085935594 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -804,16 +727,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _WA_male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7996235232042755 -INFO:pyaf.std:AR_MODEL_COEFF 2 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.5588385080467538 -INFO:pyaf.std:AR_MODEL_COEFF 3 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.41861748163626805 -INFO:pyaf.std:AR_MODEL_COEFF 4 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag6 0.4081675178697879 -INFO:pyaf.std:AR_MODEL_COEFF 5 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.3116477307385535 -INFO:pyaf.std:AR_MODEL_COEFF 6 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.29665681240386343 -INFO:pyaf.std:AR_MODEL_COEFF 7 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.2730666311154444 -INFO:pyaf.std:AR_MODEL_COEFF 8 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.2071665281706015 -INFO:pyaf.std:AR_MODEL_COEFF 9 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.07498943285909025 -INFO:pyaf.std:AR_MODEL_COEFF 10 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag14 0.07494965524931987 +INFO:pyaf.std:AR_MODEL_COEFF 1 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7996235232042754 +INFO:pyaf.std:AR_MODEL_COEFF 2 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.5588385080467542 +INFO:pyaf.std:AR_MODEL_COEFF 3 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.4186174816362683 +INFO:pyaf.std:AR_MODEL_COEFF 4 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag6 0.408167517869788 +INFO:pyaf.std:AR_MODEL_COEFF 5 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag2 0.3116477307385538 +INFO:pyaf.std:AR_MODEL_COEFF 6 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.296656812403863 +INFO:pyaf.std:AR_MODEL_COEFF 7 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.2730666311154446 +INFO:pyaf.std:AR_MODEL_COEFF 8 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.20716652817060094 +INFO:pyaf.std:AR_MODEL_COEFF 9 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.07498943285909018 +INFO:pyaf.std:AR_MODEL_COEFF 10 _WA_male_ConstantTrend_residue_zeroCycle_residue_Lag14 0.07494965524932029 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='_female' Length=59 Min=802 Max=2387 Mean=1738.3728813559321 StdDev=469.42741531319837 @@ -826,7 +749,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '__female_ConstantTrend_residue_zeroCycle_residue_A INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0514 MAPE_Forecast=0.0514 MAPE_Test=0.0514 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0513 SMAPE_Forecast=0.0513 SMAPE_Test=0.0513 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.921 MASE_Forecast=0.921 MASE_Test=0.921 -INFO:pyaf.std:MODEL_L1 L1_Fit=86.98718002042585 L1_Forecast=86.98718002042585 L1_Test=86.98718002042585 +INFO:pyaf.std:MODEL_L1 L1_Fit=86.98718002042588 L1_Forecast=86.98718002042588 L1_Test=86.98718002042588 INFO:pyaf.std:MODEL_L2 L2_Fit=107.9116048164168 L2_Forecast=107.9116048164168 L2_Test=107.9116048164168 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -839,16 +762,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES __female_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 __female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9213203396536638 -INFO:pyaf.std:AR_MODEL_COEFF 2 __female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.2858209060602198 -INFO:pyaf.std:AR_MODEL_COEFF 3 __female_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.22135280821648448 -INFO:pyaf.std:AR_MODEL_COEFF 4 __female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.20586177528831912 -INFO:pyaf.std:AR_MODEL_COEFF 5 __female_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.17094489575852562 -INFO:pyaf.std:AR_MODEL_COEFF 6 __female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1408492153777583 -INFO:pyaf.std:AR_MODEL_COEFF 7 __female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.12352902588521011 -INFO:pyaf.std:AR_MODEL_COEFF 8 __female_ConstantTrend_residue_zeroCycle_residue_Lag14 0.0886704953512847 -INFO:pyaf.std:AR_MODEL_COEFF 9 __female_ConstantTrend_residue_zeroCycle_residue_Lag12 0.0777468168089054 -INFO:pyaf.std:AR_MODEL_COEFF 10 __female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.06635748168640254 +INFO:pyaf.std:AR_MODEL_COEFF 1 __female_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9213203396536637 +INFO:pyaf.std:AR_MODEL_COEFF 2 __female_ConstantTrend_residue_zeroCycle_residue_Lag5 0.2858209060602196 +INFO:pyaf.std:AR_MODEL_COEFF 3 __female_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.22135280821648465 +INFO:pyaf.std:AR_MODEL_COEFF 4 __female_ConstantTrend_residue_zeroCycle_residue_Lag9 0.20586177528831962 +INFO:pyaf.std:AR_MODEL_COEFF 5 __female_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.17094489575852512 +INFO:pyaf.std:AR_MODEL_COEFF 6 __female_ConstantTrend_residue_zeroCycle_residue_Lag2 0.14084921537775785 +INFO:pyaf.std:AR_MODEL_COEFF 7 __female_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.12352902588520995 +INFO:pyaf.std:AR_MODEL_COEFF 8 __female_ConstantTrend_residue_zeroCycle_residue_Lag14 0.08867049535128513 +INFO:pyaf.std:AR_MODEL_COEFF 9 __female_ConstantTrend_residue_zeroCycle_residue_Lag12 0.07774681680890559 +INFO:pyaf.std:AR_MODEL_COEFF 10 __female_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.06635748168640271 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='_male' Length=59 Min=1049 Max=3096 Mean=2297.271186440678 StdDev=610.3587950224886 @@ -861,8 +784,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '__male_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0424 MAPE_Forecast=0.0424 MAPE_Test=0.0424 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0422 SMAPE_Forecast=0.0422 SMAPE_Test=0.0422 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8265 MASE_Forecast=0.8265 MASE_Test=0.8265 -INFO:pyaf.std:MODEL_L1 L1_Fit=90.69885284059069 L1_Forecast=90.69885284059069 L1_Test=90.69885284059069 -INFO:pyaf.std:MODEL_L2 L2_Fit=116.08593586971591 L2_Forecast=116.08593586971591 L2_Test=116.08593586971591 +INFO:pyaf.std:MODEL_L1 L1_Fit=90.69885284059066 L1_Forecast=90.69885284059066 L1_Test=90.69885284059066 +INFO:pyaf.std:MODEL_L2 L2_Fit=116.08593586971588 L2_Forecast=116.08593586971588 L2_Test=116.08593586971588 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -874,16 +797,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES __male_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 __male_ConstantTrend_residue_zeroCycle_residue_Lag1 1.1036312001347572 -INFO:pyaf.std:AR_MODEL_COEFF 2 __male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.7142791160229571 -INFO:pyaf.std:AR_MODEL_COEFF 3 __male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.4095631444640226 +INFO:pyaf.std:AR_MODEL_COEFF 1 __male_ConstantTrend_residue_zeroCycle_residue_Lag1 1.103631200134757 +INFO:pyaf.std:AR_MODEL_COEFF 2 __male_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.7142791160229567 +INFO:pyaf.std:AR_MODEL_COEFF 3 __male_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.4095631444640224 INFO:pyaf.std:AR_MODEL_COEFF 4 __male_ConstantTrend_residue_zeroCycle_residue_Lag11 0.38246023653185773 -INFO:pyaf.std:AR_MODEL_COEFF 5 __male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.32541893587583104 +INFO:pyaf.std:AR_MODEL_COEFF 5 __male_ConstantTrend_residue_zeroCycle_residue_Lag8 0.3254189358758301 INFO:pyaf.std:AR_MODEL_COEFF 6 __male_ConstantTrend_residue_zeroCycle_residue_Lag5 0.3052699089937121 -INFO:pyaf.std:AR_MODEL_COEFF 7 __male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.29975532955819195 -INFO:pyaf.std:AR_MODEL_COEFF 8 __male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.2735114915064893 -INFO:pyaf.std:AR_MODEL_COEFF 9 __male_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.2650821046838131 -INFO:pyaf.std:AR_MODEL_COEFF 10 __male_ConstantTrend_residue_zeroCycle_residue_Lag7 0.2445551571357526 +INFO:pyaf.std:AR_MODEL_COEFF 7 __male_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.29975532955819073 +INFO:pyaf.std:AR_MODEL_COEFF 8 __male_ConstantTrend_residue_zeroCycle_residue_Lag4 0.2735114915064889 +INFO:pyaf.std:AR_MODEL_COEFF 9 __male_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.2650821046838129 +INFO:pyaf.std:AR_MODEL_COEFF 10 __male_ConstantTrend_residue_zeroCycle_residue_Lag7 0.24455515713575224 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='_' Length=59 Min=1851 Max=5458 Mean=4035.64406779661 StdDev=1078.2170587848439 @@ -896,8 +819,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '___ConstantTrend_residue_zeroCycle_residue_AR(14)' INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0422 MAPE_Forecast=0.0422 MAPE_Test=0.0422 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0421 SMAPE_Forecast=0.0421 SMAPE_Test=0.0421 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8393 MASE_Forecast=0.8393 MASE_Test=0.8393 -INFO:pyaf.std:MODEL_L1 L1_Fit=163.73171242584746 L1_Forecast=163.73171242584746 L1_Test=163.73171242584746 -INFO:pyaf.std:MODEL_L2 L2_Fit=214.36140356957702 L2_Forecast=214.36140356957702 L2_Test=214.36140356957702 +INFO:pyaf.std:MODEL_L1 L1_Fit=163.73171242584732 L1_Forecast=163.73171242584732 L1_Test=163.73171242584732 +INFO:pyaf.std:MODEL_L2 L2_Fit=214.3614035695769 L2_Forecast=214.3614035695769 L2_Test=214.3614035695769 INFO:pyaf.std:MODEL_COMPLEXITY 14 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -909,61 +832,24 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES ___ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 ___ConstantTrend_residue_zeroCycle_residue_Lag1 1.0764495872616955 -INFO:pyaf.std:AR_MODEL_COEFF 2 ___ConstantTrend_residue_zeroCycle_residue_Lag6 -0.42126843929652724 -INFO:pyaf.std:AR_MODEL_COEFF 3 ___ConstantTrend_residue_zeroCycle_residue_Lag13 -0.32104330281177346 -INFO:pyaf.std:AR_MODEL_COEFF 4 ___ConstantTrend_residue_zeroCycle_residue_Lag5 0.29158385125791114 +INFO:pyaf.std:AR_MODEL_COEFF 1 ___ConstantTrend_residue_zeroCycle_residue_Lag1 1.0764495872616957 +INFO:pyaf.std:AR_MODEL_COEFF 2 ___ConstantTrend_residue_zeroCycle_residue_Lag6 -0.4212684392965277 +INFO:pyaf.std:AR_MODEL_COEFF 3 ___ConstantTrend_residue_zeroCycle_residue_Lag13 -0.3210433028117729 +INFO:pyaf.std:AR_MODEL_COEFF 4 ___ConstantTrend_residue_zeroCycle_residue_Lag5 0.29158385125791153 INFO:pyaf.std:AR_MODEL_COEFF 5 ___ConstantTrend_residue_zeroCycle_residue_Lag11 0.2582448862969711 -INFO:pyaf.std:AR_MODEL_COEFF 6 ___ConstantTrend_residue_zeroCycle_residue_Lag3 -0.23905173873845012 -INFO:pyaf.std:AR_MODEL_COEFF 7 ___ConstantTrend_residue_zeroCycle_residue_Lag10 -0.19523028489993477 -INFO:pyaf.std:AR_MODEL_COEFF 8 ___ConstantTrend_residue_zeroCycle_residue_Lag14 0.16332353405682767 -INFO:pyaf.std:AR_MODEL_COEFF 9 ___ConstantTrend_residue_zeroCycle_residue_Lag8 0.14937478147165387 -INFO:pyaf.std:AR_MODEL_COEFF 10 ___ConstantTrend_residue_zeroCycle_residue_Lag4 0.09760913222680831 +INFO:pyaf.std:AR_MODEL_COEFF 6 ___ConstantTrend_residue_zeroCycle_residue_Lag3 -0.23905173873845098 +INFO:pyaf.std:AR_MODEL_COEFF 7 ___ConstantTrend_residue_zeroCycle_residue_Lag10 -0.19523028489993416 +INFO:pyaf.std:AR_MODEL_COEFF 8 ___ConstantTrend_residue_zeroCycle_residue_Lag14 0.1633235340568281 +INFO:pyaf.std:AR_MODEL_COEFF 9 ___ConstantTrend_residue_zeroCycle_residue_Lag8 0.14937478147165384 +INFO:pyaf.std:AR_MODEL_COEFF 10 ___ConstantTrend_residue_zeroCycle_residue_Lag4 0.0976091322268083 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'ACT_female'), (0, 'ACT_male'), (0, 'NSW_female'), (0, 'NSW_male'), (0, 'NT_female'), (0, 'NT_male'), (0, 'QLD_female'), (0, 'QLD_male'), (0, 'SA_female'), (0, 'SA_male'), (0, 'TAS_female'), (0, 'TAS_male'), (0, 'VIC_female'), (0, 'VIC_male'), (0, 'WA_female'), (0, 'WA_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'ACT_female' -INFO:pyaf.std:START_FORECASTING 'ACT_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_female' 0.5106325149536133 -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'ACT_male' 0.6684865951538086 -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.9785614013671875 -INFO:pyaf.std:START_FORECASTING 'NT_female' -INFO:pyaf.std:START_FORECASTING 'NT_male' -INFO:pyaf.std:START_FORECASTING 'QLD_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 1.336350440979004 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_female' 0.7752318382263184 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NT_male' 0.796513557434082 -INFO:pyaf.std:START_FORECASTING 'QLD_male' -INFO:pyaf.std:START_FORECASTING 'SA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_female' 0.9413752555847168 -INFO:pyaf.std:START_FORECASTING 'SA_male' -INFO:pyaf.std:START_FORECASTING 'TAS_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_male' 1.106562852859497 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_female' 1.0939979553222656 -INFO:pyaf.std:START_FORECASTING 'TAS_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'SA_male' 1.194715976715088 -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_female' 1.2664234638214111 -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING 'WA_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'TAS_male' 1.2794530391693115 -INFO:pyaf.std:START_FORECASTING 'WA_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 1.2804105281829834 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 1.167658805847168 -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_male' 1.0618093013763428 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'WA_female' 1.4805212020874023 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.8089766502380371 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.5410115718841553 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.3530921936035156 +INFO:pyaf.std:START_FORECASTING '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ACT_female', 'ACT_male', 'NSW_female', 'NSW_male', 'NT_female', 'NT_male', 'QLD_female', 'QLD_male', 'SA_female', 'SA_male', 'TAS_female', 'TAS_male', 'VIC_female', 'VIC_male', 'WA_female', 'WA_male', '_female', '_male', '_']' 6.156930685043335 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 7.964772462844849 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 6.28606104850769 diff --git a/tests/references/hierarchical_test_grouped_signals_BU.log b/tests/references/hierarchical_test_grouped_signals_BU.log index a2829c332..7a82851a6 100644 --- a/tests/references/hierarchical_test_grouped_signals_BU.log +++ b/tests/references/hierarchical_test_grouped_signals_BU.log @@ -1,35 +1,9 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 7.2628514766693115 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 7.4855499267578125 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 7.552940607070923 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 7.5550243854522705 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 7.559192895889282 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 7.584506034851074 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 7.589756011962891 +INFO:pyaf.std:START_TRAINING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 17.403355836868286 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.8742942810058594 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.8163561820983887 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.7655813694000244 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.6885652542114258 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.6337287425994873 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.610039472579956 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.36207079887390137 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 3.5305051803588867 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD @@ -50,26 +24,23 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female '_male_BU_Forecast', '__BU_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['_female', '_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.962506218113037} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.962506218113037} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731268} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731268} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711864, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163617} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711864, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163617} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.610169491525426, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830509, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072746} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.610169491525426, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830509, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072746} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '__BU_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556231} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '__BU_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556231} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.967922899960135} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.967922899960135} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253134} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253134} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 9.639385461807251 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629301, 'MAE': 43.25423728813559, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830509, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731265} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268225, 'MAE': 54.45762711864407, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169491, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731265} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711864, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163619} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711864, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163619} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.610169491525426, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830509, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072747} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.610169491525426, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830509, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072747} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '__BU_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556235} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '__BU_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556235} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.9679228999601347} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_female_BU_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.9679228999601347} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_BU_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 21.76284098625183 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_female' Length=59 Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_NSW_female' Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 @@ -246,21 +217,8 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.695453405380249 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.9126217365264893 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.8258869647979736 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.7076923847198486 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.7240660190582275 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.5713508129119873 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.45051026344299316 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 3.476861000061035 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 1.5120058059692383 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 3.498002290725708 diff --git a/tests/references/hierarchical_test_grouped_signals_MO.log b/tests/references/hierarchical_test_grouped_signals_MO.log index eed95ce8b..363fb5d19 100644 --- a/tests/references/hierarchical_test_grouped_signals_MO.log +++ b/tests/references/hierarchical_test_grouped_signals_MO.log @@ -1,35 +1,9 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 7.3284690380096436 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 7.409416675567627 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 7.507105112075806 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 7.48045802116394 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 7.475027561187744 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 7.494397401809692 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 7.520778656005859 +INFO:pyaf.std:START_TRAINING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 15.291024208068848 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.7831263542175293 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.7630996704101562 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.8287196159362793 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.6791210174560547 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.7238562107086182 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.5534327030181885 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.42455625534057617 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.2541353702545166 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['MO'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD @@ -50,26 +24,23 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female 'VIC_male_MO_Forecast', '__MO_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['_female', '_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0813, 'RMSE': 62.32278723251837, 'MAE': 48.70371692503398, 'SMAPE': 0.079, 'ErrorMean': 7.869221952578178, 'ErrorStdDev': 61.82398526697237, 'R2': 0.9105114608134931, 'Pearson': 0.9559136222127097} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0813, 'RMSE': 62.32278723251837, 'MAE': 48.70371692503398, 'SMAPE': 0.079, 'ErrorMean': 7.869221952578178, 'ErrorStdDev': 61.82398526697237, 'R2': 0.9105114608134931, 'Pearson': 0.9559136222127097} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0699, 'RMSE': 71.08956064805535, 'MAE': 56.25353847722968, 'SMAPE': 0.0679, 'ErrorMean': 11.006816792816519, 'ErrorStdDev': 70.23229753626826, 'R2': 0.9305788542591356, 'Pearson': 0.966393454139386} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0699, 'RMSE': 71.08956064805535, 'MAE': 56.25353847722968, 'SMAPE': 0.0679, 'ErrorMean': 11.006816792816519, 'ErrorStdDev': 70.23229753626826, 'R2': 0.9305788542591356, 'Pearson': 0.966393454139386} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0813, 'RMSE': 62.32278723251837, 'MAE': 48.70371692503398, 'SMAPE': 0.079, 'ErrorMean': 7.869221952578178, 'ErrorStdDev': 61.82398526697237, 'R2': 0.9105114608134931, 'Pearson': 0.9559136222127095} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0813, 'RMSE': 62.32278723251837, 'MAE': 48.70371692503398, 'SMAPE': 0.079, 'ErrorMean': 7.869221952578178, 'ErrorStdDev': 61.82398526697237, 'R2': 0.9105114608134931, 'Pearson': 0.9559136222127095} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0699, 'RMSE': 71.08956064805535, 'MAE': 56.25353847722968, 'SMAPE': 0.0679, 'ErrorMean': 11.006816792816519, 'ErrorStdDev': 70.23229753626826, 'R2': 0.9305788542591356, 'Pearson': 0.9663934541393859} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0699, 'RMSE': 71.08956064805535, 'MAE': 56.25353847722968, 'SMAPE': 0.0679, 'ErrorMean': 11.006816792816519, 'ErrorStdDev': 70.23229753626826, 'R2': 0.9305788542591356, 'Pearson': 0.9663934541393859} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0877, 'RMSE': 43.077644041671846, 'MAE': 34.847752172270496, 'SMAPE': 0.0865, 'ErrorMean': 5.147727199964073, 'ErrorStdDev': 42.76896445853858, 'R2': 0.8760363360058929, 'Pearson': 0.9400739653817467} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0877, 'RMSE': 43.077644041671846, 'MAE': 34.847752172270496, 'SMAPE': 0.0865, 'ErrorMean': 5.147727199964073, 'ErrorStdDev': 42.76896445853858, 'R2': 0.8760363360058929, 'Pearson': 0.9400739653817467} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0767, 'RMSE': 50.005918797978644, 'MAE': 40.2321040264098, 'SMAPE': 0.0754, 'ErrorMean': 6.993183207183485, 'ErrorStdDev': 49.51451608832305, 'R2': 0.900044202610638, 'Pearson': 0.9511536267019925} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0767, 'RMSE': 50.005918797978644, 'MAE': 40.2321040264098, 'SMAPE': 0.0754, 'ErrorMean': 6.993183207183485, 'ErrorStdDev': 49.51451608832305, 'R2': 0.900044202610638, 'Pearson': 0.9511536267019925} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '__MO_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556231} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '__MO_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556231} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.967922899960135} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.967922899960135} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253134} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253134} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 9.54099154472351 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0767, 'RMSE': 50.005918797978644, 'MAE': 40.2321040264098, 'SMAPE': 0.0754, 'ErrorMean': 6.993183207183485, 'ErrorStdDev': 49.51451608832305, 'R2': 0.900044202610638, 'Pearson': 0.9511536267019924} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0767, 'RMSE': 50.005918797978644, 'MAE': 40.2321040264098, 'SMAPE': 0.0754, 'ErrorMean': 6.993183207183485, 'ErrorStdDev': 49.51451608832305, 'R2': 0.900044202610638, 'Pearson': 0.9511536267019924} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '__MO_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556235} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '__MO_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.23812824865286, 'MAE': 132.03389830508473, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152542374, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556235} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.9679228999601347} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_female_MO_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715808, 'MAE': 67.86440677966101, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542374, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.9679228999601347} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_MO_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 18.024953365325928 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_female' Length=59 Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_NSW_female' Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 @@ -246,21 +217,8 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.5800716876983643 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.70660400390625 -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.7810602188110352 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.6974256038665771 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.6498215198516846 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.5673320293426514 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.4405331611633301 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.260754346847534 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['MO'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 1.5620417594909668 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.278029441833496 diff --git a/tests/references/hierarchical_test_grouped_signals_OC.log b/tests/references/hierarchical_test_grouped_signals_OC.log index dd91a3201..4d6287e0b 100644 --- a/tests/references/hierarchical_test_grouped_signals_OC.log +++ b/tests/references/hierarchical_test_grouped_signals_OC.log @@ -1,35 +1,9 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 7.367550373077393 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 7.494046449661255 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 7.563509702682495 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 7.555171012878418 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 7.554503440856934 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 7.632709741592407 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 7.624057054519653 +INFO:pyaf.std:START_TRAINING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 15.806123733520508 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.7161350250244141 -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.7817206382751465 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.7539007663726807 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.7249696254730225 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.588038444519043 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.42423486709594727 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.6338491439819336 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.1937341690063477 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD @@ -50,26 +24,23 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female '_male_OC_Forecast', '__OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['_female', '_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629302, 'MAE': 43.25423728813563, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830692, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.962506218113037} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629302, 'MAE': 43.25423728813563, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389830692, 'ErrorStdDev': 56.54782418951946, 'R2': 0.9248778434795353, 'Pearson': 0.962506218113037} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268226, 'MAE': 54.457627118644076, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169509, 'ErrorStdDev': 70.30053268037928, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731269} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268226, 'MAE': 54.457627118644076, 'SMAPE': 0.0653, 'ErrorMean': 11.067796610169509, 'ErrorStdDev': 70.30053268037928, 'R2': 0.9304286389667543, 'Pearson': 0.9654607890731269} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.69595970061859, 'MAE': 35.9322033898305, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711786, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737462, 'Pearson': 0.9324933676163617} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.69595970061859, 'MAE': 35.9322033898305, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711786, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737462, 'Pearson': 0.9324933676163617} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.61016949152541, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830429, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072746} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.68662996769434, 'MAE': 43.61016949152541, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830429, 'ErrorStdDev': 53.23719370374587, 'R2': 0.8847880700331765, 'Pearson': 0.9420698072072746} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '__OC_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.2381282486528, 'MAE': 132.0338983050847, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152541986, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556231} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '__OC_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.2381282486528, 'MAE': 132.0338983050847, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152541986, 'ErrorStdDev': 165.35421573663845, 'R2': 0.9490192907244944, 'Pearson': 0.9750900202556231} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.5839069971581, 'MAE': 67.86440677966105, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542564, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.967922899960135} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.5839069971581, 'MAE': 67.86440677966105, 'SMAPE': 0.067, 'ErrorMean': 13.016949152542564, 'ErrorStdDev': 81.55158324444339, 'R2': 0.9352639961603366, 'Pearson': 0.967922899960135} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.2973774621749, 'MAE': 77.49152542372875, 'SMAPE': 0.0576, 'ErrorMean': 17.99999999999965, 'ErrorStdDev': 95.61788358365266, 'R2': 0.9468779874911686, 'Pearson': 0.974033332225313} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.2973774621749, 'MAE': 77.49152542372875, 'SMAPE': 0.0576, 'ErrorMean': 17.99999999999965, 'ErrorStdDev': 95.61788358365266, 'R2': 0.9468779874911686, 'Pearson': 0.974033332225313} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 9.55079698562622 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629308, 'MAE': 43.25423728813573, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389831088, 'ErrorStdDev': 56.547824189519446, 'R2': 0.9248778434795352, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0694, 'RMSE': 57.10145594629308, 'MAE': 43.25423728813573, 'SMAPE': 0.0683, 'ErrorMean': 7.932203389831088, 'ErrorStdDev': 56.547824189519446, 'R2': 0.9248778434795352, 'Pearson': 0.9625062181130374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268229, 'MAE': 54.45762711864411, 'SMAPE': 0.0653, 'ErrorMean': 11.06779661016978, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667542, 'Pearson': 0.9654607890731266} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0666, 'RMSE': 71.16643181268229, 'MAE': 54.45762711864411, 'SMAPE': 0.0653, 'ErrorMean': 11.06779661016978, 'ErrorStdDev': 70.30053268037926, 'R2': 0.9304286389667542, 'Pearson': 0.9654607890731266} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711835, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163619} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0908, 'RMSE': 44.6959597006186, 'MAE': 35.932203389830505, 'SMAPE': 0.089, 'ErrorMean': 5.084745762711835, 'ErrorStdDev': 44.40578987123082, 'R2': 0.8665473963737461, 'Pearson': 0.9324933676163619} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.686629967694294, 'MAE': 43.61016949152536, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830127, 'ErrorStdDev': 53.23719370374586, 'R2': 0.8847880700331766, 'Pearson': 0.9420698072072745} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0852, 'RMSE': 53.686629967694294, 'MAE': 43.61016949152536, 'SMAPE': 0.0834, 'ErrorMean': 6.932203389830127, 'ErrorStdDev': 53.23719370374586, 'R2': 0.8847880700331766, 'Pearson': 0.9420698072072745} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '__OC_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.238128248653, 'MAE': 132.03389830508488, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152543184, 'ErrorStdDev': 165.35421573663842, 'R2': 0.9490192907244943, 'Pearson': 0.9750900202556234} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '__OC_Forecast', 'Length': 59, 'MAPE': 0.0581, 'RMSE': 168.238128248653, 'MAE': 132.03389830508488, 'SMAPE': 0.0571, 'ErrorMean': 31.016949152543184, 'ErrorStdDev': 165.35421573663842, 'R2': 0.9490192907244943, 'Pearson': 0.9750900202556234} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715818, 'MAE': 67.86440677966112, 'SMAPE': 0.067, 'ErrorMean': 13.016949152543038, 'ErrorStdDev': 81.5515832444434, 'R2': 0.9352639961603365, 'Pearson': 0.9679228999601346} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_female_OC_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 82.58390699715818, 'MAE': 67.86440677966112, 'SMAPE': 0.067, 'ErrorMean': 13.016949152543038, 'ErrorStdDev': 81.5515832444434, 'R2': 0.9352639961603365, 'Pearson': 0.9679228999601346} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_OC_Forecast', 'Length': 59, 'MAPE': 0.0588, 'RMSE': 97.29737746217495, 'MAE': 77.49152542372882, 'SMAPE': 0.0576, 'ErrorMean': 18.0, 'ErrorStdDev': 95.61788358365264, 'R2': 0.9468779874911686, 'Pearson': 0.9740333322253129} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 18.5515239238739 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_female' Length=59 Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_NSW_female' Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 @@ -246,21 +217,8 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.73301100730896 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.778597354888916 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.7971949577331543 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.756537914276123 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.597900390625 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.5433483123779297 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.4688553810119629 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.505427598953247 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 1.5906383991241455 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.5176749229431152 diff --git a/tests/references/hierarchical_test_grouped_signals_TD.log b/tests/references/hierarchical_test_grouped_signals_TD.log index 56d52a88a..6fc2ae769 100644 --- a/tests/references/hierarchical_test_grouped_signals_TD.log +++ b/tests/references/hierarchical_test_grouped_signals_TD.log @@ -1,35 +1,9 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_TRAINING 'NSW_female' -INFO:pyaf.std:START_TRAINING 'NSW_male' -INFO:pyaf.std:START_TRAINING 'VIC_male' -INFO:pyaf.std:START_TRAINING 'VIC_female' -INFO:pyaf.std:START_TRAINING '_female' -INFO:pyaf.std:START_TRAINING '_male' -INFO:pyaf.std:START_TRAINING '_' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_female' 7.410377740859985 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_female' 7.5371809005737305 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_female' 7.587181091308594 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_' 7.576600074768066 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '_male' 7.600994825363159 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_male' 7.686426639556885 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_male' 7.706680536270142 +INFO:pyaf.std:START_TRAINING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 16.84771156311035 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.887709379196167 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.871171236038208 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.8354153633117676 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.8388586044311523 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.6600697040557861 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.7297792434692383 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.6357650756835938 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.6979801654815674 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['TD'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD @@ -54,40 +28,37 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Index', 'NSW_female', 'NSW_female 'NSW_male_PHA_TD_Forecast', 'VIC_male_PHA_TD_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['_female', '_male']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['_female', '_male']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['_']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0782, 'RMSE': 61.88312394961418, 'MAE': 47.33891833501301, 'SMAPE': 0.0765, 'ErrorMean': 3.1970833853646603, 'ErrorStdDev': 61.800482907420204, 'R2': 0.911769621603849, 'Pearson': 0.9574962766489252} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0782, 'RMSE': 61.88312394961418, 'MAE': 47.33891833501301, 'SMAPE': 0.0765, 'ErrorMean': 3.1970833853646603, 'ErrorStdDev': 61.800482907420204, 'R2': 0.911769621603849, 'Pearson': 0.9574962766489252} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0799, 'RMSE': 62.016771833343164, 'MAE': 47.68929397311254, 'SMAPE': 0.0775, 'ErrorMean': 8.015543376062395, 'ErrorStdDev': 61.49659383262953, 'R2': 0.9113881109148605, 'Pearson': 0.9574962766489251} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_female_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0799, 'RMSE': 62.016771833343164, 'MAE': 47.68929397311254, 'SMAPE': 0.0775, 'ErrorMean': 8.015543376062395, 'ErrorStdDev': 61.49659383262953, 'R2': 0.9113881109148605, 'Pearson': 0.9574962766489251} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 70.39300898637983, 'MAE': 55.09053975912032, 'SMAPE': 0.0667, 'ErrorMean': 7.628548216350093, 'ErrorStdDev': 69.97843215068035, 'R2': 0.9319325977712878, 'Pearson': 0.9663700630732055} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_male_AHP_TD_Forecast', 'Length': 59, 'MAPE': 0.0683, 'RMSE': 70.39300898637983, 'MAE': 55.09053975912032, 'SMAPE': 0.0667, 'ErrorMean': 7.628548216350093, 'ErrorStdDev': 69.97843215068035, 'R2': 0.9319325977712878, 'Pearson': 0.9663700630732055} 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-INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.9758202550657132} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.9758202550657132} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 9.655235052108765 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.975820255065713} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '_male_PHA_TD_Forecast', 'Length': 59, 'MAPE': 0.0576, 'RMSE': 93.96986777755437, 'MAE': 74.89426726951811, 'SMAPE': 0.0565, 'ErrorMean': 17.75796101051672, 'ErrorStdDev': 92.27670817102232, 'R2': 0.9504493355809386, 'Pearson': 0.975820255065713} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 20.3052077293396 INFO:pyaf.std:TIME_DETAIL TimeVariable='Index' TimeMin=1933 TimeMax=1991 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_female' Length=59 Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_NSW_female' Min=270 Max=1000 Mean=650.9322033898305 StdDev=208.3354420653596 @@ -264,22 +235,9 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'NSW_female'), (0, 'NSW_male'), (0, 'VIC_female'), (0, 'VIC_male'), (1, '_female'), (1, '_male'), (2, '_')] -INFO:pyaf.std:START_FORECASTING 'NSW_female' -INFO:pyaf.std:START_FORECASTING 'NSW_male' -INFO:pyaf.std:START_FORECASTING 'VIC_female' -INFO:pyaf.std:START_FORECASTING 'VIC_male' -INFO:pyaf.std:START_FORECASTING '_female' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_male' 0.7357490062713623 -INFO:pyaf.std:START_FORECASTING '_male' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_female' 0.894660472869873 -INFO:pyaf.std:START_FORECASTING '_' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_female' 0.778484582901001 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_male' 0.7506077289581299 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_female' 0.6850986480712891 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_male' 0.4481852054595947 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '_' 0.4062643051147461 +INFO:pyaf.std:START_FORECASTING '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['NSW_female', 'NSW_male', 'VIC_female', 'VIC_male', '_female', '_male', '_']' 2.5330307483673096 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['TD'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 1.5645689964294434 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.583409309387207 diff --git a/tests/references/hierarchical_test_hierarchy_AU.log b/tests/references/hierarchical_test_hierarchy_AU.log index 380aa6b1b..2089af969 100644 --- a/tests/references/hierarchical_test_hierarchy_AU.log +++ b/tests/references/hierarchical_test_hierarchy_AU.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] +INFO:pyaf.std:START_TRAINING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' City State Country 0 Sydney NSW_State Australia 1 NSW NSW_State Australia @@ -9,60 +9,10 @@ INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneG 5 QLD QLD_State Australia 6 Capitals Other_State Australia 7 Other Other_State Australia -INFO:pyaf.std:START_TRAINING 'BrisbaneGC' -INFO:pyaf.std:START_TRAINING 'Melbourne' -INFO:pyaf.std:START_TRAINING 'NSW' -INFO:pyaf.std:START_TRAINING 'Capitals' -INFO:pyaf.std:START_TRAINING 'Other' -INFO:pyaf.std:START_TRAINING 'QLD' -INFO:pyaf.std:START_TRAINING 'Sydney' -INFO:pyaf.std:START_TRAINING 'VIC' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BrisbaneGC' 12.15464186668396 -INFO:pyaf.std:START_TRAINING 'NSW_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW' 12.227551460266113 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Melbourne' 12.253730058670044 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other' 12.242761373519897 -INFO:pyaf.std:START_TRAINING 'Other_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC' 12.249642372131348 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Sydney' 12.27659296989441 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD' 12.294761657714844 -INFO:pyaf.std:START_TRAINING 'QLD_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Capitals' 12.333317518234253 -INFO:pyaf.std:START_TRAINING 'VIC_State' -INFO:pyaf.std:START_TRAINING 'Australia' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other_State' 6.863790273666382 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_State' 6.879067897796631 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_State' 7.139341354370117 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_State' 7.018179655075073 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Australia' 6.945321798324585 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 42.48564338684082 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 0.911921501159668 -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 0.8515059947967529 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 1.0508089065551758 -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 0.8541808128356934 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 0.8739001750946045 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 0.8586137294769287 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.0259768962860107 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.9016165733337402 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.9068992137908936 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.708533525466919 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.4730713367462158 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.42303037643432617 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.2433176040649414 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 5.847686052322388 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD @@ -94,38 +44,35 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Date', 'BrisbaneGC', 'BrisbaneGC_ 'Australia_BU_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0307, 'RMSE': 2649.2533389732453, 'MAE': 2160.6711851064942, 'SMAPE': 0.0305, 'ErrorMean': 39.733151644220264, 'ErrorStdDev': 2648.955365936034, 'R2': 0.8873042283973063, 'Pearson': 0.9420309611478158} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0307, 'RMSE': 2649.2533389732453, 'MAE': 2160.6711851064942, 'SMAPE': 0.0305, 'ErrorMean': 39.733151644220264, 'ErrorStdDev': 2648.955365936034, 'R2': 0.8873042283973063, 'Pearson': 0.9420309611478158} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615456} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615456} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Capitals_BU_Forecast', 'Length': 44, 'MAPE': 0.0577, 'RMSE': 608.1757558469427, 'MAE': 460.8863636363636, 'SMAPE': 0.0582, 'ErrorMean': -66.79545454545455, 'ErrorStdDev': 604.4965816711176, 'R2': 0.5328349460551864, 'Pearson': 0.7348640156077884} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Capitals_BU_Forecast', 'Length': 44, 'MAPE': 0.0577, 'RMSE': 608.1757558469427, 'MAE': 460.8863636363636, 'SMAPE': 0.0582, 'ErrorMean': -66.79545454545455, 'ErrorStdDev': 604.4965816711176, 'R2': 0.5328349460551864, 'Pearson': 0.7348640156077884} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0307, 'RMSE': 2649.2533389732457, 'MAE': 2160.671185106495, 'SMAPE': 0.0305, 'ErrorMean': 39.73315164421927, 'ErrorStdDev': 2648.9553659360345, 'R2': 0.8873042283973062, 'Pearson': 0.9420309611478157} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0307, 'RMSE': 2649.2533389732457, 'MAE': 2160.671185106495, 'SMAPE': 0.0305, 'ErrorMean': 39.73315164421927, 'ErrorStdDev': 2648.9553659360345, 'R2': 0.8873042283973062, 'Pearson': 0.9420309611478157} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615457} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615457} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Capitals_BU_Forecast', 'Length': 44, 'MAPE': 0.0577, 'RMSE': 608.1757558469427, 'MAE': 460.8863636363636, 'SMAPE': 0.0582, 'ErrorMean': -66.79545454545455, 'ErrorStdDev': 604.4965816711176, 'R2': 0.5328349460551864, 'Pearson': 0.7348640156077881} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Capitals_BU_Forecast', 'Length': 44, 'MAPE': 0.0577, 'RMSE': 608.1757558469427, 'MAE': 460.8863636363636, 'SMAPE': 0.0582, 'ErrorMean': -66.79545454545455, 'ErrorStdDev': 604.4965816711176, 'R2': 0.5328349460551864, 'Pearson': 0.7348640156077881} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Melbourne_BU_Forecast', 'Length': 44, 'MAPE': 0.0737, 'RMSE': 434.35490306681453, 'MAE': 348.45454545454544, 'SMAPE': 0.0743, 'ErrorMean': -59.22727272727273, 'ErrorStdDev': 430.29793397536906, 'R2': 0.4015155887084072, 'Pearson': 0.6562149335446998} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Melbourne_BU_Forecast', 'Length': 44, 'MAPE': 0.0737, 'RMSE': 434.35490306681453, 'MAE': 348.45454545454544, 'SMAPE': 0.0743, 'ErrorMean': -59.22727272727273, 'ErrorStdDev': 430.29793397536906, 'R2': 0.4015155887084072, 'Pearson': 0.6562149335446998} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_BU_Forecast', 'Length': 44, 'MAPE': 0.0465, 'RMSE': 987.8115734012507, 'MAE': 718.0681818181819, 'SMAPE': 0.046, 'ErrorMean': 73.47727272727273, 'ErrorStdDev': 985.0750199543263, 'R2': 0.8925743901564223, 'Pearson': 0.9451029550194883} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_BU_Forecast', 'Length': 44, 'MAPE': 0.0465, 'RMSE': 987.8115734012507, 'MAE': 718.0681818181819, 'SMAPE': 0.046, 'ErrorMean': 73.47727272727273, 'ErrorStdDev': 985.0750199543263, 'R2': 0.8925743901564223, 'Pearson': 0.9451029550194883} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0408, 'RMSE': 1144.1632267718876, 'MAE': 864.2155932819365, 'SMAPE': 0.0407, 'ErrorMean': -52.84151316113036, 'ErrorStdDev': 1142.9423712435812, 'R2': 0.8909662627803002, 'Pearson': 0.9444143200613907} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0408, 'RMSE': 1144.1632267718876, 'MAE': 864.2155932819365, 'SMAPE': 0.0407, 'ErrorMean': -52.84151316113036, 'ErrorStdDev': 1142.9423712435812, 'R2': 0.8909662627803002, 'Pearson': 0.9444143200613907} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_BU_Forecast', 'Length': 44, 'MAPE': 0.0465, 'RMSE': 987.8115734012507, 'MAE': 718.0681818181819, 'SMAPE': 0.046, 'ErrorMean': 73.47727272727273, 'ErrorStdDev': 985.0750199543263, 'R2': 0.8925743901564223, 'Pearson': 0.9451029550194885} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_BU_Forecast', 'Length': 44, 'MAPE': 0.0465, 'RMSE': 987.8115734012507, 'MAE': 718.0681818181819, 'SMAPE': 0.046, 'ErrorMean': 73.47727272727273, 'ErrorStdDev': 985.0750199543263, 'R2': 0.8925743901564223, 'Pearson': 0.9451029550194885} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0408, 'RMSE': 1144.1632267718876, 'MAE': 864.2155932819365, 'SMAPE': 0.0407, 'ErrorMean': -52.84151316113036, 'ErrorStdDev': 1142.9423712435812, 'R2': 0.8909662627803002, 'Pearson': 0.9444143200613906} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0408, 'RMSE': 1144.1632267718876, 'MAE': 864.2155932819365, 'SMAPE': 0.0407, 'ErrorMean': -52.84151316113036, 'ErrorStdDev': 1142.9423712435812, 'R2': 0.8909662627803002, 'Pearson': 0.9444143200613906} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Other_BU_Forecast', 'Length': 44, 'MAPE': 0.0692, 'RMSE': 955.3693835846474, 'MAE': 721.9318181818181, 'SMAPE': 0.0676, 'ErrorMean': 59.93181818181818, 'ErrorStdDev': 953.4877221340245, 'R2': 0.29570938060484053, 'Pearson': 0.5464561094756151} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Other_BU_Forecast', 'Length': 44, 'MAPE': 0.0692, 'RMSE': 955.3693835846474, 'MAE': 721.9318181818181, 'SMAPE': 0.0676, 'ErrorMean': 59.93181818181818, 'ErrorStdDev': 953.4877221340245, 'R2': 0.29570938060484053, 'Pearson': 0.5464561094756151} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Other_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0515, 'RMSE': 1194.0341094564487, 'MAE': 937.5909090909091, 'SMAPE': 0.051, 'ErrorMean': -6.863636363636363, 'ErrorStdDev': 1194.014382258992, 'R2': 0.440331329148694, 'Pearson': 0.6638984782397178} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Other_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0515, 'RMSE': 1194.0341094564487, 'MAE': 937.5909090909091, 'SMAPE': 0.051, 'ErrorMean': -6.863636363636363, 'ErrorStdDev': 1194.014382258992, 'R2': 0.440331329148694, 'Pearson': 0.6638984782397178} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Other_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0515, 'RMSE': 1194.0341094564487, 'MAE': 937.5909090909091, 'SMAPE': 0.051, 'ErrorMean': -6.863636363636363, 'ErrorStdDev': 1194.014382258992, 'R2': 0.440331329148694, 'Pearson': 0.6638984782397177} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Other_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0515, 'RMSE': 1194.0341094564487, 'MAE': 937.5909090909091, 'SMAPE': 0.051, 'ErrorMean': -6.863636363636363, 'ErrorStdDev': 1194.014382258992, 'R2': 0.440331329148694, 'Pearson': 0.6638984782397177} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_BU_Forecast', 'Length': 44, 'MAPE': 0.0654, 'RMSE': 907.8588822058194, 'MAE': 701.2954545454545, 'SMAPE': 0.065, 'ErrorMean': -37.38636363636363, 'ErrorStdDev': 907.0887552020748, 'R2': 0.7511816244233342, 'Pearson': 0.8674625497115258} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_BU_Forecast', 'Length': 44, 'MAPE': 0.0654, 'RMSE': 907.8588822058194, 'MAE': 701.2954545454545, 'SMAPE': 0.065, 'ErrorMean': -37.38636363636363, 'ErrorStdDev': 907.0887552020748, 'R2': 0.7511816244233342, 'Pearson': 0.8674625497115258} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0512, 'RMSE': 1153.4137080068808, 'MAE': 934.5909090909091, 'SMAPE': 0.0504, 'ErrorMean': 92.13636363636364, 'ErrorStdDev': 1149.727825319562, 'R2': 0.7396794284983607, 'Pearson': 0.8613958375031755} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0512, 'RMSE': 1153.4137080068808, 'MAE': 934.5909090909091, 'SMAPE': 0.0504, 'ErrorMean': 92.13636363636364, 'ErrorStdDev': 1149.727825319562, 'R2': 0.7396794284983607, 'Pearson': 0.8613958375031755} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0512, 'RMSE': 1153.4137080068808, 'MAE': 934.5909090909091, 'SMAPE': 0.0504, 'ErrorMean': 92.13636363636364, 'ErrorStdDev': 1149.727825319562, 'R2': 0.7396794284983607, 'Pearson': 0.8613958375031756} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0512, 'RMSE': 1153.4137080068808, 'MAE': 934.5909090909091, 'SMAPE': 0.0504, 'ErrorMean': 92.13636363636364, 'ErrorStdDev': 1149.727825319562, 'R2': 0.7396794284983607, 'Pearson': 0.8613958375031756} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Sydney_BU_Forecast', 'Length': 44, 'MAPE': 0.0607, 'RMSE': 558.1781992520188, 'MAE': 370.6134016376204, 'SMAPE': 0.0622, 'ErrorMean': -126.31878588840294, 'ErrorStdDev': 543.6970355371695, 'R2': 0.4725848925563002, 'Pearson': 0.7104826236354815} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Sydney_BU_Forecast', 'Length': 44, 'MAPE': 0.0607, 'RMSE': 558.1781992520188, 'MAE': 370.6134016376204, 'SMAPE': 0.0622, 'ErrorMean': -126.31878588840294, 'ErrorStdDev': 543.6970355371695, 'R2': 0.4725848925563002, 'Pearson': 0.7104826236354815} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_BU_Forecast', 'Length': 44, 'MAPE': 0.0516, 'RMSE': 530.0781460398937, 'MAE': 424.20454545454544, 'SMAPE': 0.0511, 'ErrorMean': 14.75, 'ErrorStdDev': 529.8728889168523, 'R2': 0.9608523426800629, 'Pearson': 0.9803775115824175} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_BU_Forecast', 'Length': 44, 'MAPE': 0.0516, 'RMSE': 530.0781460398937, 'MAE': 424.20454545454544, 'SMAPE': 0.0511, 'ErrorMean': 14.75, 'ErrorStdDev': 529.8728889168523, 'R2': 0.9608523426800629, 'Pearson': 0.9803775115824175} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0417, 'RMSE': 680.2621653718246, 'MAE': 527.4772727272727, 'SMAPE': 0.0417, 'ErrorMean': -44.47727272727273, 'ErrorStdDev': 678.8065894252261, 'R2': 0.9499726039950437, 'Pearson': 0.9748137776870249} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0417, 'RMSE': 680.2621653718246, 'MAE': 527.4772727272727, 'SMAPE': 0.0417, 'ErrorMean': -44.47727272727273, 'ErrorStdDev': 678.8065894252261, 'R2': 0.9499726039950437, 'Pearson': 0.9748137776870249} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 23.76754093170166 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_BU_Forecast', 'Length': 44, 'MAPE': 0.0516, 'RMSE': 530.0781460398937, 'MAE': 424.20454545454544, 'SMAPE': 0.0511, 'ErrorMean': 14.75, 'ErrorStdDev': 529.8728889168523, 'R2': 0.9608523426800629, 'Pearson': 0.9803775115824177} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_BU_Forecast', 'Length': 44, 'MAPE': 0.0516, 'RMSE': 530.0781460398937, 'MAE': 424.20454545454544, 'SMAPE': 0.0511, 'ErrorMean': 14.75, 'ErrorStdDev': 529.8728889168523, 'R2': 0.9608523426800629, 'Pearson': 0.9803775115824177} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0417, 'RMSE': 680.2621653718246, 'MAE': 527.4772727272727, 'SMAPE': 0.0417, 'ErrorMean': -44.47727272727273, 'ErrorStdDev': 678.8065894252261, 'R2': 0.9499726039950437, 'Pearson': 0.9748137776870248} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0417, 'RMSE': 680.2621653718246, 'MAE': 527.4772727272727, 'SMAPE': 0.0417, 'ErrorMean': -44.47727272727273, 'ErrorStdDev': 678.8065894252261, 'R2': 0.9499726039950437, 'Pearson': 0.9748137776870248} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 49.16141748428345 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='BrisbaneGC' Length=44 Min=5616 Max=9970 Mean=7945.977272727273 StdDev=953.1993850529328 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_BrisbaneGC' Min=5616 Max=9970 Mean=7945.977272727273 StdDev=953.1993850529328 @@ -337,8 +284,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_NSW_State_LinearTrend_residue_Seasonal_MonthOfYea INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.034 MAPE_Forecast=0.034 MAPE_Test=0.034 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0338 SMAPE_Forecast=0.0338 SMAPE_Test=0.0338 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1808 MASE_Forecast=0.1808 MASE_Test=0.1808 -INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843487 L1_Forecast=727.7548391843487 L1_Test=727.7548391843487 -INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912562 L2_Forecast=979.3716750912562 L2_Test=979.3716750912562 +INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843488 L1_Forecast=727.7548391843488 L1_Test=727.7548391843488 +INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912563 L2_Forecast=979.3716750912563 L2_Test=979.3716750912563 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -347,7 +294,7 @@ INFO:pyaf.std:TREND_DETAIL_START INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (23488.339004277248, array([-2964.69662101])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717456 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717438 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END @@ -452,33 +399,8 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 1.039536952972412 -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 1.0012400150299072 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.1627497673034668 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 1.181838035583496 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 1.081838846206665 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 1.0939335823059082 -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.0358467102050781 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.8783268928527832 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.9706869125366211 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.752068042755127 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.536144495010376 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.37528038024902344 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.3558778762817383 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 7.666489839553833 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.1928369998931885 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 7.732038736343384 diff --git a/tests/references/hierarchical_test_hierarchy_AU_AllMethods.log b/tests/references/hierarchical_test_hierarchy_AU_AllMethods.log index 1ec657390..a3c861cb9 100644 --- a/tests/references/hierarchical_test_hierarchy_AU_AllMethods.log +++ b/tests/references/hierarchical_test_hierarchy_AU_AllMethods.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] +INFO:pyaf.std:START_TRAINING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' City State Country 0 Sydney NSW_State Australia 1 NSW NSW_State Australia @@ -9,60 +9,10 @@ INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneG 5 QLD QLD_State Australia 6 Capitals Other_State Australia 7 Other Other_State Australia -INFO:pyaf.std:START_TRAINING 'BrisbaneGC' -INFO:pyaf.std:START_TRAINING 'Melbourne' -INFO:pyaf.std:START_TRAINING 'Capitals' -INFO:pyaf.std:START_TRAINING 'NSW' -INFO:pyaf.std:START_TRAINING 'Other' -INFO:pyaf.std:START_TRAINING 'QLD' -INFO:pyaf.std:START_TRAINING 'Sydney' -INFO:pyaf.std:START_TRAINING 'VIC' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BrisbaneGC' 13.756000280380249 -INFO:pyaf.std:START_TRAINING 'NSW_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Melbourne' 13.993942022323608 -INFO:pyaf.std:START_TRAINING 'Other_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Capitals' 14.062113285064697 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW' 14.042025804519653 -INFO:pyaf.std:START_TRAINING 'QLD_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other' 14.121605157852173 -INFO:pyaf.std:START_TRAINING 'VIC_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC' 14.115411043167114 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Sydney' 14.155516147613525 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD' 14.195281744003296 -INFO:pyaf.std:START_TRAINING 'Australia' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_State' 7.43557071685791 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Australia' 7.4942543506622314 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_State' 8.060273885726929 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other_State' 7.869746923446655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_State' 7.8715009689331055 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 42.34905481338501 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.9482285976409912 -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.066910743713379 -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 1.2054944038391113 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 1.2668380737304688 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 1.2568416595458984 -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 1.2631151676177979 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.2315561771392822 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 1.0629630088806152 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 1.0612993240356445 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.832880973815918 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.6190931797027588 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.4240107536315918 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.311129093170166 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 5.615399360656738 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -82,142 +32,139 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Date', 'BrisbaneGC', 'BrisbaneGC_ 'VIC_State_OC_Forecast', 'Australia_OC_Forecast'], dtype='object', length=118) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0307, 'RMSE': 2649.2533389732453, 'MAE': 2160.6711851064942, 'SMAPE': 0.0305, 'ErrorMean': 39.733151644220264, 'ErrorStdDev': 2648.955365936034, 'R2': 0.8873042283973063, 'Pearson': 0.9420309611478158} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0307, 'RMSE': 2649.2533389732453, 'MAE': 2160.6711851064942, 'SMAPE': 0.0305, 'ErrorMean': 39.733151644220264, 'ErrorStdDev': 2648.955365936034, 'R2': 0.8873042283973063, 'Pearson': 0.9420309611478158} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2681.5655374823823, 'MAE': 2131.612691344388, 'SMAPE': 0.0301, 'ErrorMean': 228.73315164422027, 'ErrorStdDev': 2671.792446495813, 'R2': 0.8845384264642633, 'Pearson': 0.9414055707483306} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2681.5655374823823, 'MAE': 2131.612691344388, 'SMAPE': 0.0301, 'ErrorMean': 228.73315164422027, 'ErrorStdDev': 2671.792446495813, 'R2': 0.8845384264642633, 'Pearson': 0.9414055707483306} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.031, 'RMSE': 2862.61287413609, 'MAE': 2153.1032257967736, 'SMAPE': 0.0305, 'ErrorMean': 578.6910966231361, 'ErrorStdDev': 2803.510135858046, 'R2': 0.8684212122377221, 'Pearson': 0.9349191863682207} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.031, 'RMSE': 2862.61287413609, 'MAE': 2153.1032257967736, 'SMAPE': 0.0305, 'ErrorMean': 578.6910966231361, 'ErrorStdDev': 2803.510135858046, 'R2': 0.8684212122377221, 'Pearson': 0.9349191863682207} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0809, 'RMSE': 724.7303370979117, 'MAE': 616.4243205312524, 'SMAPE': 0.0784, 'ErrorMean': 169.3104648748422, 'ErrorStdDev': 704.6758318503039, 'R2': 0.42192341928602084, 'Pearson': 0.6908359461872079} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0809, 'RMSE': 724.7303370979117, 'MAE': 616.4243205312524, 'SMAPE': 0.0784, 'ErrorMean': 169.3104648748422, 'ErrorStdDev': 704.6758318503039, 'R2': 0.42192341928602084, 'Pearson': 0.6908359461872079} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615456} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615456} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 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0.8873042283973062, 'Pearson': 0.9420309611478157} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2681.5655374823828, 'MAE': 2131.6126913443895, 'SMAPE': 0.0301, 'ErrorMean': 228.73315164421928, 'ErrorStdDev': 2671.7924464958137, 'R2': 0.8845384264642633, 'Pearson': 0.9414055707483305} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2681.5655374823828, 'MAE': 2131.6126913443895, 'SMAPE': 0.0301, 'ErrorMean': 228.73315164421928, 'ErrorStdDev': 2671.7924464958137, 'R2': 0.8845384264642633, 'Pearson': 0.9414055707483305} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.031, 'RMSE': 2862.612874136096, 'MAE': 2153.1032257967713, 'SMAPE': 0.0305, 'ErrorMean': 578.6910966231643, 'ErrorStdDev': 2803.510135858046, 'R2': 0.8684212122377215, 'Pearson': 0.9349191863682205} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.031, 'RMSE': 2862.612874136096, 'MAE': 2153.1032257967713, 'SMAPE': 0.0305, 'ErrorMean': 578.6910966231643, 'ErrorStdDev': 2803.510135858046, 'R2': 0.8684212122377215, 'Pearson': 0.9349191863682205} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980038} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980038} 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+INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615457} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_MO_Forecast', 'Length': 44, 'MAPE': 0.0949, 'RMSE': 891.436491265439, 'MAE': 753.2690381856381, 'SMAPE': 0.0931, 'ErrorMean': 32.514564742801234, 'ErrorStdDev': 890.8433201406538, 'R2': 0.12539227441774858, 'Pearson': 0.5041140331831235} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_MO_Forecast', 'Length': 44, 'MAPE': 0.0949, 'RMSE': 891.436491265439, 'MAE': 753.2690381856381, 'SMAPE': 0.0931, 'ErrorMean': 32.514564742801234, 'ErrorStdDev': 890.8433201406538, 'R2': 0.12539227441774858, 'Pearson': 0.5041140331831235} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_OC_Forecast', 'Length': 44, 'MAPE': 0.0626, 'RMSE': 629.3346461015539, 'MAE': 468.37060043602963, 'SMAPE': 0.0602, 'ErrorMean': 177.9666041559154, 'ErrorStdDev': 603.6472352201903, 'R2': 0.5640910606579395, 'Pearson': 0.7740043728403107} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_OC_Forecast', 'Length': 44, 'MAPE': 0.0626, 'RMSE': 629.3346461015539, 'MAE': 468.37060043602963, 'SMAPE': 0.0602, 'ErrorMean': 177.9666041559154, 'ErrorStdDev': 603.6472352201903, 'R2': 0.5640910606579395, 'Pearson': 0.7740043728403107} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0782, 'RMSE': 707.5583460073358, 'MAE': 599.3252224154016, 'SMAPE': 0.0766, 'ErrorMean': 81.05206329976524, 'ErrorStdDev': 702.9006871809755, 'R2': 0.44899313581762723, 'Pearson': 0.6908359461872081} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0782, 'RMSE': 707.5583460073358, 'MAE': 599.3252224154016, 'SMAPE': 0.0766, 'ErrorMean': 81.05206329976524, 'ErrorStdDev': 702.9006871809755, 'R2': 0.44899313581762723, 'Pearson': 0.6908359461872081} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Capitals_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0652, 'RMSE': 630.2134648393815, 'MAE': 509.8819671002353, 'SMAPE': 0.0641, 'ErrorMean': 117.49743279598165, 'ErrorStdDev': 619.1634392882157, 'R2': 0.4983653853935368, 'Pearson': 0.7343026400806666} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Capitals_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0652, 'RMSE': 630.2134648393815, 'MAE': 509.8819671002353, 'SMAPE': 0.0641, 'ErrorMean': 117.49743279598165, 'ErrorStdDev': 619.1634392882157, 'R2': 0.4983653853935368, 'Pearson': 0.7343026400806666} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Capitals_BU_Forecast', 'Length': 44, 'MAPE': 0.0577, 'RMSE': 608.1757558469427, 'MAE': 460.8863636363636, 'SMAPE': 0.0582, 'ErrorMean': -66.79545454545455, 'ErrorStdDev': 604.4965816711176, 'R2': 0.5328349460551864, 'Pearson': 0.7348640156077884} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Capitals_BU_Forecast', 'Length': 44, 'MAPE': 0.0577, 'RMSE': 608.1757558469427, 'MAE': 460.8863636363636, 'SMAPE': 0.0582, 'ErrorMean': -66.79545454545455, 'ErrorStdDev': 604.4965816711176, 'R2': 0.5328349460551864, 'Pearson': 0.7348640156077884} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_OC_Forecast', 'Length': 44, 'MAPE': 0.0626, 'RMSE': 629.3346461015509, 'MAE': 468.370600436027, 'SMAPE': 0.0602, 'ErrorMean': 177.96660415590512, 'ErrorStdDev': 603.6472352201903, 'R2': 0.5640910606579435, 'Pearson': 0.7740043728403107} 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INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_BrisbaneGC' Min=5616 Max=9970 Mean=7945.977272727273 StdDev=953.1993850529328 @@ -429,8 +376,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_NSW_State_LinearTrend_residue_Seasonal_MonthOfYea INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.034 MAPE_Forecast=0.034 MAPE_Test=0.034 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0338 SMAPE_Forecast=0.0338 SMAPE_Test=0.0338 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1808 MASE_Forecast=0.1808 MASE_Test=0.1808 -INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843487 L1_Forecast=727.7548391843487 L1_Test=727.7548391843487 -INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912562 L2_Forecast=979.3716750912562 L2_Test=979.3716750912562 +INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843488 L1_Forecast=727.7548391843488 L1_Test=727.7548391843488 +INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912563 L2_Forecast=979.3716750912563 L2_Test=979.3716750912563 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -439,7 +386,7 @@ INFO:pyaf.std:TREND_DETAIL_START INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (23488.339004277248, array([-2964.69662101])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717456 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717438 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END @@ -544,51 +491,441 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 0.7383577823638916 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.8462839126586914 -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 0.90157151222229 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 1.0393915176391602 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 1.008680820465088 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 1.0083563327789307 -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.0164263248443604 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 1.057133674621582 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 1.0472588539123535 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.870288610458374 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.6304359436035156 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.4327201843261719 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.23011469841003418 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 9.705420017242432 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.709299325942993 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 9.996079921722412 -{'Structure': {0: {'Sydney': [], 'NSW': [], 'Melbourne': [], 'VIC': [], 'BrisbaneGC': [], 'QLD': [], 'Capitals': [], 'Other': []}, 1: {'NSW_State': ['NSW', 'Sydney'], 'VIC_State': ['Melbourne', 'VIC'], 'QLD_State': ['BrisbaneGC', 'QLD'], 'Other_State': ['Capitals', 'Other']}, 2: {'Australia': ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']}}, 'Models': {'BrisbaneGC': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'BrisbaneGC', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_BrisbaneGC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.059', 'MASE': '0.3197', 'MAE': '440.84090909090907', 'RMSE': '614.1944650589835', 'COMPLEXITY': '4'}}, 'Capitals': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'Capitals', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_Capitals_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0577', 'MASE': '0.4024', 'MAE': '460.8863636363636', 'RMSE': '608.1757558469427', 'COMPLEXITY': '4'}}, 'Melbourne': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'Melbourne', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_Melbourne_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0737', 'MASE': '0.5573', 'MAE': '348.45454545454544', 'RMSE': '434.35490306681453', 'COMPLEXITY': '4'}}, 'NSW': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'NSW', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_NSW_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0465', 'MASE': '0.2009', 'MAE': '718.0681818181819', 'RMSE': '987.8115734012507', 'COMPLEXITY': '4'}}, 'Other': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'Other', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_Other_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0692', 'MASE': '0.5248', 'MAE': '721.9318181818181', 'RMSE': '955.3693835846474', 'COMPLEXITY': '4'}}, 'QLD': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'QLD', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_QLD_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0654', 'MASE': '0.299', 'MAE': '701.2954545454545', 'RMSE': '907.8588822058194', 'COMPLEXITY': '4'}}, 'Sydney': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'Sydney', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_Sydney_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'LinearTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0607', 'MASE': '0.5732', 'MAE': '370.6134016376204', 'RMSE': '558.1781992520188', 'COMPLEXITY': '20'}}, 'VIC': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'VIC', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0516', 'MASE': '0.1283', 'MAE': '424.20454545454544', 'RMSE': '530.0781460398937', 'COMPLEXITY': '4'}}, 'NSW_State': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'NSW_State', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_NSW_State_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'LinearTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.034', 'MASE': '0.1808', 'MAE': '727.7548391843487', 'RMSE': '979.3716750912562', 'COMPLEXITY': '20'}}, 'Other_State': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'Other_State', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_Other_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0503', 'MASE': '0.4414', 'MAE': '907.9545454545455', 'RMSE': '1240.6616819628584', 'COMPLEXITY': '4'}}, 'QLD_State': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'QLD_State', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_QLD_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0506', 'MASE': '0.2822', 'MAE': '925.2272727272727', 'RMSE': '1152.847206622882', 'COMPLEXITY': '4'}}, 'VIC_State': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'VIC_State', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_VIC_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0409', 'MASE': '0.1362', 'MAE': '511.6136363636364', 'RMSE': '688.3676178269336', 'COMPLEXITY': '4'}}, 'Australia': {'Dataset': {'Time': {'TimeVariable': 'Date', 'TimeMinMax': ['1998-01-01 00:00:00', '2008-10-01 00:00:00'], 'Horizon': 12}, 'Signal': 'Australia', 'Training_Signal_Length': 44}, 'Model': {'Best_Decomposition': '_Australia_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR', 'Signal_Transoformation': 'NoTransf', 'Trend': 'ConstantTrend', 'Cycle': 'Seasonal_MonthOfYear', 'AR_Model': 'NoAR'}, 'Model_Performance': {'MAPE': '0.0314', 'MASE': '0.2095', 'MAE': '2171.068181818182', 'RMSE': '2988.2555377830236', 'COMPLEXITY': '4'}}}} +{ + "Models": { + "Australia": { + "Dataset": { + "Signal": "Australia", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1998-01-01 00:00:00", + "2008-10-01 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 44 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Australia_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "2171.068181818182", + "MAPE": "0.0314", + "MASE": "0.2095", + "RMSE": "2988.2555377830236" + } + }, + "BrisbaneGC": { + "Dataset": { + "Signal": "BrisbaneGC", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1998-01-01 00:00:00", + "2008-10-01 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 44 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_BrisbaneGC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "440.84090909090907", + "MAPE": "0.059", + "MASE": "0.3197", + "RMSE": "614.1944650589835" + } + }, + "Capitals": { + "Dataset": { + "Signal": "Capitals", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1998-01-01 00:00:00", + "2008-10-01 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 44 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Capitals_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "460.8863636363636", + "MAPE": "0.0577", + "MASE": "0.4024", + "RMSE": "608.1757558469427" + } + }, + "Melbourne": { + "Dataset": { + "Signal": "Melbourne", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1998-01-01 00:00:00", + "2008-10-01 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 44 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Melbourne_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "348.45454545454544", + "MAPE": "0.0737", + "MASE": "0.5573", + "RMSE": "434.35490306681453" + } + }, + "NSW": { + "Dataset": { + "Signal": "NSW", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1998-01-01 00:00:00", + "2008-10-01 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 44 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_NSW_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "718.0681818181819", + "MAPE": "0.0465", + "MASE": "0.2009", + "RMSE": "987.8115734012507" + } + }, + "NSW_State": { + "Dataset": { + "Signal": "NSW_State", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1998-01-01 00:00:00", + "2008-10-01 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 44 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_NSW_State_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "727.7548391843488", + "MAPE": "0.034", + "MASE": "0.1808", + "RMSE": 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diff --git a/tests/references/hierarchical_test_hierarchy_AU_AllMethods_Exogenous_all_nodes.log b/tests/references/hierarchical_test_hierarchy_AU_AllMethods_Exogenous_all_nodes.log index a32ddae29..a4b013b95 100644 --- a/tests/references/hierarchical_test_hierarchy_AU_AllMethods_Exogenous_all_nodes.log +++ b/tests/references/hierarchical_test_hierarchy_AU_AllMethods_Exogenous_all_nodes.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] +INFO:pyaf.std:START_TRAINING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' City State Country 0 Sydney NSW_State Australia 1 NSW NSW_State Australia @@ -9,60 +9,10 @@ INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneG 5 QLD QLD_State Australia 6 Capitals Other_State Australia 7 Other Other_State Australia -INFO:pyaf.std:START_TRAINING 'BrisbaneGC' -INFO:pyaf.std:START_TRAINING 'Capitals' -INFO:pyaf.std:START_TRAINING 'Melbourne' -INFO:pyaf.std:START_TRAINING 'NSW' -INFO:pyaf.std:START_TRAINING 'Sydney' -INFO:pyaf.std:START_TRAINING 'Other' -INFO:pyaf.std:START_TRAINING 'QLD' -INFO:pyaf.std:START_TRAINING 'VIC' -INFO:pyaf.std:START_TRAINING 'Other_State' -INFO:pyaf.std:START_TRAINING 'NSW_State' -INFO:pyaf.std:START_TRAINING 'Australia' -INFO:pyaf.std:START_TRAINING 'QLD_State' -INFO:pyaf.std:START_TRAINING 'VIC_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other_State' 85.19737815856934 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other' 85.4001293182373 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Australia' 85.4191267490387 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW' 85.53999972343445 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_State' 85.45803952217102 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BrisbaneGC' 85.69148206710815 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD' 85.74842476844788 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Capitals' 85.94797396659851 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC' 85.85294008255005 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_State' 85.82337594032288 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_State' 85.99733185768127 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Sydney' 86.18470978736877 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Melbourne' 86.23639273643494 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 83.18322682380676 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.7058300971984863 -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.5292160511016846 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 1.0500831604003906 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 1.3928766250610352 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 1.1457622051239014 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 1.95035719871521 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.9056944847106934 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 1.4000425338745117 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.88338303565979 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.6934499740600586 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 1.2425332069396973 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.5407392978668213 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.2955160140991211 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 4.56397819519043 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -82,142 +32,139 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Date', 'BrisbaneGC', 'BrisbaneGC_ 'VIC_State_OC_Forecast', 'Australia_OC_Forecast'], dtype='object', length=118) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0287, 'RMSE': 2428.091877726309, 'MAE': 2030.3047869783127, 'SMAPE': 0.0286, 'ErrorMean': 135.8588360939605, 'ErrorStdDev': 2424.288048754864, 'R2': 0.9053346874604287, 'Pearson': 0.9517530706968891} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0287, 'RMSE': 2428.091877726309, 'MAE': 2030.3047869783127, 'SMAPE': 0.0286, 'ErrorMean': 135.8588360939605, 'ErrorStdDev': 2424.288048754864, 'R2': 0.9053346874604287, 'Pearson': 0.9517530706968891} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2669.152684412323, 'MAE': 2126.2066372463746, 'SMAPE': 0.0301, 'ErrorMean': 229.79545454545521, 'ErrorStdDev': 2659.242392444841, 'R2': 0.8856048858881285, 'Pearson': 0.9420978222872554} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2669.152684412323, 'MAE': 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_Capitals_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 -0.04808280678601823 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Capitals_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.03545110762656872 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Melbourne' Length=44 Min=3825 Max=5724 Mean=4728.227272727273 StdDev=561.4593348682911 @@ -290,8 +237,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Melbourne_ConstantTrend_residue_Seasonal_MonthOfY INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.061 MAPE_Forecast=0.061 MAPE_Test=0.061 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0609 SMAPE_Forecast=0.0609 SMAPE_Test=0.0609 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4592 MASE_Forecast=0.4592 MASE_Test=0.4592 -INFO:pyaf.std:MODEL_L1 L1_Fit=287.1198889426962 L1_Forecast=287.1198889426962 L1_Test=287.1198889426962 -INFO:pyaf.std:MODEL_L2 L2_Fit=355.3102138987579 L2_Forecast=355.3102138987579 L2_Test=355.3102138987579 +INFO:pyaf.std:MODEL_L1 L1_Fit=287.1198889426963 L1_Forecast=287.1198889426963 L1_Test=287.1198889426963 +INFO:pyaf.std:MODEL_L2 L2_Fit=355.310213898758 L2_Forecast=355.310213898758 L2_Test=355.310213898758 INFO:pyaf.std:MODEL_COMPLEXITY 15 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -303,16 +250,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:SEASONAL_MODEL_VALUES _Melbourne_ConstantTrend_residue_Seasonal_MonthOfYear -26.727272727272975 {1: 649.772727272727, 4: -500.227272727273, 7: -358.227272727273, 10: -28.227272727272975} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Date_day_name=Monday_Lag3 256.0715167923761 +INFO:pyaf.std:AR_MODEL_COEFF 1 Date_day_name=Monday_Lag3 256.071516792376 INFO:pyaf.std:AR_MODEL_COEFF 2 Date_dayofweek_Lag5 151.37549452636898 -INFO:pyaf.std:AR_MODEL_COEFF 3 Date_month_name=January_Lag8 -97.99089196607106 -INFO:pyaf.std:AR_MODEL_COEFF 4 Date_day_name=Sunday_Lag1 95.87605286948238 -INFO:pyaf.std:AR_MODEL_COEFF 5 Date_day_name=Thursday_Lag11 -83.09510087406181 -INFO:pyaf.std:AR_MODEL_COEFF 6 Date_week_Lag11 53.92114564718228 -INFO:pyaf.std:AR_MODEL_COEFF 7 Date_day_name=Thursday_Lag1 -44.75827742858273 -INFO:pyaf.std:AR_MODEL_COEFF 8 Date_day_name=Thursday_Lag2 -40.31021820818803 -INFO:pyaf.std:AR_MODEL_COEFF 9 Date_week_Lag10 32.80279073745405 -INFO:pyaf.std:AR_MODEL_COEFF 10 Date_week_Lag7 -17.041216827351704 +INFO:pyaf.std:AR_MODEL_COEFF 3 Date_month_name=January_Lag8 -97.99089196607125 +INFO:pyaf.std:AR_MODEL_COEFF 4 Date_day_name=Sunday_Lag1 95.8760528694824 +INFO:pyaf.std:AR_MODEL_COEFF 5 Date_day_name=Thursday_Lag11 -83.09510087406197 +INFO:pyaf.std:AR_MODEL_COEFF 6 Date_week_Lag11 53.92114564718218 +INFO:pyaf.std:AR_MODEL_COEFF 7 Date_day_name=Thursday_Lag1 -44.75827742858261 +INFO:pyaf.std:AR_MODEL_COEFF 8 Date_day_name=Thursday_Lag2 -40.31021820818813 +INFO:pyaf.std:AR_MODEL_COEFF 9 Date_week_Lag10 32.80279073745408 +INFO:pyaf.std:AR_MODEL_COEFF 10 Date_week_Lag7 -17.041216827351775 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW' Length=44 Min=12216 Max=22718 Mean=16128.272727272728 StdDev=3013.840025340945 @@ -401,7 +348,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Sydney_ConstantTrend_residue_Seasonal_MonthOfYear INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0495 MAPE_Forecast=0.0495 MAPE_Test=0.0495 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0495 SMAPE_Forecast=0.0495 SMAPE_Test=0.0495 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4559 MASE_Forecast=0.4559 MASE_Test=0.4559 -INFO:pyaf.std:MODEL_L1 L1_Fit=294.8046413812124 L1_Forecast=294.8046413812124 L1_Test=294.8046413812124 +INFO:pyaf.std:MODEL_L1 L1_Fit=294.8046413812163 L1_Forecast=294.8046413812163 L1_Test=294.8046413812163 INFO:pyaf.std:MODEL_L2 L2_Fit=410.9916856450198 L2_Forecast=410.9916856450198 L2_Test=410.9916856450198 INFO:pyaf.std:MODEL_COMPLEXITY 15 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -414,16 +361,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:SEASONAL_MODEL_VALUES _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear -71.75 {1: 642.75, 4: -227.25, 7: -614.25, 10: -34.25} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Date_day_name=Saturday_Lag2 783.2231767054784 -INFO:pyaf.std:AR_MODEL_COEFF 2 Date_day_name=Thursday_Lag8 380.1248874536548 -INFO:pyaf.std:AR_MODEL_COEFF 3 Date_day_name=Thursday_Lag7 65.76868821074937 -INFO:pyaf.std:AR_MODEL_COEFF 4 Date_day_name=Thursday_Lag5 50.10856399397443 -INFO:pyaf.std:AR_MODEL_COEFF 5 Date_day_name=Thursday_Lag6 -45.078388593319644 -INFO:pyaf.std:AR_MODEL_COEFF 6 Date_day_name=Thursday_Lag11 -6.774523352993128 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 0.23098337281695835 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.18105117787278258 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag8 0.14236145686450175 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.14156916582013734 +INFO:pyaf.std:AR_MODEL_COEFF 1 Date_day_name=Saturday_Lag2 783.2231767054781 +INFO:pyaf.std:AR_MODEL_COEFF 2 Date_day_name=Thursday_Lag8 380.12488745365465 +INFO:pyaf.std:AR_MODEL_COEFF 3 Date_day_name=Thursday_Lag7 65.76868821074945 +INFO:pyaf.std:AR_MODEL_COEFF 4 Date_day_name=Thursday_Lag5 50.10856399397437 +INFO:pyaf.std:AR_MODEL_COEFF 5 Date_day_name=Thursday_Lag6 -45.07838859331977 +INFO:pyaf.std:AR_MODEL_COEFF 6 Date_day_name=Thursday_Lag11 -6.774523352992546 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 0.23098337281696826 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.18105117787276676 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag8 0.14236145686452778 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.1415691658201368 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='VIC' Length=44 Min=5800 Max=14071 Mean=8714.75 StdDev=2679.088193982491 @@ -436,8 +383,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_VIC_ConstantTrend_residue_Seasonal_MonthOfYear_re INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0446 MAPE_Forecast=0.0446 MAPE_Test=0.0446 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0442 SMAPE_Forecast=0.0442 SMAPE_Test=0.0442 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1127 MASE_Forecast=0.1127 MASE_Test=0.1127 -INFO:pyaf.std:MODEL_L1 L1_Fit=372.61864530202655 L1_Forecast=372.61864530202655 L1_Test=372.61864530202655 -INFO:pyaf.std:MODEL_L2 L2_Fit=487.25499517580613 L2_Forecast=487.25499517580613 L2_Test=487.25499517580613 +INFO:pyaf.std:MODEL_L1 L1_Fit=372.61864530202644 L1_Forecast=372.61864530202644 L1_Test=372.61864530202644 +INFO:pyaf.std:MODEL_L2 L2_Fit=487.2549951758062 L2_Forecast=487.2549951758062 L2_Test=487.2549951758062 INFO:pyaf.std:MODEL_COMPLEXITY 15 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -449,16 +396,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:SEASONAL_MODEL_VALUES _VIC_ConstantTrend_residue_Seasonal_MonthOfYear -1097.25 {1: 4414.25, 4: -913.75, 7: -2198.75, 10: -1242.75} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.19612566300709383 -INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.17628829298908283 -INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 -0.15961215468460155 -INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag9 0.14503586663357215 -INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag8 0.13626712878321878 -INFO:pyaf.std:AR_MODEL_COEFF 6 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 -0.12201063457883657 -INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.1101270747710357 -INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 0.09866381773793553 -INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.08388372892425316 -INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 -0.0481136826091074 +INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.19612566300709255 +INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.17628829298908355 +INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 -0.15961215468460205 +INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag9 0.1450358666335717 +INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag8 0.13626712878321906 +INFO:pyaf.std:AR_MODEL_COEFF 6 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 -0.12201063457883661 +INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.11012707477103577 +INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 0.098663817737935 +INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.08388372892425355 +INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 -0.04811368260910698 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_State' Length=44 Min=17435 Max=29320 Mean=22006.522727272728 StdDev=3465.0342874378366 @@ -471,8 +418,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_NSW_State_ConstantTrend_residue_Seasonal_MonthOfY INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0339 MAPE_Forecast=0.0339 MAPE_Test=0.0339 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0337 SMAPE_Forecast=0.0337 SMAPE_Test=0.0337 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1805 MASE_Forecast=0.1805 MASE_Test=0.1805 -INFO:pyaf.std:MODEL_L1 L1_Fit=726.5714603179459 L1_Forecast=726.5714603179459 L1_Test=726.5714603179459 -INFO:pyaf.std:MODEL_L2 L2_Fit=923.4271941557964 L2_Forecast=923.4271941557964 L2_Test=923.4271941557964 +INFO:pyaf.std:MODEL_L1 L1_Fit=726.5714603179458 L1_Forecast=726.5714603179458 L1_Test=726.5714603179458 +INFO:pyaf.std:MODEL_L2 L2_Fit=923.4271941557962 L2_Forecast=923.4271941557962 L2_Test=923.4271941557962 INFO:pyaf.std:MODEL_COMPLEXITY 15 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -484,16 +431,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear -1438.522727272728 {1: 5117.477272727272, 4: -1910.522727272728, 7: -2395.522727272728, 10: -963.5227272727279} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 0.45196349113832557 -INFO:pyaf.std:AR_MODEL_COEFF 2 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.17481258294868324 -INFO:pyaf.std:AR_MODEL_COEFF 3 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 0.14997404116357177 -INFO:pyaf.std:AR_MODEL_COEFF 4 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.12721188711733034 -INFO:pyaf.std:AR_MODEL_COEFF 5 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 0.09429282831039254 +INFO:pyaf.std:AR_MODEL_COEFF 1 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 0.4519634911383256 +INFO:pyaf.std:AR_MODEL_COEFF 2 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.1748125829486834 +INFO:pyaf.std:AR_MODEL_COEFF 3 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 0.149974041163572 +INFO:pyaf.std:AR_MODEL_COEFF 4 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.12721188711733045 +INFO:pyaf.std:AR_MODEL_COEFF 5 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 0.09429282831039251 INFO:pyaf.std:AR_MODEL_COEFF 6 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag9 0.08596045514378588 -INFO:pyaf.std:AR_MODEL_COEFF 7 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 -0.08383949545908846 -INFO:pyaf.std:AR_MODEL_COEFF 8 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.04294012236307723 -INFO:pyaf.std:AR_MODEL_COEFF 9 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag5 0.03787854823084488 -INFO:pyaf.std:AR_MODEL_COEFF 10 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.034742989253749315 +INFO:pyaf.std:AR_MODEL_COEFF 7 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 -0.08383949545908861 +INFO:pyaf.std:AR_MODEL_COEFF 8 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.04294012236307679 +INFO:pyaf.std:AR_MODEL_COEFF 9 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag5 0.037878548230844925 +INFO:pyaf.std:AR_MODEL_COEFF 10 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.034742989253749565 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Other_State' Length=44 Min=15384 Max=22284 Mean=18492.613636363636 StdDev=1596.067428740646 @@ -596,37 +543,12 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.5966699123382568 -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.1122567653656006 -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 0.8269491195678711 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 0.9391734600067139 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 1.4687581062316895 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 0.9233601093292236 -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 1.2601871490478516 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.637892723083496 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 1.2535114288330078 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.8326270580291748 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.6928043365478516 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.4414341449737549 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.2693018913269043 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 4.252389669418335 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.530208587646484 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.39527440071106 diff --git a/tests/references/hierarchical_test_hierarchy_AU_AllMethods_Exogenous_per_node.log b/tests/references/hierarchical_test_hierarchy_AU_AllMethods_Exogenous_per_node.log index 6fb1407ea..ab9124f04 100644 --- a/tests/references/hierarchical_test_hierarchy_AU_AllMethods_Exogenous_per_node.log +++ b/tests/references/hierarchical_test_hierarchy_AU_AllMethods_Exogenous_per_node.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] +INFO:pyaf.std:START_TRAINING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' City State Country 0 Sydney NSW_State Australia 1 NSW NSW_State Australia @@ -9,60 +9,10 @@ INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneG 5 QLD QLD_State Australia 6 Capitals Other_State Australia 7 Other Other_State Australia -INFO:pyaf.std:START_TRAINING 'BrisbaneGC' -INFO:pyaf.std:START_TRAINING 'Melbourne' -INFO:pyaf.std:START_TRAINING 'Other_State' -INFO:pyaf.std:START_TRAINING 'VIC' -INFO:pyaf.std:START_TRAINING 'Capitals' -INFO:pyaf.std:START_TRAINING 'NSW' -INFO:pyaf.std:START_TRAINING 'QLD' -INFO:pyaf.std:START_TRAINING 'VIC_State' -INFO:pyaf.std:START_TRAINING 'Sydney' -INFO:pyaf.std:START_TRAINING 'QLD_State' -INFO:pyaf.std:START_TRAINING 'NSW_State' -INFO:pyaf.std:START_TRAINING 'Other' -INFO:pyaf.std:START_TRAINING 'Australia' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other_State' 36.012815713882446 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Australia' 36.23024129867554 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD' 36.2655816078186 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Melbourne' 36.331854581832886 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BrisbaneGC' 36.44637680053711 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW' 36.47415828704834 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other' 36.52402949333191 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Capitals' 36.77666759490967 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC' 36.95744037628174 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Sydney' 36.95798087120056 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_State' 40.691426038742065 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_State' 44.31799674034119 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_State' 45.078853368759155 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 55.76332211494446 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.7746875286102295 -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.330331563949585 -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 0.9935958385467529 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 0.9877269268035889 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 1.0221827030181885 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 0.9227278232574463 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.2743029594421387 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 1.246781349182129 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.8157734870910645 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 1.133439064025879 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.7199342250823975 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.4522891044616699 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.35753321647644043 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 9.580282926559448 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -82,142 +32,139 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Date', 'BrisbaneGC', 'BrisbaneGC_ 'VIC_State_OC_Forecast', 'Australia_OC_Forecast'], dtype='object', length=118) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.029, 'RMSE': 2478.9021480252372, 'MAE': 2041.1413317998747, 'SMAPE': 0.0288, 'ErrorMean': 92.84090909090959, 'ErrorStdDev': 2477.1629791120545, 'R2': 0.9013312995330796, 'Pearson': 0.9495689304533909} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.029, 'RMSE': 2478.9021480252372, 'MAE': 2041.1413317998747, 'SMAPE': 0.0288, 'ErrorMean': 92.84090909090959, 'ErrorStdDev': 2477.1629791120545, 'R2': 0.9013312995330796, 'Pearson': 0.9495689304533909} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2669.152684412323, 'MAE': 2126.2066372463746, 'SMAPE': 0.0301, 'ErrorMean': 229.79545454545521, 'ErrorStdDev': 2659.242392444841, 'R2': 0.8856048858881285, 'Pearson': 0.9420978222872554} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2669.152684412323, 'MAE': 2126.2066372463746, 'SMAPE': 0.0301, 'ErrorMean': 229.79545454545521, 'ErrorStdDev': 2659.242392444841, 'R2': 0.8856048858881285, 'Pearson': 0.9420978222872554} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.0308, 'RMSE': 2829.1222108745715, 'MAE': 2134.1917129572666, 'SMAPE': 0.0303, 'ErrorMean': 595.0991735537114, 'ErrorStdDev': 2765.8252760612904, 'R2': 0.8714819707600234, 'Pearson': 0.9367598660056953} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.0308, 'RMSE': 2829.1222108745715, 'MAE': 2134.1917129572666, 'SMAPE': 0.0303, 'ErrorMean': 595.0991735537114, 'ErrorStdDev': 2765.8252760612904, 'R2': 0.8714819707600234, 'Pearson': 0.9367598660056953} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0809, 'RMSE': 724.7303370979117, 'MAE': 616.4243205312524, 'SMAPE': 0.0784, 'ErrorMean': 169.3104648748422, 'ErrorStdDev': 704.6758318503039, 'R2': 0.42192341928602084, 'Pearson': 0.6908359461872079} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0809, 'RMSE': 724.7303370979117, 'MAE': 616.4243205312524, 'SMAPE': 0.0784, 'ErrorMean': 169.3104648748422, 'ErrorStdDev': 704.6758318503039, 'R2': 0.42192341928602084, 'Pearson': 0.6908359461872079} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615456} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615456} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980038} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980038} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.029, 'RMSE': 2478.902148025238, 'MAE': 2041.1413317998754, 'SMAPE': 0.0288, 'ErrorMean': 92.84090909090942, 'ErrorStdDev': 2477.1629791120554, 'R2': 0.9013312995330796, 'Pearson': 0.949568930453391} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.029, 'RMSE': 2478.902148025238, 'MAE': 2041.1413317998754, 'SMAPE': 0.0288, 'ErrorMean': 92.84090909090942, 'ErrorStdDev': 2477.1629791120554, 'R2': 0.9013312995330796, 'Pearson': 0.949568930453391} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2669.152684412323, 'MAE': 2126.2066372463746, 'SMAPE': 0.0301, 'ErrorMean': 229.79545454545521, 'ErrorStdDev': 2659.242392444841, 'R2': 0.8856048858881285, 'Pearson': 0.9420978222872555} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2669.152684412323, 'MAE': 2126.2066372463746, 'SMAPE': 0.0301, 'ErrorMean': 229.79545454545521, 'ErrorStdDev': 2659.242392444841, 'R2': 0.8856048858881285, 'Pearson': 0.9420978222872555} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.0308, 'RMSE': 2829.122210874579, 'MAE': 2134.19171295727, 'SMAPE': 0.0303, 'ErrorMean': 595.0991735537398, 'ErrorStdDev': 2765.825276061292, 'R2': 0.8714819707600228, 'Pearson': 0.9367598660056953} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.0308, 'RMSE': 2829.122210874579, 'MAE': 2134.19171295727, 'SMAPE': 0.0303, 'ErrorMean': 595.0991735537398, 'ErrorStdDev': 2765.825276061292, 'R2': 0.8714819707600228, 'Pearson': 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_Capitals_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.03545110762656872 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Melbourne' Length=44 Min=3825 Max=5724 Mean=4728.227272727273 StdDev=561.4593348682911 @@ -389,7 +336,7 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Sydney_ConstantTrend_residue_Seasonal_MonthOfYear INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0651 MAPE_Forecast=0.0651 MAPE_Test=0.0651 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0653 SMAPE_Forecast=0.0653 SMAPE_Test=0.0653 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5943 MASE_Forecast=0.5943 MASE_Test=0.5943 -INFO:pyaf.std:MODEL_L1 L1_Fit=384.26286970640575 L1_Forecast=384.26286970640575 L1_Test=384.26286970640575 +INFO:pyaf.std:MODEL_L1 L1_Fit=384.2628697064058 L1_Forecast=384.2628697064058 L1_Test=384.2628697064058 INFO:pyaf.std:MODEL_L2 L2_Fit=519.7037131477329 L2_Forecast=519.7037131477329 L2_Test=519.7037131477329 INFO:pyaf.std:MODEL_COMPLEXITY 15 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START @@ -402,16 +349,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:SEASONAL_MODEL_VALUES _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear -71.75 {1: 642.75, 4: -227.25, 7: -614.25, 10: -34.25} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.49452731711970477 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.24048157063507436 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag5 -0.239025462414975 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag8 0.16617307435745987 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Sydney_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 -0.133609189181439 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SignalVariable='VIC' Length=44 Min=5800 Max=14071 Mean=8714.75 StdDev=2679.088193982491 @@ -424,8 +371,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_VIC_ConstantTrend_residue_Seasonal_MonthOfYear_re INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0446 MAPE_Forecast=0.0446 MAPE_Test=0.0446 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0442 SMAPE_Forecast=0.0442 SMAPE_Test=0.0442 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1127 MASE_Forecast=0.1127 MASE_Test=0.1127 -INFO:pyaf.std:MODEL_L1 L1_Fit=372.61864530202655 L1_Forecast=372.61864530202655 L1_Test=372.61864530202655 -INFO:pyaf.std:MODEL_L2 L2_Fit=487.25499517580613 L2_Forecast=487.25499517580613 L2_Test=487.25499517580613 +INFO:pyaf.std:MODEL_L1 L1_Fit=372.61864530202644 L1_Forecast=372.61864530202644 L1_Test=372.61864530202644 +INFO:pyaf.std:MODEL_L2 L2_Fit=487.2549951758062 L2_Forecast=487.2549951758062 L2_Test=487.2549951758062 INFO:pyaf.std:MODEL_COMPLEXITY 15 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -437,16 +384,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:SEASONAL_MODEL_VALUES _VIC_ConstantTrend_residue_Seasonal_MonthOfYear -1097.25 {1: 4414.25, 4: -913.75, 7: -2198.75, 10: -1242.75} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.19612566300709383 -INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.17628829298908283 -INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 -0.15961215468460155 -INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag9 0.14503586663357215 -INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag8 0.13626712878321878 -INFO:pyaf.std:AR_MODEL_COEFF 6 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 -0.12201063457883657 -INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.1101270747710357 -INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 0.09866381773793553 -INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.08388372892425316 -INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 -0.0481136826091074 +INFO:pyaf.std:AR_MODEL_COEFF 1 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.19612566300709255 +INFO:pyaf.std:AR_MODEL_COEFF 2 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.17628829298908355 +INFO:pyaf.std:AR_MODEL_COEFF 3 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 -0.15961215468460205 +INFO:pyaf.std:AR_MODEL_COEFF 4 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag9 0.1450358666335717 +INFO:pyaf.std:AR_MODEL_COEFF 5 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag8 0.13626712878321906 +INFO:pyaf.std:AR_MODEL_COEFF 6 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 -0.12201063457883661 +INFO:pyaf.std:AR_MODEL_COEFF 7 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.11012707477103577 +INFO:pyaf.std:AR_MODEL_COEFF 8 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 0.098663817737935 +INFO:pyaf.std:AR_MODEL_COEFF 9 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.08388372892425355 +INFO:pyaf.std:AR_MODEL_COEFF 10 _VIC_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 -0.04811368260910698 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='NSW_State' Length=44 Min=17435 Max=29320 Mean=22006.522727272728 StdDev=3465.0342874378366 @@ -459,8 +406,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_NSW_State_ConstantTrend_residue_Seasonal_MonthOfY INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0339 MAPE_Forecast=0.0339 MAPE_Test=0.0339 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0337 SMAPE_Forecast=0.0337 SMAPE_Test=0.0337 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1805 MASE_Forecast=0.1805 MASE_Test=0.1805 -INFO:pyaf.std:MODEL_L1 L1_Fit=726.5714603179459 L1_Forecast=726.5714603179459 L1_Test=726.5714603179459 -INFO:pyaf.std:MODEL_L2 L2_Fit=923.4271941557964 L2_Forecast=923.4271941557964 L2_Test=923.4271941557964 +INFO:pyaf.std:MODEL_L1 L1_Fit=726.5714603179458 L1_Forecast=726.5714603179458 L1_Test=726.5714603179458 +INFO:pyaf.std:MODEL_L2 L2_Fit=923.4271941557962 L2_Forecast=923.4271941557962 L2_Test=923.4271941557962 INFO:pyaf.std:MODEL_COMPLEXITY 15 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -472,16 +419,16 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear -1438.522727272728 {1: 5117.477272727272, 4: -1910.522727272728, 7: -2395.522727272728, 10: -963.5227272727279} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 0.45196349113832557 -INFO:pyaf.std:AR_MODEL_COEFF 2 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.17481258294868324 -INFO:pyaf.std:AR_MODEL_COEFF 3 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 0.14997404116357177 -INFO:pyaf.std:AR_MODEL_COEFF 4 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.12721188711733034 -INFO:pyaf.std:AR_MODEL_COEFF 5 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 0.09429282831039254 +INFO:pyaf.std:AR_MODEL_COEFF 1 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 0.4519634911383256 +INFO:pyaf.std:AR_MODEL_COEFF 2 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.1748125829486834 +INFO:pyaf.std:AR_MODEL_COEFF 3 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 0.149974041163572 +INFO:pyaf.std:AR_MODEL_COEFF 4 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.12721188711733045 +INFO:pyaf.std:AR_MODEL_COEFF 5 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag6 0.09429282831039251 INFO:pyaf.std:AR_MODEL_COEFF 6 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag9 0.08596045514378588 -INFO:pyaf.std:AR_MODEL_COEFF 7 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 -0.08383949545908846 -INFO:pyaf.std:AR_MODEL_COEFF 8 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.04294012236307723 -INFO:pyaf.std:AR_MODEL_COEFF 9 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag5 0.03787854823084488 -INFO:pyaf.std:AR_MODEL_COEFF 10 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.034742989253749315 +INFO:pyaf.std:AR_MODEL_COEFF 7 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 -0.08383949545908861 +INFO:pyaf.std:AR_MODEL_COEFF 8 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.04294012236307679 +INFO:pyaf.std:AR_MODEL_COEFF 9 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag5 0.037878548230844925 +INFO:pyaf.std:AR_MODEL_COEFF 10 _NSW_State_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.034742989253749565 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Other_State' Length=44 Min=15384 Max=22284 Mean=18492.613636363636 StdDev=1596.067428740646 @@ -584,37 +531,12 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.7344732284545898 -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 0.8754699230194092 -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.3223450183868408 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 0.9367532730102539 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 0.9445805549621582 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 0.8628373146057129 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.3310818672180176 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 1.1790821552276611 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 1.141010046005249 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.762566089630127 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.5100197792053223 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.36837077140808105 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.18804359436035156 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 8.46851658821106 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.717730283737183 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 8.62671184539795 diff --git a/tests/references/hierarchical_test_hierarchy_AU_BU.log b/tests/references/hierarchical_test_hierarchy_AU_BU.log index 303d0ea7f..56c9a760e 100644 --- a/tests/references/hierarchical_test_hierarchy_AU_BU.log +++ b/tests/references/hierarchical_test_hierarchy_AU_BU.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] +INFO:pyaf.std:START_TRAINING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' City State Country 0 Sydney NSW_State Australia 1 NSW NSW_State Australia @@ -9,60 +9,10 @@ INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneG 5 QLD QLD_State Australia 6 Capitals Other_State Australia 7 Other Other_State Australia -INFO:pyaf.std:START_TRAINING 'Capitals' -INFO:pyaf.std:START_TRAINING 'BrisbaneGC' -INFO:pyaf.std:START_TRAINING 'Melbourne' -INFO:pyaf.std:START_TRAINING 'NSW' -INFO:pyaf.std:START_TRAINING 'Other' -INFO:pyaf.std:START_TRAINING 'QLD' -INFO:pyaf.std:START_TRAINING 'Sydney' -INFO:pyaf.std:START_TRAINING 'VIC' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Capitals' 12.131661891937256 -INFO:pyaf.std:START_TRAINING 'NSW_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other' 12.171405792236328 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW' 12.197098970413208 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Melbourne' 12.202324390411377 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD' 12.199206113815308 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BrisbaneGC' 12.230625867843628 -INFO:pyaf.std:START_TRAINING 'Other_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Sydney' 12.251120805740356 -INFO:pyaf.std:START_TRAINING 'QLD_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC' 12.271479845046997 -INFO:pyaf.std:START_TRAINING 'VIC_State' -INFO:pyaf.std:START_TRAINING 'Australia' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_State' 6.912095308303833 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_State' 7.044766187667847 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other_State' 7.078492641448975 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Australia' 6.972016334533691 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_State' 7.2617833614349365 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 41.247684478759766 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.994532585144043 -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.1180493831634521 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 1.1574630737304688 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 1.1564977169036865 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 1.0786964893341064 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 1.0552797317504883 -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.0092785358428955 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.8668355941772461 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.9466028213500977 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.6373610496520996 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.5301499366760254 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.2645580768585205 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.3804473876953125 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 6.108653783798218 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD @@ -94,38 +44,35 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Date', 'BrisbaneGC', 'BrisbaneGC_ 'Australia_BU_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0307, 'RMSE': 2649.2533389732453, 'MAE': 2160.6711851064942, 'SMAPE': 0.0305, 'ErrorMean': 39.733151644220264, 'ErrorStdDev': 2648.955365936034, 'R2': 0.8873042283973063, 'Pearson': 0.9420309611478158} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 'MAPE': 0.0307, 'RMSE': 2649.2533389732453, 'MAE': 2160.6711851064942, 'SMAPE': 0.0305, 'ErrorMean': 39.733151644220264, 'ErrorStdDev': 2648.955365936034, 'R2': 0.8873042283973063, 'Pearson': 0.9420309611478158} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615456} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_BU_Forecast', 'Length': 44, 'MAPE': 0.059, 'RMSE': 614.1944650589835, 'MAE': 440.84090909090907, 'SMAPE': 0.0568, 'ErrorMean': 129.52272727272728, 'ErrorStdDev': 600.3821316702602, 'R2': 0.5848124808738524, 'Pearson': 0.7767112806615456} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Capitals_BU_Forecast', 'Length': 44, 'MAPE': 0.0577, 'RMSE': 608.1757558469427, 'MAE': 460.8863636363636, 'SMAPE': 0.0582, 'ErrorMean': -66.79545454545455, 'ErrorStdDev': 604.4965816711176, 'R2': 0.5328349460551864, 'Pearson': 0.7348640156077884} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Capitals_BU_Forecast', 'Length': 44, 'MAPE': 0.0577, 'RMSE': 608.1757558469427, 'MAE': 460.8863636363636, 'SMAPE': 0.0582, 'ErrorMean': -66.79545454545455, 'ErrorStdDev': 604.4965816711176, 'R2': 0.5328349460551864, 'Pearson': 0.7348640156077884} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_BU_Forecast', 'Length': 44, 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INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_BU_Forecast', 'Length': 44, 'MAPE': 0.0654, 'RMSE': 907.8588822058194, 'MAE': 701.2954545454545, 'SMAPE': 0.065, 'ErrorMean': -37.38636363636363, 'ErrorStdDev': 907.0887552020748, 'R2': 0.7511816244233342, 'Pearson': 0.8674625497115258} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_BU_Forecast', 'Length': 44, 'MAPE': 0.0654, 'RMSE': 907.8588822058194, 'MAE': 701.2954545454545, 'SMAPE': 0.065, 'ErrorMean': -37.38636363636363, 'ErrorStdDev': 907.0887552020748, 'R2': 0.7511816244233342, 'Pearson': 0.8674625497115258} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0512, 'RMSE': 1153.4137080068808, 'MAE': 934.5909090909091, 'SMAPE': 0.0504, 'ErrorMean': 92.13636363636364, 'ErrorStdDev': 1149.727825319562, 'R2': 0.7396794284983607, 'Pearson': 0.8613958375031755} 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INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Sydney_BU_Forecast', 'Length': 44, 'MAPE': 0.0607, 'RMSE': 558.1781992520188, 'MAE': 370.6134016376204, 'SMAPE': 0.0622, 'ErrorMean': -126.31878588840294, 'ErrorStdDev': 543.6970355371695, 'R2': 0.4725848925563002, 'Pearson': 0.7104826236354815} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Sydney_BU_Forecast', 'Length': 44, 'MAPE': 0.0607, 'RMSE': 558.1781992520188, 'MAE': 370.6134016376204, 'SMAPE': 0.0622, 'ErrorMean': -126.31878588840294, 'ErrorStdDev': 543.6970355371695, 'R2': 0.4725848925563002, 'Pearson': 0.7104826236354815} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_BU_Forecast', 'Length': 44, 'MAPE': 0.0516, 'RMSE': 530.0781460398937, 'MAE': 424.20454545454544, 'SMAPE': 0.0511, 'ErrorMean': 14.75, 'ErrorStdDev': 529.8728889168523, 'R2': 0.9608523426800629, 'Pearson': 0.9803775115824175} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_BU_Forecast', 'Length': 44, 'MAPE': 0.0516, 'RMSE': 530.0781460398937, 'MAE': 424.20454545454544, 'SMAPE': 0.0511, 'ErrorMean': 14.75, 'ErrorStdDev': 529.8728889168523, 'R2': 0.9608523426800629, 'Pearson': 0.9803775115824175} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0417, 'RMSE': 680.2621653718246, 'MAE': 527.4772727272727, 'SMAPE': 0.0417, 'ErrorMean': -44.47727272727273, 'ErrorStdDev': 678.8065894252261, 'R2': 0.9499726039950437, 'Pearson': 0.9748137776870249} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0417, 'RMSE': 680.2621653718246, 'MAE': 527.4772727272727, 'SMAPE': 0.0417, 'ErrorMean': -44.47727272727273, 'ErrorStdDev': 678.8065894252261, 'R2': 0.9499726039950437, 'Pearson': 0.9748137776870249} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 23.909221410751343 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_BU_Forecast', 'Length': 44, 'MAPE': 0.0516, 'RMSE': 530.0781460398937, 'MAE': 424.20454545454544, 'SMAPE': 0.0511, 'ErrorMean': 14.75, 'ErrorStdDev': 529.8728889168523, 'R2': 0.9608523426800629, 'Pearson': 0.9803775115824177} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_BU_Forecast', 'Length': 44, 'MAPE': 0.0516, 'RMSE': 530.0781460398937, 'MAE': 424.20454545454544, 'SMAPE': 0.0511, 'ErrorMean': 14.75, 'ErrorStdDev': 529.8728889168523, 'R2': 0.9608523426800629, 'Pearson': 0.9803775115824177} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_State_BU_Forecast', 'Length': 44, 'MAPE': 0.0417, 'RMSE': 680.2621653718246, 'MAE': 527.4772727272727, 'SMAPE': 0.0417, 'ErrorMean': -44.47727272727273, 'ErrorStdDev': 678.8065894252261, 'R2': 0.9499726039950437, 'Pearson': 0.9748137776870248} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 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SMAPE_Test=0.0338 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1808 MASE_Forecast=0.1808 MASE_Test=0.1808 -INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843487 L1_Forecast=727.7548391843487 L1_Test=727.7548391843487 -INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912562 L2_Forecast=979.3716750912562 L2_Test=979.3716750912562 +INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843488 L1_Forecast=727.7548391843488 L1_Test=727.7548391843488 +INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912563 L2_Forecast=979.3716750912563 L2_Test=979.3716750912563 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -347,7 +294,7 @@ INFO:pyaf.std:TREND_DETAIL_START INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (23488.339004277248, array([-2964.69662101])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717456 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717438 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END @@ -452,33 +399,8 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 0.8155381679534912 -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.9142649173736572 -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 0.8809165954589844 -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 0.7345521450042725 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 0.7856028079986572 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 0.7681024074554443 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 0.8339667320251465 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.7410612106323242 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.8109951019287109 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.7459578514099121 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.39508581161499023 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.5769424438476562 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.21752572059631348 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 6.066989421844482 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.238082408905029 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 6.153406381607056 diff --git a/tests/references/hierarchical_test_hierarchy_AU_MO.log b/tests/references/hierarchical_test_hierarchy_AU_MO.log index 31e3f144c..7c936e837 100644 --- a/tests/references/hierarchical_test_hierarchy_AU_MO.log +++ b/tests/references/hierarchical_test_hierarchy_AU_MO.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] +INFO:pyaf.std:START_TRAINING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' City State Country 0 Sydney NSW_State Australia 1 NSW NSW_State Australia @@ -9,60 +9,10 @@ INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneG 5 QLD QLD_State Australia 6 Capitals Other_State Australia 7 Other Other_State Australia -INFO:pyaf.std:START_TRAINING 'Capitals' -INFO:pyaf.std:START_TRAINING 'Other' -INFO:pyaf.std:START_TRAINING 'BrisbaneGC' -INFO:pyaf.std:START_TRAINING 'NSW' -INFO:pyaf.std:START_TRAINING 'Melbourne' -INFO:pyaf.std:START_TRAINING 'Sydney' -INFO:pyaf.std:START_TRAINING 'QLD' -INFO:pyaf.std:START_TRAINING 'VIC' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Capitals' 11.964649438858032 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC' 11.933469295501709 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD' 11.967644453048706 -INFO:pyaf.std:START_TRAINING 'NSW_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Melbourne' 12.02350926399231 -INFO:pyaf.std:START_TRAINING 'Other_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other' 12.061925172805786 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW' 12.108350276947021 -INFO:pyaf.std:START_TRAINING 'QLD_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BrisbaneGC' 12.143953561782837 -INFO:pyaf.std:START_TRAINING 'VIC_State' -INFO:pyaf.std:START_TRAINING 'Australia' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Sydney' 12.224885940551758 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_State' 6.867918252944946 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other_State' 6.966046094894409 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_State' 7.11854100227356 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_State' 7.101913213729858 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Australia' 7.052791357040405 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 41.205138206481934 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.9595770835876465 -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.1657474040985107 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 1.0913920402526855 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 1.0279765129089355 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 0.9758720397949219 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 1.0576999187469482 -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 0.9054062366485596 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.8995215892791748 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.865117073059082 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.47545409202575684 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.7328171730041504 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.35899853706359863 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.22143149375915527 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 6.408642292022705 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['MO'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD @@ -94,38 +44,35 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Date', 'BrisbaneGC', 'BrisbaneGC_ 'Australia_MO_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2681.5655374823823, 'MAE': 2131.612691344388, 'SMAPE': 0.0301, 'ErrorMean': 228.73315164422027, 'ErrorStdDev': 2671.792446495813, 'R2': 0.8845384264642633, 'Pearson': 0.9414055707483306} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2681.5655374823823, 'MAE': 2131.612691344388, 'SMAPE': 0.0301, 'ErrorMean': 228.73315164422027, 'ErrorStdDev': 2671.792446495813, 'R2': 0.8845384264642633, 'Pearson': 0.9414055707483306} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2681.5655374823828, 'MAE': 2131.6126913443895, 'SMAPE': 0.0301, 'ErrorMean': 228.73315164421928, 'ErrorStdDev': 2671.7924464958137, 'R2': 0.8845384264642633, 'Pearson': 0.9414055707483305} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_MO_Forecast', 'Length': 44, 'MAPE': 0.0304, 'RMSE': 2681.5655374823828, 'MAE': 2131.6126913443895, 'SMAPE': 0.0301, 'ErrorMean': 228.73315164421928, 'ErrorStdDev': 2671.7924464958137, 'R2': 0.8845384264642633, 'Pearson': 0.9414055707483305} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_MO_Forecast', 'Length': 44, 'MAPE': 0.0949, 'RMSE': 891.436491265439, 'MAE': 753.2690381856381, 'SMAPE': 0.0931, 'ErrorMean': 32.514564742801234, 'ErrorStdDev': 890.8433201406538, 'R2': 0.12539227441774858, 'Pearson': 0.5041140331831235} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_MO_Forecast', 'Length': 44, 'MAPE': 0.0949, 'RMSE': 891.436491265439, 'MAE': 753.2690381856381, 'SMAPE': 0.0931, 'ErrorMean': 32.514564742801234, 'ErrorStdDev': 890.8433201406538, 'R2': 0.12539227441774858, 'Pearson': 0.5041140331831235} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Capitals_MO_Forecast', 'Length': 44, 'MAPE': 0.0758, 'RMSE': 780.4120013840982, 'MAE': 602.2219691802347, 'SMAPE': 0.0756, 'ErrorMean': 25.907527296191077, 'ErrorStdDev': 779.9818535925891, 'R2': 0.2307632577217924, 'Pearson': 0.4813486401057031} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Capitals_MO_Forecast', 'Length': 44, 'MAPE': 0.0758, 'RMSE': 780.4120013840982, 'MAE': 602.2219691802347, 'SMAPE': 0.0756, 'ErrorMean': 25.907527296191077, 'ErrorStdDev': 779.9818535925891, 'R2': 0.2307632577217924, 'Pearson': 0.4813486401057031} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Melbourne_MO_Forecast', 'Length': 44, 'MAPE': 0.1374, 'RMSE': 823.8988772084131, 'MAE': 677.0659433686917, 'SMAPE': 0.1355, 'ErrorMean': 32.54252934917533, 'ErrorStdDev': 823.255940548771, 'R2': -1.1533330609077264, 'Pearson': 0.6187624097211959} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Melbourne_MO_Forecast', 'Length': 44, 'MAPE': 0.1374, 'RMSE': 823.8988772084131, 'MAE': 677.0659433686917, 'SMAPE': 0.1355, 'ErrorMean': 32.54252934917533, 'ErrorStdDev': 823.255940548771, 'R2': -1.1533330609077264, 'Pearson': 0.6187624097211959} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_MO_Forecast', 'Length': 44, 'MAPE': 0.051, 'RMSE': 1006.8184226196723, 'MAE': 815.6706475381162, 'SMAPE': 0.0507, 'ErrorMean': -0.7785469391231924, 'ErrorStdDev': 1006.8181216044082, 'R2': 0.8884005858497668, 'Pearson': 0.9549017189460917} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_MO_Forecast', 'Length': 44, 'MAPE': 0.051, 'RMSE': 1006.8184226196723, 'MAE': 815.6706475381162, 'SMAPE': 0.0507, 'ErrorMean': -0.7785469391231924, 'ErrorStdDev': 1006.8181216044082, 'R2': 0.8884005858497668, 'Pearson': 0.9549017189460917} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_State_MO_Forecast', 'Length': 44, 'MAPE': 0.034, 'RMSE': 979.3716750912562, 'MAE': 727.7548391843487, 'SMAPE': 0.0338, 'ErrorMean': -1.0623029012326284, 'ErrorStdDev': 979.3710989627981, 'R2': 0.9201122837859887, 'Pearson': 0.9592555371332373} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_State_MO_Forecast', 'Length': 44, 'MAPE': 0.034, 'RMSE': 979.3716750912562, 'MAE': 727.7548391843487, 'SMAPE': 0.0338, 'ErrorMean': -1.0623029012326284, 'ErrorStdDev': 979.3710989627981, 'R2': 0.9201122837859887, 'Pearson': 0.9592555371332373} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_MO_Forecast', 'Length': 44, 'MAPE': 0.051, 'RMSE': 1006.8184226196724, 'MAE': 815.6706475381162, 'SMAPE': 0.0507, 'ErrorMean': -0.7785469391233991, 'ErrorStdDev': 1006.8181216044084, 'R2': 0.8884005858497668, 'Pearson': 0.954901718946092} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_MO_Forecast', 'Length': 44, 'MAPE': 0.051, 'RMSE': 1006.8184226196724, 'MAE': 815.6706475381162, 'SMAPE': 0.0507, 'ErrorMean': -0.7785469391233991, 'ErrorStdDev': 1006.8181216044084, 'R2': 0.8884005858497668, 'Pearson': 0.954901718946092} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_State_MO_Forecast', 'Length': 44, 'MAPE': 0.034, 'RMSE': 979.3716750912563, 'MAE': 727.7548391843488, 'SMAPE': 0.0338, 'ErrorMean': -1.0623029012328764, 'ErrorStdDev': 979.3710989627984, 'R2': 0.9201122837859887, 'Pearson': 0.9592555371332374} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_State_MO_Forecast', 'Length': 44, 'MAPE': 0.034, 'RMSE': 979.3716750912563, 'MAE': 727.7548391843488, 'SMAPE': 0.0338, 'ErrorMean': -1.0623029012328764, 'ErrorStdDev': 979.3710989627984, 'R2': 0.9201122837859887, 'Pearson': 0.9592555371332374} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Other_MO_Forecast', 'Length': 44, 'MAPE': 0.072, 'RMSE': 981.5535994405298, 'MAE': 757.060080778287, 'SMAPE': 0.0708, 'ErrorMean': 35.2288363401746, 'ErrorStdDev': 980.9211984990319, 'R2': 0.25657475440456146, 'Pearson': 0.5075089347431032} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Other_MO_Forecast', 'Length': 44, 'MAPE': 0.072, 'RMSE': 981.5535994405298, 'MAE': 757.060080778287, 'SMAPE': 0.0708, 'ErrorMean': 35.2288363401746, 'ErrorStdDev': 980.9211984990319, 'R2': 0.25657475440456146, 'Pearson': 0.5075089347431032} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Other_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0503, 'RMSE': 1240.6616819628584, 'MAE': 907.9545454545455, 'SMAPE': 0.0494, 'ErrorMean': 61.13636363636363, 'ErrorStdDev': 1239.1544512820956, 'R2': 0.3957672392950735, 'Pearson': 0.6303332298305678} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Other_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0503, 'RMSE': 1240.6616819628584, 'MAE': 907.9545454545455, 'SMAPE': 0.0494, 'ErrorMean': 61.13636363636363, 'ErrorStdDev': 1239.1544512820956, 'R2': 0.3957672392950735, 'Pearson': 0.6303332298305678} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_MO_Forecast', 'Length': 44, 'MAPE': 0.083, 'RMSE': 1139.6475572238442, 'MAE': 879.959497439337, 'SMAPE': 0.0812, 'ErrorMean': 43.621798893562634, 'ErrorStdDev': 1138.8124048093105, 'R2': 0.6079089902495953, 'Pearson': 0.8053204951882433} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_MO_Forecast', 'Length': 44, 'MAPE': 0.083, 'RMSE': 1139.6475572238442, 'MAE': 879.959497439337, 'SMAPE': 0.0812, 'ErrorMean': 43.621798893562634, 'ErrorStdDev': 1138.8124048093105, 'R2': 0.6079089902495953, 'Pearson': 0.8053204951882433} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0506, 'RMSE': 1152.847206622882, 'MAE': 925.2272727272727, 'SMAPE': 0.0499, 'ErrorMean': 76.13636363636364, 'ErrorStdDev': 1150.3303594839238, 'R2': 0.7399350796102638, 'Pearson': 0.8609182686947952} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0506, 'RMSE': 1152.847206622882, 'MAE': 925.2272727272727, 'SMAPE': 0.0499, 'ErrorMean': 76.13636363636364, 'ErrorStdDev': 1150.3303594839238, 'R2': 0.7399350796102638, 'Pearson': 0.8609182686947952} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Sydney_MO_Forecast', 'Length': 44, 'MAPE': 0.0898, 'RMSE': 762.1984542585886, 'MAE': 554.0595449531894, 'SMAPE': 0.0897, 'ErrorMean': -0.28375596210945664, 'ErrorStdDev': 762.198401439373, 'R2': 0.01657113211821315, 'Pearson': 0.5801928182761823} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Sydney_MO_Forecast', 'Length': 44, 'MAPE': 0.0898, 'RMSE': 762.1984542585886, 'MAE': 554.0595449531894, 'SMAPE': 0.0897, 'ErrorMean': -0.28375596210945664, 'ErrorStdDev': 762.198401439373, 'R2': 0.01657113211821315, 'Pearson': 0.5801928182761823} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Other_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0503, 'RMSE': 1240.6616819628584, 'MAE': 907.9545454545455, 'SMAPE': 0.0494, 'ErrorMean': 61.13636363636363, 'ErrorStdDev': 1239.1544512820956, 'R2': 0.3957672392950735, 'Pearson': 0.6303332298305677} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Other_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0503, 'RMSE': 1240.6616819628584, 'MAE': 907.9545454545455, 'SMAPE': 0.0494, 'ErrorMean': 61.13636363636363, 'ErrorStdDev': 1239.1544512820956, 'R2': 0.3957672392950735, 'Pearson': 0.6303332298305677} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_MO_Forecast', 'Length': 44, 'MAPE': 0.083, 'RMSE': 1139.6475572238442, 'MAE': 879.959497439337, 'SMAPE': 0.0812, 'ErrorMean': 43.621798893562634, 'ErrorStdDev': 1138.8124048093105, 'R2': 0.6079089902495953, 'Pearson': 0.8053204951882432} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_MO_Forecast', 'Length': 44, 'MAPE': 0.083, 'RMSE': 1139.6475572238442, 'MAE': 879.959497439337, 'SMAPE': 0.0812, 'ErrorMean': 43.621798893562634, 'ErrorStdDev': 1138.8124048093105, 'R2': 0.6079089902495953, 'Pearson': 0.8053204951882432} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0506, 'RMSE': 1152.847206622882, 'MAE': 925.2272727272727, 'SMAPE': 0.0499, 'ErrorMean': 76.13636363636364, 'ErrorStdDev': 1150.3303594839238, 'R2': 0.7399350796102638, 'Pearson': 0.8609182686947954} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0506, 'RMSE': 1152.847206622882, 'MAE': 925.2272727272727, 'SMAPE': 0.0499, 'ErrorMean': 76.13636363636364, 'ErrorStdDev': 1150.3303594839238, 'R2': 0.7399350796102638, 'Pearson': 0.8609182686947954} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Sydney_MO_Forecast', 'Length': 44, 'MAPE': 0.0898, 'RMSE': 762.1984542585885, 'MAE': 554.0595449531894, 'SMAPE': 0.0897, 'ErrorMean': -0.28375596210951864, 'ErrorStdDev': 762.1984014393731, 'R2': 0.01657113211821326, 'Pearson': 0.5801928182761824} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Sydney_MO_Forecast', 'Length': 44, 'MAPE': 0.0898, 'RMSE': 762.1984542585885, 'MAE': 554.0595449531894, 'SMAPE': 0.0897, 'ErrorMean': -0.28375596210951864, 'ErrorStdDev': 762.1984014393731, 'R2': 0.01657113211821326, 'Pearson': 0.5801928182761824} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_MO_Forecast', 'Length': 44, 'MAPE': 0.0885, 'RMSE': 904.4324304538285, 'MAE': 749.0641921317176, 'SMAPE': 0.0861, 'ErrorMean': 59.980197923553305, 'ErrorStdDev': 902.4413538361763, 'R2': 0.8860332320616641, 'Pearson': 0.9766390290861455} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_MO_Forecast', 'Length': 44, 'MAPE': 0.0885, 'RMSE': 904.4324304538285, 'MAE': 749.0641921317176, 'SMAPE': 0.0861, 'ErrorMean': 59.980197923553305, 'ErrorStdDev': 902.4413538361763, 'R2': 0.8860332320616641, 'Pearson': 0.9766390290861455} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0409, 'RMSE': 688.3676178269336, 'MAE': 511.6136363636364, 'SMAPE': 0.0404, 'ErrorMean': 92.52272727272727, 'ErrorStdDev': 682.1213398001443, 'R2': 0.9487733297344288, 'Pearson': 0.9745249001884253} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0409, 'RMSE': 688.3676178269336, 'MAE': 511.6136363636364, 'SMAPE': 0.0404, 'ErrorMean': 92.52272727272727, 'ErrorStdDev': 682.1213398001443, 'R2': 0.9487733297344288, 'Pearson': 0.9745249001884253} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 23.72507882118225 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0409, 'RMSE': 688.3676178269336, 'MAE': 511.6136363636364, 'SMAPE': 0.0404, 'ErrorMean': 92.52272727272727, 'ErrorStdDev': 682.1213398001443, 'R2': 0.9487733297344288, 'Pearson': 0.9745249001884256} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_State_MO_Forecast', 'Length': 44, 'MAPE': 0.0409, 'RMSE': 688.3676178269336, 'MAE': 511.6136363636364, 'SMAPE': 0.0404, 'ErrorMean': 92.52272727272727, 'ErrorStdDev': 682.1213398001443, 'R2': 0.9487733297344288, 'Pearson': 0.9745249001884256} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 48.4985625743866 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='BrisbaneGC' Length=44 Min=5616 Max=9970 Mean=7945.977272727273 StdDev=953.1993850529328 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_BrisbaneGC' Min=5616 Max=9970 Mean=7945.977272727273 StdDev=953.1993850529328 @@ -337,8 +284,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_NSW_State_LinearTrend_residue_Seasonal_MonthOfYea INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.034 MAPE_Forecast=0.034 MAPE_Test=0.034 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0338 SMAPE_Forecast=0.0338 SMAPE_Test=0.0338 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1808 MASE_Forecast=0.1808 MASE_Test=0.1808 -INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843487 L1_Forecast=727.7548391843487 L1_Test=727.7548391843487 -INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912562 L2_Forecast=979.3716750912562 L2_Test=979.3716750912562 +INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843488 L1_Forecast=727.7548391843488 L1_Test=727.7548391843488 +INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912563 L2_Forecast=979.3716750912563 L2_Test=979.3716750912563 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -347,7 +294,7 @@ INFO:pyaf.std:TREND_DETAIL_START INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (23488.339004277248, array([-2964.69662101])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717456 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717438 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END @@ -452,33 +399,8 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.8587746620178223 -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 0.8865337371826172 -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 0.9001491069793701 -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 1.0368328094482422 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 0.9820942878723145 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 1.0043771266937256 -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 0.9641866683959961 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.8920600414276123 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.9516644477844238 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.6696906089782715 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.6529593467712402 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.394697904586792 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.29161572456359863 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 5.612283945083618 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['MO'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.284876823425293 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 5.655675888061523 diff --git a/tests/references/hierarchical_test_hierarchy_AU_OC.log b/tests/references/hierarchical_test_hierarchy_AU_OC.log index 688e356c6..e86af0f37 100644 --- a/tests/references/hierarchical_test_hierarchy_AU_OC.log +++ b/tests/references/hierarchical_test_hierarchy_AU_OC.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] +INFO:pyaf.std:START_TRAINING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' City State Country 0 Sydney NSW_State Australia 1 NSW NSW_State Australia @@ -9,60 +9,10 @@ INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneG 5 QLD QLD_State Australia 6 Capitals Other_State Australia 7 Other Other_State Australia -INFO:pyaf.std:START_TRAINING 'BrisbaneGC' -INFO:pyaf.std:START_TRAINING 'Capitals' -INFO:pyaf.std:START_TRAINING 'QLD' -INFO:pyaf.std:START_TRAINING 'Sydney' -INFO:pyaf.std:START_TRAINING 'VIC' -INFO:pyaf.std:START_TRAINING 'NSW' -INFO:pyaf.std:START_TRAINING 'Other' -INFO:pyaf.std:START_TRAINING 'Melbourne' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BrisbaneGC' 11.977782249450684 -INFO:pyaf.std:START_TRAINING 'NSW_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other' 12.217578649520874 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Capitals' 12.278749704360962 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC' 12.239952087402344 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW' 12.273058891296387 -INFO:pyaf.std:START_TRAINING 'Other_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Melbourne' 12.251441717147827 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD' 12.302724838256836 -INFO:pyaf.std:START_TRAINING 'QLD_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Sydney' 12.350156784057617 -INFO:pyaf.std:START_TRAINING 'VIC_State' -INFO:pyaf.std:START_TRAINING 'Australia' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other_State' 7.000065565109253 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_State' 7.336339235305786 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_State' 7.069367408752441 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Australia' 7.044593572616577 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_State' 7.196722507476807 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 39.032670736312866 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.6941075325012207 -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 0.7527885437011719 -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 0.7645161151885986 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 0.7510526180267334 -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 0.8754143714904785 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 0.9573118686676025 -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.061213493347168 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.8190033435821533 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.8922460079193115 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.6462640762329102 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.5054950714111328 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.3410797119140625 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.2692239284515381 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 7.0476086139678955 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD @@ -94,38 +44,35 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Date', 'BrisbaneGC', 'BrisbaneGC_ 'Australia_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.031, 'RMSE': 2862.61287413609, 'MAE': 2153.1032257967736, 'SMAPE': 0.0305, 'ErrorMean': 578.6910966231361, 'ErrorStdDev': 2803.510135858046, 'R2': 0.8684212122377221, 'Pearson': 0.9349191863682207} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.031, 'RMSE': 2862.61287413609, 'MAE': 2153.1032257967736, 'SMAPE': 0.0305, 'ErrorMean': 578.6910966231361, 'ErrorStdDev': 2803.510135858046, 'R2': 0.8684212122377221, 'Pearson': 0.9349191863682207} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_OC_Forecast', 'Length': 44, 'MAPE': 0.0626, 'RMSE': 629.3346461015539, 'MAE': 468.37060043602963, 'SMAPE': 0.0602, 'ErrorMean': 177.9666041559154, 'ErrorStdDev': 603.6472352201903, 'R2': 0.5640910606579395, 'Pearson': 0.7740043728403107} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_OC_Forecast', 'Length': 44, 'MAPE': 0.0626, 'RMSE': 629.3346461015539, 'MAE': 468.37060043602963, 'SMAPE': 0.0602, 'ErrorMean': 177.9666041559154, 'ErrorStdDev': 603.6472352201903, 'R2': 0.5640910606579395, 'Pearson': 0.7740043728403107} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Capitals_OC_Forecast', 'Length': 44, 'MAPE': 0.0609, 'RMSE': 597.7976206243463, 'MAE': 480.927810240591, 'SMAPE': 0.0607, 'ErrorMean': 9.648422337710269, 'ErrorStdDev': 597.7197530369255, 'R2': 0.5486426642946032, 'Pearson': 0.7418405371605905} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Capitals_OC_Forecast', 'Length': 44, 'MAPE': 0.0609, 'RMSE': 597.7976206243463, 'MAE': 480.927810240591, 'SMAPE': 0.0607, 'ErrorMean': 9.648422337710269, 'ErrorStdDev': 597.7197530369255, 'R2': 0.5486426642946032, 'Pearson': 0.7418405371605905} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Melbourne_OC_Forecast', 'Length': 44, 'MAPE': 0.077, 'RMSE': 427.9286803374229, 'MAE': 358.0537563538887, 'SMAPE': 0.076, 'ErrorMean': 40.21660415591294, 'ErrorStdDev': 426.03471713640295, 'R2': 0.41909358133872987, 'Pearson': 0.6685329217747098} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Melbourne_OC_Forecast', 'Length': 44, 'MAPE': 0.077, 'RMSE': 427.9286803374229, 'MAE': 358.0537563538887, 'SMAPE': 0.076, 'ErrorMean': 40.21660415591294, 'ErrorStdDev': 426.03471713640295, 'R2': 0.41909358133872987, 'Pearson': 0.6685329217747098} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_OC_Forecast', 'Length': 44, 'MAPE': 0.0454, 'RMSE': 939.8443171946626, 'MAE': 700.6063512041296, 'SMAPE': 0.0447, 'ErrorMean': 144.51421969711944, 'ErrorStdDev': 928.6673144180506, 'R2': 0.9027540670658177, 'Pearson': 0.9513464888418197} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_OC_Forecast', 'Length': 44, 'MAPE': 0.0454, 'RMSE': 939.8443171946626, 'MAE': 700.6063512041296, 'SMAPE': 0.0447, 'ErrorMean': 144.51421969711944, 'ErrorStdDev': 928.6673144180506, 'R2': 0.9027540670658177, 'Pearson': 0.9513464888418197} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'NSW_State_OC_Forecast', 'Length': 44, 'MAPE': 0.0378, 'RMSE': 1060.5569035455087, 'MAE': 801.4883373101005, 'SMAPE': 0.0374, 'ErrorMean': 89.23238077853074, 'ErrorStdDev': 1056.7963511853327, 'R2': 0.9063187066914318, 'Pearson': 0.9523987523402475} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'NSW_State_OC_Forecast', 'Length': 44, 'MAPE': 0.0378, 'RMSE': 1060.5569035455087, 'MAE': 801.4883373101005, 'SMAPE': 0.0374, 'ErrorMean': 89.23238077853074, 'ErrorStdDev': 1056.7963511853327, 'R2': 0.9063187066914318, 'Pearson': 0.9523987523402475} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Other_OC_Forecast', 'Length': 44, 'MAPE': 0.0721, 'RMSE': 990.7624928576089, 'MAE': 745.4312715498155, 'SMAPE': 0.0699, 'ErrorMean': 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'Pearson': 0.8648468057069636} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_State_OC_Forecast', 'Length': 44, 'MAPE': 0.0512, 'RMSE': 1156.305210589418, 'MAE': 931.2749129181628, 'SMAPE': 0.0502, 'ErrorMean': 189.02411740275713, 'ErrorStdDev': 1140.7504648591414, 'R2': 0.7383725928042493, 'Pearson': 0.8648468057069636} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Sydney_OC_Forecast', 'Length': 44, 'MAPE': 0.0644, 'RMSE': 558.446948405196, 'MAE': 389.7134562331135, 'SMAPE': 0.0652, 'ErrorMean': -55.2818389185893, 'ErrorStdDev': 555.7039791731338, 'R2': 0.4720768953381026, 'Pearson': 0.6943065371796711} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Sydney_OC_Forecast', 'Length': 44, 'MAPE': 0.0644, 'RMSE': 558.446948405196, 'MAE': 389.7134562331135, 'SMAPE': 0.0652, 'ErrorMean': -55.2818389185893, 'ErrorStdDev': 555.7039791731338, 'R2': 0.4720768953381026, 'Pearson': 0.6943065371796711} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_OC_Forecast', 'Length': 44, 'MAPE': 0.0571, 'RMSE': 562.53099773067, 'MAE': 460.59477642322696, 'SMAPE': 0.0557, 'ErrorMean': 114.19387688319667, 'ErrorStdDev': 550.818374684658, 'R2': 0.9559121524252973, 'Pearson': 0.9786711976405592} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_OC_Forecast', 'Length': 44, 'MAPE': 0.0571, 'RMSE': 562.53099773067, 'MAE': 460.59477642322696, 'SMAPE': 0.0557, 'ErrorMean': 114.19387688319667, 'ErrorStdDev': 550.818374684658, 'R2': 0.9559121524252973, 'Pearson': 0.9786711976405592} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_State_OC_Forecast', 'Length': 44, 'MAPE': 0.0457, 'RMSE': 722.9043684390057, 'MAE': 571.4447630028994, 'SMAPE': 0.045, 'ErrorMean': 154.4104810391045, 'ErrorStdDev': 706.2210201158488, 'R2': 0.9435040960558352, 'Pearson': 0.9729030718491571} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_State_OC_Forecast', 'Length': 44, 'MAPE': 0.0457, 'RMSE': 722.9043684390057, 'MAE': 571.4447630028994, 'SMAPE': 0.045, 'ErrorMean': 154.4104810391045, 'ErrorStdDev': 706.2210201158488, 'R2': 0.9435040960558352, 'Pearson': 0.9729030718491571} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 24.007869958877563 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.031, 'RMSE': 2862.612874136096, 'MAE': 2153.1032257967713, 'SMAPE': 0.0305, 'ErrorMean': 578.6910966231643, 'ErrorStdDev': 2803.510135858046, 'R2': 0.8684212122377215, 'Pearson': 0.9349191863682205} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_OC_Forecast', 'Length': 44, 'MAPE': 0.031, 'RMSE': 2862.612874136096, 'MAE': 2153.1032257967713, 'SMAPE': 0.0305, 'ErrorMean': 578.6910966231643, 'ErrorStdDev': 2803.510135858046, 'R2': 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+INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_OC_Forecast', 'Length': 44, 'MAPE': 0.0668, 'RMSE': 899.288481653328, 'MAE': 712.8808663019946, 'SMAPE': 0.066, 'ErrorMean': 11.057513246825279, 'ErrorStdDev': 899.2204983401705, 'R2': 0.7558572577449072, 'Pearson': 0.8709622408397039} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'QLD_State_OC_Forecast', 'Length': 44, 'MAPE': 0.0512, 'RMSE': 1156.305210589414, 'MAE': 931.2749129181607, 'SMAPE': 0.0502, 'ErrorMean': 189.0241174027323, 'ErrorStdDev': 1140.7504648591412, 'R2': 0.7383725928042513, 'Pearson': 0.8648468057069637} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'QLD_State_OC_Forecast', 'Length': 44, 'MAPE': 0.0512, 'RMSE': 1156.305210589414, 'MAE': 931.2749129181607, 'SMAPE': 0.0502, 'ErrorMean': 189.0241174027323, 'ErrorStdDev': 1140.7504648591412, 'R2': 0.7383725928042513, 'Pearson': 0.8648468057069637} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Sydney_OC_Forecast', 'Length': 44, 'MAPE': 0.0644, 'RMSE': 558.4469484051945, 'MAE': 389.7134562331143, 'SMAPE': 0.0652, 'ErrorMean': -55.28183891857473, 'ErrorStdDev': 555.7039791731338, 'R2': 0.4720768953381056, 'Pearson': 0.6943065371796713} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Sydney_OC_Forecast', 'Length': 44, 'MAPE': 0.0644, 'RMSE': 558.4469484051945, 'MAE': 389.7134562331143, 'SMAPE': 0.0652, 'ErrorMean': -55.28183891857473, 'ErrorStdDev': 555.7039791731338, 'R2': 0.4720768953381056, 'Pearson': 0.6943065371796713} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_OC_Forecast', 'Length': 44, 'MAPE': 0.0571, 'RMSE': 562.5309977306665, 'MAE': 460.59477642322435, 'SMAPE': 0.0557, 'ErrorMean': 114.1938768831787, 'ErrorStdDev': 550.8183746846582, 'R2': 0.9559121524252978, 'Pearson': 0.9786711976405593} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_OC_Forecast', 'Length': 44, 'MAPE': 0.0571, 'RMSE': 562.5309977306665, 'MAE': 460.59477642322435, 'SMAPE': 0.0557, 'ErrorMean': 114.1938768831787, 'ErrorStdDev': 550.8183746846582, 'R2': 0.9559121524252978, 'Pearson': 0.9786711976405593} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'VIC_State_OC_Forecast', 'Length': 44, 'MAPE': 0.0457, 'RMSE': 722.9043684390068, 'MAE': 571.4447630028999, 'SMAPE': 0.045, 'ErrorMean': 154.4104810391088, 'ErrorStdDev': 706.2210201158489, 'R2': 0.943504096055835, 'Pearson': 0.9729030718491571} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'VIC_State_OC_Forecast', 'Length': 44, 'MAPE': 0.0457, 'RMSE': 722.9043684390068, 'MAE': 571.4447630028999, 'SMAPE': 0.045, 'ErrorMean': 154.4104810391088, 'ErrorStdDev': 706.2210201158489, 'R2': 0.943504096055835, 'Pearson': 0.9729030718491571} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 47.2637882232666 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=1998-01-01T00:00:00.000000 TimeMax=2008-10-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='BrisbaneGC' Length=44 Min=5616 Max=9970 Mean=7945.977272727273 StdDev=953.1993850529328 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_BrisbaneGC' Min=5616 Max=9970 Mean=7945.977272727273 StdDev=953.1993850529328 @@ -337,8 +284,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_NSW_State_LinearTrend_residue_Seasonal_MonthOfYea INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.034 MAPE_Forecast=0.034 MAPE_Test=0.034 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0338 SMAPE_Forecast=0.0338 SMAPE_Test=0.0338 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1808 MASE_Forecast=0.1808 MASE_Test=0.1808 -INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843487 L1_Forecast=727.7548391843487 L1_Test=727.7548391843487 -INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912562 L2_Forecast=979.3716750912562 L2_Test=979.3716750912562 +INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843488 L1_Forecast=727.7548391843488 L1_Test=727.7548391843488 +INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912563 L2_Forecast=979.3716750912563 L2_Test=979.3716750912563 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -347,7 +294,7 @@ INFO:pyaf.std:TREND_DETAIL_START INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (23488.339004277248, array([-2964.69662101])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717456 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717438 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END @@ -452,33 +399,8 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.9003024101257324 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 0.9152727127075195 -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 0.9179410934448242 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 0.9313783645629883 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 1.0160973072052002 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 0.9129750728607178 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 0.8730127811431885 -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.8923931121826172 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.8297004699707031 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.578880786895752 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.4664132595062256 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.3842611312866211 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.2001512050628662 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 6.167191505432129 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.26064395904541 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 6.196476697921753 diff --git a/tests/references/hierarchical_test_hierarchy_AU_TD.log b/tests/references/hierarchical_test_hierarchy_AU_TD.log index 47ce3a042..fc869fbf6 100644 --- a/tests/references/hierarchical_test_hierarchy_AU_TD.log +++ b/tests/references/hierarchical_test_hierarchy_AU_TD.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_TRAINING -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] +INFO:pyaf.std:START_TRAINING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' City State Country 0 Sydney NSW_State Australia 1 NSW NSW_State Australia @@ -9,60 +9,10 @@ INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneG 5 QLD QLD_State Australia 6 Capitals Other_State Australia 7 Other Other_State Australia -INFO:pyaf.std:START_TRAINING 'Capitals' -INFO:pyaf.std:START_TRAINING 'BrisbaneGC' -INFO:pyaf.std:START_TRAINING 'QLD' -INFO:pyaf.std:START_TRAINING 'Melbourne' -INFO:pyaf.std:START_TRAINING 'Other' -INFO:pyaf.std:START_TRAINING 'VIC' -INFO:pyaf.std:START_TRAINING 'NSW' -INFO:pyaf.std:START_TRAINING 'Sydney' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Capitals' 12.275784492492676 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Melbourne' 12.26540207862854 -INFO:pyaf.std:START_TRAINING 'NSW_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW' 12.275164604187012 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other' 12.284179449081421 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'BrisbaneGC' 12.328962802886963 -INFO:pyaf.std:START_TRAINING 'Other_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC' 12.349184274673462 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Sydney' 12.37428092956543 -INFO:pyaf.std:START_TRAINING 'QLD_State' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD' 12.412939310073853 -INFO:pyaf.std:START_TRAINING 'VIC_State' -INFO:pyaf.std:START_TRAINING 'Australia' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'QLD_State' 6.83719801902771 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Other_State' 6.900710821151733 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Australia' 6.919825553894043 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'VIC_State' 7.011423826217651 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'NSW_State' 7.185641288757324 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 35.78697943687439 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 0.9154980182647705 -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 0.8768746852874756 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.0289888381958008 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 0.9442777633666992 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 0.998197078704834 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 0.9685089588165283 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 1.1197388172149658 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.9484517574310303 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.8819799423217773 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.8302290439605713 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.5863146781921387 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.43836069107055664 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.23820066452026367 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 8.073690176010132 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['TD'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD @@ -103,64 +53,61 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['Date', 'BrisbaneGC', 'BrisbaneGC_ 'VIC_PHA_TD_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['NSW_State', 'Other_State', 'QLD_State', 'VIC_State']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Australia']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Australia_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Australia_PHA_TD_Forecast', 'Length': 44, 'MAPE': 0.0314, 'RMSE': 2988.2555377830236, 'MAE': 2171.068181818182, 'SMAPE': 0.0308, 'ErrorMean': 740.0227272727273, 'ErrorStdDev': 2895.1748690209965, 'R2': 0.8566175137768354, 'Pearson': 0.9303137342980036} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'BrisbaneGC_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0809, 'RMSE': 724.7303370979117, 'MAE': 616.4243205312524, 'SMAPE': 0.0784, 'ErrorMean': 169.3104648748422, 'ErrorStdDev': 704.6758318503039, 'R2': 0.42192341928602084, 'Pearson': 0.6908359461872079} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'BrisbaneGC_AHP_TD_Forecast', 'Length': 44, 'MAPE': 0.0809, 'RMSE': 724.7303370979117, 'MAE': 616.4243205312524, 'SMAPE': 0.0784, 'ErrorMean': 169.3104648748422, 'ErrorStdDev': 704.6758318503039, 'R2': 0.42192341928602084, 'Pearson': 0.6908359461872079} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 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INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_BrisbaneGC' Min=5616 Max=9970 Mean=7945.977272727273 StdDev=953.1993850529328 @@ -372,8 +319,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_NSW_State_LinearTrend_residue_Seasonal_MonthOfYea INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.034 MAPE_Forecast=0.034 MAPE_Test=0.034 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0338 SMAPE_Forecast=0.0338 SMAPE_Test=0.0338 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1808 MASE_Forecast=0.1808 MASE_Test=0.1808 -INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843487 L1_Forecast=727.7548391843487 L1_Test=727.7548391843487 -INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912562 L2_Forecast=979.3716750912562 L2_Test=979.3716750912562 +INFO:pyaf.std:MODEL_L1 L1_Fit=727.7548391843488 L1_Forecast=727.7548391843488 L1_Test=727.7548391843488 +INFO:pyaf.std:MODEL_L2 L2_Fit=979.3716750912563 L2_Forecast=979.3716750912563 L2_Test=979.3716750912563 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -382,7 +329,7 @@ INFO:pyaf.std:TREND_DETAIL_START INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (23488.339004277248, array([-2964.69662101])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717456 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _NSW_State_LinearTrend_residue_Seasonal_MonthOfYear -1069.1953597717438 {1: 5290.18221239089, 4: -1702.1101557823677, 7: -2732.030202679438, 10: -860.2910655340165} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END @@ -487,34 +434,9 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'BrisbaneGC'), (0, 'Capitals'), (0, 'Melbourne'), (0, 'NSW'), (0, 'Other'), (0, 'QLD'), (0, 'Sydney'), (0, 'VIC'), (1, 'NSW_State'), (1, 'Other_State'), (1, 'QLD_State'), (1, 'VIC_State'), (2, 'Australia')] -INFO:pyaf.std:START_FORECASTING 'BrisbaneGC' -INFO:pyaf.std:START_FORECASTING 'Capitals' -INFO:pyaf.std:START_FORECASTING 'Melbourne' -INFO:pyaf.std:START_FORECASTING 'NSW' -INFO:pyaf.std:START_FORECASTING 'Other' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'BrisbaneGC' 1.0751705169677734 -INFO:pyaf.std:START_FORECASTING 'QLD' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Capitals' 1.071852445602417 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Melbourne' 1.1201415061950684 -INFO:pyaf.std:START_FORECASTING 'Sydney' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW' 1.0624539852142334 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other' 1.1109464168548584 -INFO:pyaf.std:START_FORECASTING 'VIC' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD' 1.0329811573028564 -INFO:pyaf.std:START_FORECASTING 'NSW_State' -INFO:pyaf.std:START_FORECASTING 'Other_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Sydney' 0.9802811145782471 -INFO:pyaf.std:START_FORECASTING 'QLD_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC' 0.8714346885681152 -INFO:pyaf.std:START_FORECASTING 'VIC_State' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'NSW_State' 0.9088134765625 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Other_State' 0.7289812564849854 -INFO:pyaf.std:START_FORECASTING 'Australia' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'QLD_State' 0.5651659965515137 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'VIC_State' 0.3444192409515381 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Australia' 0.2520442008972168 +INFO:pyaf.std:START_FORECASTING '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['BrisbaneGC', 'Capitals', 'Melbourne', 'NSW', 'Other', 'QLD', 'Sydney', 'VIC', 'NSW_State', 'Other_State', 'QLD_State', 'VIC_State', 'Australia']' 6.832457065582275 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['TD'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 4.285261869430542 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 6.928085803985596 diff --git a/tests/references/model_control_test_ozone_all_models_enabled.log b/tests/references/model_control_test_ozone_all_models_enabled.log index 36d55859e..dfe0e01d7 100644 --- a/tests/references/model_control_test_ozone_all_models_enabled.log +++ b/tests/references/model_control_test_ozone_all_models_enabled.log @@ -1,85 +1,85 @@ INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.5, 0.1, 0.0) Month Ozone Time 0 1955-01 2.7 1955-01-01 1 1955-02 2.0 1955-02-01 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.5, 0.1, 0.0) 34.471455574035645 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.6000000000000001, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.6000000000000001, 0.1, 0.0) 50.80827975273132 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.7000000000000001, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.7000000000000001, 0.1, 0.0) 52.26551842689514 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.8, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.8, 0.1, 0.0) 59.4208197593689 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.9, 0.1, 0.0) -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.9, 0.1, 0.0) 71.04203200340271 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_TIME_IN_SECONDS _Ozone 268.03283309936523 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 268.42837953567505 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1968-07-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 32.75124788284302 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)' [PolyTrend + Cycle_None + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_bestCycle_byMAPE' [Cycle_None] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1581 MAPE_Forecast=0.1382 MAPE_Test=None -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1491 SMAPE_Forecast=0.1376 SMAPE_Test=None -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6784 MASE_Forecast=0.5719 MASE_Test=None -INFO:pyaf.std:MODEL_L1 L1_Fit=0.5946639323035057 L1_Forecast=0.46050213610999613 L1_Test=None -INFO:pyaf.std:MODEL_L2 L2_Fit=0.7992783835359704 L2_Forecast=0.5988849665280183 L2_Test=None -INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_Ozone' Min=1.224744871391589 Max=2.345207879911715 Mean=1.6888656389128833 StdDev=0.23126713490313816 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL 'Anscombe_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1642 MAPE_Forecast=0.1384 MAPE_Test=0.1408 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1559 SMAPE_Forecast=0.1512 SMAPE_Test=0.1438 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7168 MASE_Forecast=0.6055 MASE_Test=0.7524 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.62999952384404 L1_Forecast=0.47008581809495725 L1_Test=0.35565668910694576 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8311449866422522 L2_Forecast=0.6550475624599839 L2_Test=0.4346696712588745 +INFO:pyaf.std:MODEL_COMPLEXITY 86 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.1221874309598405, array([-2.38954202, -0.38841589, 1.03620791])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.8694442837265477, array([-0.27591835])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Ozone_PolyTrend_residue_bestCycle_byMAPE None -0.036022548290779 {} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Anscombe_Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag1 0.4016363567221183 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag10 0.1863306116230097 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag12 0.1753142557903944 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag36 0.17375658201440342 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag7 -0.16469315893917696 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag30 0.14800975722350934 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag20 -0.1188727499892588 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag4 0.10707580719819523 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag48 0.10312520062123998 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_Lag35 -0.1029173113587088 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.3209998211502658 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.11917290422114361 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag51 -0.10966014115710274 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.10811218454630063 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.08843747251747286 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.088412812875769 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.08727106698203252 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.08115916216732097 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.0811466286025117 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.07382216509764589 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.316213846206665 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3489341735839844 - Split Transformation ... TestMAPE IC -0 (0.5, 0.1, 0.0) _Ozone ... None 0 -1 (0.5, 0.1, 0.0) _Ozone ... None 0 -2 (0.5, 0.1, 0.0) Quantized_10_Ozone ... None 0 -3 (0.5, 0.1, 0.0) Anscombe_Ozone ... None 0 -4 (0.5, 0.1, 0.0) Anscombe_Ozone ... None 0 -... ... ... ... ... .. -1255 (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone ... None 0 -1256 (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone ... None 0 -1257 (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone ... None 0 -1258 (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone ... None 0 -1259 (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone ... None 0 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.272937059402466 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.5507197380065918 + Split Transformation ... TestMAPE IC +0 None Anscombe_Ozone ... 1.408000e-01 1 +1 None Anscombe_Ozone ... 1.382000e-01 1 +2 None _Ozone ... 2.426000e-01 1 +3 None _Ozone ... 2.447000e-01 1 +4 None Quantized_20_Ozone ... 1.380000e-01 0 +... ... ... ... ... .. +1255 None Box_Cox_-2.0_Ozone ... 2.639209e+08 0 +1256 None Box_Cox_-2.0_Ozone ... 5.677107e+08 0 +1257 None Box_Cox_-2.0_Ozone ... 5.677107e+08 0 +1258 None Box_Cox_-2.0_Ozone ... 1.097449e+09 0 +1259 None Box_Cox_-2.0_Ozone ... 4.579053e+08 0 -[5880 rows x 9 columns] -Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', - 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', - '_Ozone_PolyTrend_residue', '_Ozone_PolyTrend_residue_bestCycle_byMAPE', - '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue', - '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)', - '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)_residue', - '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', - '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', - '_Ozone_TransformedForecast', 'Ozone_Forecast', - '_Ozone_TransformedResidue', 'Ozone_Residue', +[1260 rows x 9 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', 'Anscombe_Ozone', + 'Anscombe_Ozone_LinearTrend', 'Anscombe_Ozone_LinearTrend_residue', + 'Anscombe_Ozone_LinearTrend_residue_zeroCycle', + 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue', + 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)', + 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)_residue', + 'Anscombe_Ozone_Trend', 'Anscombe_Ozone_Trend_residue', + 'Anscombe_Ozone_Cycle', 'Anscombe_Ozone_Cycle_residue', + 'Anscombe_Ozone_AR', 'Anscombe_Ozone_AR_residue', + 'Anscombe_Ozone_TransformedForecast', 'Ozone_Forecast', + 'Anscombe_Ozone_TransformedResidue', 'Ozone_Residue', 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], dtype='object') @@ -95,47 +95,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 1.184938 -205 1972-02-01 NaN 2.491261 -206 1972-03-01 NaN 3.010337 -207 1972-04-01 NaN 3.587059 -208 1972-05-01 NaN 3.825771 -209 1972-06-01 NaN 4.381998 -210 1972-07-01 NaN 4.433500 -211 1972-08-01 NaN 4.790043 -212 1972-09-01 NaN 4.165712 -213 1972-10-01 NaN 3.423718 -214 1972-11-01 NaN 2.645356 -215 1972-12-01 NaN 2.235572 +204 1972-01-01 NaN 1.024415 +205 1972-02-01 NaN 1.541823 +206 1972-03-01 NaN 2.017001 +207 1972-04-01 NaN 2.430853 +208 1972-05-01 NaN 2.879409 +209 1972-06-01 NaN 3.280126 +210 1972-07-01 NaN 3.414366 +211 1972-08-01 NaN 3.394543 +212 1972-09-01 NaN 3.075428 +213 1972-10-01 NaN 2.214360 +214 1972-11-01 NaN 1.542736 +215 1972-12-01 NaN 1.112522 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51)", - "Cycle": "Cycle_None", - "Signal_Transoformation": "NoTransf", - "Trend": "PolyTrend" - }, - "Model_Performance": { - "COMPLEXITY": "64", - "MAE": "0.46050213610999613", - "MAPE": "0.1382", - "MASE": "0.5719", - "RMSE": "0.5988849665280183" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Anscombe", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "86", + "MAE": "0.47008581809495725", + "MAPE": "0.1384", + "MASE": "0.6055", + "RMSE": "0.6550475624599839" + } } } @@ -144,7 +146,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.5885638245,"193":2.3763922007,"194":3.1784235327,"195":2.5454211461,"196":3.7391670935,"197":3.4095003778,"198":4.8168231096,"199":3.8658794356,"200":3.6105365666,"201":2.998160286,"202":2.2220259921,"203":1.5897393659,"204":1.1849376732,"205":2.4912613567,"206":3.0103373659,"207":3.5870593834,"208":3.8257708791,"209":4.3819982103,"210":4.4334998784,"211":4.7900429376,"212":4.1657122589,"213":3.4237182368,"214":2.64535624,"215":2.2355719979}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.1202220186,"193":1.9384005467,"194":2.4328115966,"195":2.4019066825,"196":3.1709712472,"197":3.1681773447,"198":4.1619230994,"199":3.5832671054,"200":3.0640691022,"201":2.3561708754,"202":1.7120744534,"203":1.2276411329,"204":1.0244151392,"205":1.5418229577,"206":2.0170010596,"207":2.4308531985,"208":2.8794089774,"209":3.2801258093,"210":3.4143660536,"211":3.39454272,"212":3.0754284218,"213":2.2143596364,"214":1.5427356286,"215":1.1125216345}} diff --git a/tests/references/model_control_test_ozone_no_models_enabled.log b/tests/references/model_control_test_ozone_no_models_enabled.log index c4786f6f6..7a643b36b 100644 --- a/tests/references/model_control_test_ozone_no_models_enabled.log +++ b/tests/references/model_control_test_ozone_no_models_enabled.log @@ -1,11 +1,5 @@ INFO:pyaf.std:START_TRAINING 'Ozone' - Month Ozone Time -0 1955-01 2.7 1955-01-01 -1 1955-02 2.0 1955-02-01 -2 1955-03 3.6 1955-03-01 -3 1955-04 5.0 1955-04-01 -4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 0.4033854007720947 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 1.908677577972412 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -32,9 +26,25 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.110298156738281 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.14559626579284668 +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.17182493209839 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.3651301860809326 + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.5344 0.8365 @@ -80,31 +90,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "1.2105245517010224", - "MAPE": "0.5344", - "MASE": "1.5593", - "RMSE": "1.426654943722196" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "1.2105245517010224", + "MAPE": "0.5344", + "MASE": "1.5593", + "RMSE": "1.426654943722196" + } } } diff --git a/tests/references/neuralnet_test_air_passengers_CPU_theano.log b/tests/references/neuralnet_test_air_passengers_CPU_theano.log index f1f9de6b3..e22241032 100644 --- a/tests/references/neuralnet_test_air_passengers_CPU_theano.log +++ b/tests/references/neuralnet_test_air_passengers_CPU_theano.log @@ -1,94 +1,257 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' Using Theano backend. +WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using Theano backend. +WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using Theano backend. +WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using Theano backend. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_NoAR 8 0.4168 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_NoAR 0 0.4166 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_NoAR 40 0.094 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_NoAR 32 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_NoAR 24 0.1156 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_NoAR 16 0.1159 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_NoAR 24 0.162 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_NoAR 16 0.1614 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_NoAR 40 0.1265 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_NoAR 32 0.1255 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_NoAR 72 0.1255 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_NoAR 56 0.1298 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_NoAR 48 0.1288 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_NoAR 56 0.1776 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_NoAR 48 0.1742 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_NoAR 40 2183869.37 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_NoAR 32 2183869.37 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_NoAR 72 2183870.1252 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_NoAR 56 2183870.2071 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_NoAR 48 2183870.0849 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_NoAR 56 2183869.9651 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_NoAR 48 2183870.1427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_NoAR 40 0.9788 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_NoAR 72 0.0962 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_NoAR 56 0.4956 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_NoAR 48 0.4856 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_NoAR 56 0.2101 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_NoAR 48 0.2138 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 6.654531717300415 +WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20490') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20492') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20492') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20491') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20489') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20491') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20492') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20490') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20491') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20492') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20489') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20490') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20489') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20492') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20489') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20492') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20491') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20490') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20492') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20491') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20490') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20491') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20490') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20491') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20490') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20489') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20491') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20489') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20489') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20489') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +Using Theano backend. +WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 41 0.4887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 41 0.1717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 33 0.494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 33 0.1646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.0777 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.029 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 65 0.074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 65 0.0307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 49 0.0433 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1869 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1909 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 49 0.0536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.2523 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 0.3023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.4374 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.49 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.3491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.2219 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.297 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.3699 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.5862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1.0428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.5474 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.6746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.7238 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910119.3186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 1910118.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.9999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.236 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 0.9999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.2842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.0492 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.1993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.55 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.2139 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.2688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.1825 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.2694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.1286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_LSTM(33) 41 0.4105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_MLP(33) 41 0.084 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_LSTM(33) 33 0.4178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_MLP(33) 33 0.0733 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_LSTM(33) 73 0.0662 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_MLP(33) 73 0.0305 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_LSTM(33) 65 0.0649 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_MLP(33) 65 0.0235 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_LSTM(33) 57 0.1154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_MLP(33) 57 0.0496 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_LSTM(33) 49 0.1156 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_MLP(33) 49 0.0335 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_LSTM(33) 57 0.1292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_MLP(33) 57 0.0365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_LSTM(33) 49 0.1296 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_MLP(33) 49 0.0441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_LSTM(33) 73 0.2732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_MLP(33) 73 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_LSTM(33) 65 0.2603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_MLP(33) 65 0.2298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_LSTM(33) 105 0.4244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_MLP(33) 105 0.4541 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_LSTM(33) 97 0.2218 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_MLP(33) 97 0.0394 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_LSTM(33) 89 0.2851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_MLP(33) 89 0.159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_LSTM(33) 81 0.2535 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_MLP(33) 81 0.3577 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_LSTM(33) 89 0.3869 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_MLP(33) 89 0.6867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_LSTM(33) 81 0.345 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_MLP(33) 81 0.366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_LSTM(33) 73 0.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_MLP(33) 73 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_LSTM(33) 65 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_MLP(33) 65 2183870.8022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_LSTM(33) 105 1949317.1077 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_MLP(33) 105 2183870.8022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_LSTM(33) 97 2183870.8022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_MLP(33) 97 2183870.8022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_LSTM(33) 89 2183870.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_MLP(33) 89 2183870.0874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_LSTM(33) 81 2183870.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_MLP(33) 81 2183870.0874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_LSTM(33) 89 2183870.1427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_MLP(33) 89 2183870.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_LSTM(33) 81 2183870.1427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_MLP(33) 81 2183870.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_LSTM(33) 73 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_MLP(33) 73 0.1965 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_LSTM(33) 65 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_MLP(33) 65 0.2246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_LSTM(33) 105 0.0969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_MLP(33) 105 0.0603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_LSTM(33) 97 0.0951 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_MLP(33) 97 0.0627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_LSTM(33) 89 0.4856 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_MLP(33) 89 0.1955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_LSTM(33) 81 0.4857 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_MLP(33) 81 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_LSTM(33) 89 0.2137 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_MLP(33) 89 0.1811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_LSTM(33) 81 0.2148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_MLP(33) 81 0.1317 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 496.6165306568146 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_AirPassengers_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0862 MAPE_Forecast=0.0951 MAPE_Test=0.1 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0868 SMAPE_Forecast=0.0946 SMAPE_Test=0.1003 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9896 MASE_Forecast=0.9685 MASE_Test=0.9593 -INFO:pyaf.std:MODEL_L1 L1_Fit=18.5 L1_Forecast=35.875 L1_Test=43.166666666666664 -INFO:pyaf.std:MODEL_L2 L2_Fit=23.21143755421739 L2_Forecast=43.983424908329575 L2_Test=50.37525847741792 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)' [LinearTrend + NoCycle + MLP(33)] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)' [MLP(33)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1934 MAPE_Forecast=0.2438 MAPE_Test=0.2689 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1841 SMAPE_Forecast=0.2866 SMAPE_Test=0.3423 +INFO:pyaf.std:MODEL_MASE MASE_Fit=2.2326 MASE_Forecast=2.6171 MASE_Test=2.7548 +INFO:pyaf.std:MODEL_L1 L1_Fit=41.738135843043324 L1_Forecast=96.94640293756167 L1_Test=123.96625134046549 +INFO:pyaf.std:MODEL_L2 L2_Fit=51.28659488523553 L2_Forecast=123.98138036804812 L2_Test=162.1857017924399 +INFO:pyaf.std:MODEL_COMPLEXITY 49 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (114.90523344816275, array([197.60619977])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 30.993897676467896 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.6404180526733398 - Split Transformation ... TestMAPE TestMASE -1 None _AirPassengers ... 0.1000 0.9593 -0 None _AirPassengers ... 0.0995 0.9512 -2 None CumSum_AirPassengers ... 0.1000 0.9593 -3 None Diff_AirPassengers ... 0.1000 0.9593 -4 None RelDiff_AirPassengers ... 0.1000 0.9593 - -[5 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.0995 0.9512 -1 None _AirPassengers ... 0.1000 0.9593 -2 None CumSum_AirPassengers ... 0.1000 0.9593 -3 None Diff_AirPassengers ... 0.1000 0.9593 -4 None RelDiff_AirPassengers ... 0.1000 0.9593 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 33.083558559417725 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 1.9633114337921143 + Split Transformation ... TestMAPE TestMASE +0 None _AirPassengers ... 0.0307 0.2797 +1 None _AirPassengers ... 0.0290 0.2846 +2 None _AirPassengers ... 0.0433 0.3925 +3 None _AirPassengers ... 0.0362 0.3579 +4 None Diff_AirPassengers ... 0.0360 0.3362 [5 rows x 20 columns] Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', - '_AirPassengers', '_AirPassengers_Lag1Trend', - '_AirPassengers_Lag1Trend_residue', - '_AirPassengers_Lag1Trend_residue_zeroCycle', - '_AirPassengers_Lag1Trend_residue_zeroCycle_residue', - '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR', - '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', + '_AirPassengers', '_AirPassengers_LinearTrend', + '_AirPassengers_LinearTrend_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)_residue', '_AirPassengers_Trend', '_AirPassengers_Trend_residue', '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', '_AirPassengers_AR', '_AirPassengers_AR_residue', @@ -124,49 +287,51 @@ Forecasts 129 1959.750000 ... NaN 130 1959.833333 ... NaN 131 1959.916667 ... NaN -132 1960.000000 ... 491.207513 -133 1960.083333 ... 545.592719 -134 1960.166667 ... 580.770497 -135 1960.250000 ... 599.344979 -136 1960.333333 ... 598.644462 -137 1960.416667 ... 596.780600 -138 1960.500000 ... 594.939321 -139 1960.583333 ... 592.068798 -140 1960.666667 ... 576.374421 -141 1960.750000 ... 547.504940 -142 1960.833333 ... 504.680180 -143 1960.916667 ... 467.128960 +132 1960.000000 ... 675.357137 +133 1960.083333 ... 646.649551 +134 1960.166667 ... 615.513665 +135 1960.250000 ... 896.013747 +136 1960.333333 ... 792.230656 +137 1960.416667 ... 880.195848 +138 1960.500000 ... 1357.643291 +139 1960.583333 ... 1459.247036 +140 1960.666667 ... 1380.683472 +141 1960.750000 ... 1650.255558 +142 1960.833333 ... 2147.040217 +143 1960.916667 ... 2440.678539 [24 rows x 5 columns] { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "MLP(33)", + "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "35.875", - "MAPE": "0.0951", - "MASE": "0.9685", - "RMSE": "43.983424908329575" + "Model_Performance": { + "COMPLEXITY": "49", + "MAE": "96.94640293756167", + "MAPE": "0.2438", + "MASE": "2.6171", + "RMSE": "123.98138036804812" + } } } @@ -175,7 +340,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":337.0,"121":360.0,"122":342.0,"123":406.0,"124":396.0,"125":420.0,"126":472.0,"127":548.0,"128":559.0,"129":463.0,"130":407.0,"131":362.0,"132":405.0,"133":405.0,"134":405.0,"135":405.0,"136":405.0,"137":405.0,"138":405.0,"139":405.0,"140":405.0,"141":405.0,"142":405.0,"143":405.0},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":318.7924871797,"133":264.4072813645,"134":229.2295028548,"135":210.6550208178,"136":211.3555378191,"137":213.2194000079,"138":215.0606786366,"139":217.9312023167,"140":233.6255793105,"141":262.4950597348,"142":305.3198197567,"143":342.8710400323},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":491.2075128203,"133":545.5927186355,"134":580.7704971452,"135":599.3449791822,"136":598.6444621809,"137":596.7805999921,"138":594.9393213634,"139":592.0687976833,"140":576.3744206895,"141":547.5049402652,"142":504.6801802433,"143":467.1289599677}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":396.2165101715,"121":393.148710137,"122":435.2989276804,"123":471.6033200285,"124":371.9360708362,"125":301.4026488532,"126":344.5768265101,"127":408.5202512223,"128":411.0988840642,"129":431.6415805504,"130":478.9227795394,"131":444.1046132937,"132":432.3536314963,"133":405.6761131389,"134":453.4443612304,"135":502.0539484333,"136":405.2467341835,"137":294.2653713742,"138":363.7059598588,"139":434.9321814306,"140":394.2833000055,"141":376.079208467,"142":468.6379419406,"143":572.0598269645},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":189.3501259749,"133":164.7026756618,"134":291.3750579175,"135":108.0941503639,"136":18.2628124281,"137":-291.6651053133,"138":-630.2313716527,"139":-589.3826731146,"140":-592.1168716556,"141":-898.0971414155,"142":-1209.7643331953,"143":-1296.5588855331},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":675.3571370177,"133":646.6495506159,"134":615.5136645433,"135":896.0137465027,"136":792.230655939,"137":880.1958480617,"138":1357.6432913704,"139":1459.2470359758,"140":1380.6834716667,"141":1650.2555583495,"142":2147.0402170765,"143":2440.6785394621}} diff --git a/tests/references/neuralnet_test_air_passengers_GPU_tensorflow.log b/tests/references/neuralnet_test_air_passengers_GPU_tensorflow.log index 93f9be2b2..27b80c248 100644 --- a/tests/references/neuralnet_test_air_passengers_GPU_tensorflow.log +++ b/tests/references/neuralnet_test_air_passengers_GPU_tensorflow.log @@ -7,39 +7,71 @@ Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_NoAR 8 0.4168 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.4897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.4897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.1279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.1279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.2078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.2078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.1378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.1497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.3494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.7002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 48 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 48 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.2691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.4166 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_NoAR 0 0.4166 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_NoAR 40 0.094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_NoAR 40 0.0943 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_NoAR 32 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_NoAR 24 0.1156 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_NoAR 24 0.1159 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_NoAR 16 0.1159 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_NoAR 24 0.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_NoAR 24 0.1614 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_NoAR 16 0.1614 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_NoAR 40 0.1265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_NoAR 40 0.1255 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_NoAR 32 0.1255 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_NoAR 72 0.1255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_NoAR 72 0.4091 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_NoAR 56 0.1298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_NoAR 56 0.1288 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_NoAR 48 0.1288 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_NoAR 56 0.1776 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_NoAR 56 0.3946 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_NoAR 48 0.1742 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_NoAR 40 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_NoAR 40 0.6651 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_NoAR 32 2183869.37 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_NoAR 72 2183870.1252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.0951 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_NoAR 56 2183870.2071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_NoAR 56 2183870.0849 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_NoAR 48 2183870.0849 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_NoAR 56 2183869.9651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_NoAR 56 2183870.1427 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_NoAR 48 2183870.1427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_NoAR 40 0.9788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_NoAR 40 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_NoAR 72 0.0962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_NoAR 72 0.0991 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_NoAR 56 0.4956 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_NoAR 56 0.4856 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_NoAR 48 0.4856 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_NoAR 56 0.2101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_NoAR 56 0.2138 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_NoAR 48 0.2138 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 11.613016366958618 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 9.60141897201538 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -54,88 +86,225 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9896 MASE_Forecast=0.9685 MASE_Test=0.9593 INFO:pyaf.std:MODEL_L1 L1_Fit=18.5 L1_Forecast=35.875 L1_Test=43.166666666666664 INFO:pyaf.std:MODEL_L2 L2_Fit=23.21143755421739 L2_Forecast=43.983424908329575 L2_Test=50.37525847741792 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 112 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 33.11790418624878 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5427873134613037 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.083531379699707 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.7363481521606445 INFO:pyaf.std:START_TRAINING 'AirPassengers' Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_NoAR 8 0.4168 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_NoAR 0 0.4166 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_NoAR 40 0.094 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_NoAR 32 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_NoAR 24 0.1156 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_NoAR 16 0.1159 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_NoAR 24 0.162 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_NoAR 16 0.1614 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_NoAR 40 0.1265 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_NoAR 32 0.1255 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_NoAR 72 0.1255 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_NoAR 56 0.1298 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_NoAR 48 0.1288 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_NoAR 56 0.1776 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_NoAR 48 0.1742 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_NoAR 40 2183869.37 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_NoAR 32 2183869.37 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_NoAR 72 2183870.1252 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_NoAR 56 2183870.2071 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_NoAR 48 2183870.0849 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_NoAR 56 2183869.9651 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_NoAR 48 2183870.1427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_NoAR 40 0.9788 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_NoAR 72 0.0962 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_NoAR 56 0.4956 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_NoAR 48 0.4856 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_NoAR 56 0.2101 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_NoAR 48 0.2138 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 11.041028499603271 +WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1623: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. +Instructions for updating: +If using Keras pass *_constraint arguments to layers. +WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs. +2020-07-29 18:35:22.102608: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 +2020-07-29 18:35:22.108652: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-29 18:35:22.109070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: +name: GeForce GT 730 major: 3 minor: 5 memoryClockRate(GHz): 0.9015 +pciBusID: 0000:0f:00.0 +2020-07-29 18:35:22.109310: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.2'; dlerror: libcudart.so.10.2: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-29 18:35:22.112546: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 +2020-07-29 18:35:22.116314: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 +2020-07-29 18:35:22.117225: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 +2020-07-29 18:35:22.120904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 +2020-07-29 18:35:22.122744: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 +2020-07-29 18:35:22.123096: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-29 18:35:22.123125: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. +Skipping registering GPU devices... +2020-07-29 18:35:22.123733: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 +To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags. +2020-07-29 18:35:22.164068: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2660215000 Hz +2020-07-29 18:35:22.169873: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5e18b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: +2020-07-29 18:35:22.170009: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version +2020-07-29 18:35:22.446079: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-29 18:35:22.446691: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5e8d510 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: +2020-07-29 18:35:22.446777: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GT 730, Compute Capability 3.5 +2020-07-29 18:35:22.446993: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: +2020-07-29 18:35:22.447055: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] +2020-07-29 18:35:22.447157: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 24. Tune using inter_op_parallelism_threads for best performance. +WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead. + +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 41 0.4953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 41 0.0343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 33 0.4916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 33 0.0696 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.0761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.0313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 65 0.0709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 65 0.04 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0535 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 49 0.129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 49 0.05 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1908 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0334 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1858 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 49 0.0327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.0891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.2123 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 0.1925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.2347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.373 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.4381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.1929 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0396 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.0985 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.2447 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.1173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.3009 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.4995 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.3827 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.6001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.878 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 146088.3262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 129402.6223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 1910119.3186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.1103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.0932 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.0639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.1001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.55 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.1053 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.55 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.1052 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.2687 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.0851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.2687 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.0829 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_LSTM(33) 41 0.4194 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_MLP(33) 41 0.0336 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_LSTM(33) 33 0.4185 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_MLP(33) 33 0.0362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_LSTM(33) 73 0.0641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_MLP(33) 73 0.0199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_LSTM(33) 65 0.0596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_MLP(33) 65 0.0281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_LSTM(33) 57 0.1169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_MLP(33) 57 0.04 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_LSTM(33) 49 0.1165 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_MLP(33) 49 0.0402 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_LSTM(33) 57 0.1293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_MLP(33) 57 0.0376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_LSTM(33) 49 0.1278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_MLP(33) 49 0.0341 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_LSTM(33) 73 0.096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_MLP(33) 73 0.1823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_LSTM(33) 65 0.1329 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_MLP(33) 65 0.2084 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_LSTM(33) 105 0.3788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_MLP(33) 105 0.418 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_LSTM(33) 97 0.1699 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_MLP(33) 97 0.0263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_LSTM(33) 89 0.0811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_MLP(33) 89 0.195 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_LSTM(33) 81 0.0948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_MLP(33) 81 0.2605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_LSTM(33) 89 0.2858 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_MLP(33) 89 0.5973 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_LSTM(33) 81 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_MLP(33) 81 0.3354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_LSTM(33) 73 0.7454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_MLP(33) 73 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_LSTM(33) 65 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_MLP(33) 65 2183870.8022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_LSTM(33) 105 18228.4137 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_MLP(33) 105 2183870.8022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_LSTM(33) 97 18975.6207 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_MLP(33) 97 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_LSTM(33) 89 2183870.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_MLP(33) 89 2183870.0874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_LSTM(33) 81 2183870.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_MLP(33) 81 2183870.0874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_LSTM(33) 89 2183870.1427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_MLP(33) 89 2183870.0298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_LSTM(33) 81 2183870.1427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_MLP(33) 81 2183870.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_LSTM(33) 73 1.0002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_MLP(33) 73 0.1416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_LSTM(33) 65 1.0002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_MLP(33) 65 0.1373 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_LSTM(33) 105 0.1007 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_MLP(33) 105 0.0709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_LSTM(33) 97 0.0951 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_MLP(33) 97 0.0714 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_LSTM(33) 89 0.4855 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_MLP(33) 89 0.0901 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_LSTM(33) 81 0.4856 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_MLP(33) 81 0.1051 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_LSTM(33) 89 0.2127 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_MLP(33) 89 0.0756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_LSTM(33) 81 0.2129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_MLP(33) 81 0.0801 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 1899.278201341629 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)' [Lag1Trend + NoCycle + MLP(33)] INFO:pyaf.std:TREND_DETAIL '_AirPassengers_Lag1Trend' [Lag1Trend] INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0862 MAPE_Forecast=0.0951 MAPE_Test=0.1 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0868 SMAPE_Forecast=0.0946 SMAPE_Test=0.1003 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9896 MASE_Forecast=0.9685 MASE_Test=0.9593 -INFO:pyaf.std:MODEL_L1 L1_Fit=18.5 L1_Forecast=35.875 L1_Test=43.166666666666664 -INFO:pyaf.std:MODEL_L2 L2_Fit=23.21143755421739 L2_Forecast=43.983424908329575 L2_Test=50.37525847741792 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)' [MLP(33)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0392 MAPE_Forecast=0.0281 MAPE_Test=0.04 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0395 SMAPE_Forecast=0.0282 SMAPE_Test=0.0404 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4147 MASE_Forecast=0.2893 MASE_Test=0.3801 +INFO:pyaf.std:MODEL_L1 L1_Fit=7.753587457661827 L1_Forecast=10.717032750447592 L1_Test=17.106223861376446 +INFO:pyaf.std:MODEL_L2 L2_Fit=9.50525325843745 L2_Forecast=14.1704707078929 L2_Test=19.987808107132345 +INFO:pyaf.std:MODEL_COMPLEXITY 65 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 112 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 14.94084644317627 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.7007474899291992 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 13.299287796020508 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.6153459548950195 Split Transformation ... TestMAPE TestMASE -1 None _AirPassengers ... 0.1000 0.9593 -0 None _AirPassengers ... 0.0995 0.9512 -2 None CumSum_AirPassengers ... 0.1000 0.9593 -3 None Diff_AirPassengers ... 0.1000 0.9593 -4 None RelDiff_AirPassengers ... 0.1000 0.9593 - -[5 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.0995 0.9512 +0 None _AirPassengers ... 0.1025 0.9824 1 None _AirPassengers ... 0.1000 0.9593 2 None CumSum_AirPassengers ... 0.1000 0.9593 3 None Diff_AirPassengers ... 0.1000 0.9593 @@ -202,31 +371,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "35.875", - "MAPE": "0.0951", - "MASE": "0.9685", - "RMSE": "43.983424908329575" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "35.875", + "MAPE": "0.0951", + "MASE": "0.9685", + "RMSE": "43.983424908329575" + } } } @@ -239,20 +410,12 @@ Forecasts - Split Transformation ... TestMAPE TestMASE -1 None _AirPassengers ... 0.1000 0.9593 -0 None _AirPassengers ... 0.0995 0.9512 -2 None CumSum_AirPassengers ... 0.1000 0.9593 -3 None Diff_AirPassengers ... 0.1000 0.9593 -4 None RelDiff_AirPassengers ... 0.1000 0.9593 - -[5 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.0995 0.9512 -1 None _AirPassengers ... 0.1000 0.9593 -2 None CumSum_AirPassengers ... 0.1000 0.9593 -3 None Diff_AirPassengers ... 0.1000 0.9593 -4 None RelDiff_AirPassengers ... 0.1000 0.9593 + Split Transformation ... TestMAPE TestMASE +0 None _AirPassengers ... 0.0313 0.2821 +1 None Diff_AirPassengers ... 0.0396 0.3796 +2 None _AirPassengers ... 0.0400 0.3801 +3 None _AirPassengers ... 0.0343 0.3268 +4 None _AirPassengers ... 0.0327 0.3120 [5 rows x 20 columns] Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', @@ -260,8 +423,8 @@ Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized '_AirPassengers_Lag1Trend_residue', '_AirPassengers_Lag1Trend_residue_zeroCycle', '_AirPassengers_Lag1Trend_residue_zeroCycle_residue', - '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR', - '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', + '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)', + '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)_residue', '_AirPassengers_Trend', '_AirPassengers_Trend_residue', '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', '_AirPassengers_AR', '_AirPassengers_AR_residue', @@ -297,49 +460,51 @@ Forecasts 129 1959.750000 ... NaN 130 1959.833333 ... NaN 131 1959.916667 ... NaN -132 1960.000000 ... 491.207513 -133 1960.083333 ... 545.592719 -134 1960.166667 ... 580.770497 -135 1960.250000 ... 599.344979 -136 1960.333333 ... 598.644462 -137 1960.416667 ... 596.780600 -138 1960.500000 ... 594.939321 -139 1960.583333 ... 592.068798 -140 1960.666667 ... 576.374421 -141 1960.750000 ... 547.504940 -142 1960.833333 ... 504.680180 -143 1960.916667 ... 467.128960 +132 1960.000000 ... 5.235678e+02 +133 1960.083333 ... 5.874755e+02 +134 1960.166667 ... 6.760443e+02 +135 1960.250000 ... 8.406387e+02 +136 1960.333333 ... 1.629702e+03 +137 1960.416667 ... 4.162309e+03 +138 1960.500000 ... 1.216012e+04 +139 1960.583333 ... 3.577379e+04 +140 1960.666667 ... 1.042746e+05 +141 1960.750000 ... 2.758606e+05 +142 1960.833333 ... 6.386067e+05 +143 1960.916667 ... 1.485270e+06 [24 rows x 5 columns] { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "35.875", - "MAPE": "0.0951", - "MASE": "0.9685", - "RMSE": "43.983424908329575" + "Model": { + "AR_Model": "MLP(33)", + "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "65", + "MAE": "10.717032750447592", + "MAPE": "0.0281", + "MASE": "0.2893", + "RMSE": "14.1704707078929" + } } } @@ -348,7 +513,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":337.0,"121":360.0,"122":342.0,"123":406.0,"124":396.0,"125":420.0,"126":472.0,"127":548.0,"128":559.0,"129":463.0,"130":407.0,"131":362.0,"132":405.0,"133":405.0,"134":405.0,"135":405.0,"136":405.0,"137":405.0,"138":405.0,"139":405.0,"140":405.0,"141":405.0,"142":405.0,"143":405.0},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":318.7924871797,"133":264.4072813645,"134":229.2295028548,"135":210.6550208178,"136":211.3555378191,"137":213.2194000079,"138":215.0606786366,"139":217.9312023167,"140":233.6255793105,"141":262.4950597348,"142":305.3198197567,"143":342.8710400323},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":491.2075128203,"133":545.5927186355,"134":580.7704971452,"135":599.3449791822,"136":598.6444621809,"137":596.7805999921,"138":594.9393213634,"139":592.0687976833,"140":576.3744206895,"141":547.5049402652,"142":504.6801802433,"143":467.1289599677}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":417.1336975098,"121":391.4572906494,"122":350.0742492676,"123":398.1986560822,"124":474.5759887695,"125":380.4394378662,"126":430.4254150391,"127":522.2077999115,"128":417.8685302734,"129":375.1405029297,"130":371.494758606,"131":467.0784301758,"132":495.7937011719,"133":536.8444061279,"134":579.6602973938,"135":565.533946991,"136":633.3712730408,"137":589.8858985901,"138":522.5616607666,"139":445.5340805054,"140":280.794670105,"141":205.0109710693,"142":212.8752365112,"143":390.9945011139},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":468.0195785844,"133":486.2132921084,"134":483.2763325867,"135":290.4292094442,"136":-362.9594085609,"137":-2982.5374203055,"138":-11114.9984940824,"139":-34882.7264667011,"140":-103713.0034670222,"141":-275450.6197955635,"142":-638180.9110635865,"143":-1484487.6945061714},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":523.5678237593,"133":587.4755201475,"134":676.0442622009,"135":840.6386845378,"136":1629.7019546425,"137":4162.3092174856,"138":12160.1218156156,"139":35773.7946277118,"140":104274.5928072321,"141":275860.6417377021,"142":638606.6615366089,"143":1485269.6835083992}} diff --git a/tests/references/neuralnet_test_air_passengers_GPU_theano.log b/tests/references/neuralnet_test_air_passengers_GPU_theano.log index 6012d729d..9914ae9bb 100644 --- a/tests/references/neuralnet_test_air_passengers_GPU_theano.log +++ b/tests/references/neuralnet_test_air_passengers_GPU_theano.log @@ -63,39 +63,71 @@ Using Theano backend. Using Theano backend. Using Theano backend. Using Theano backend. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_NoAR 8 0.4168 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.4897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.4897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.1279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.1279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.2078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.2078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.1378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.1497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.3494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.7002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 48 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 48 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.2691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.4166 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_NoAR 0 0.4166 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_NoAR 40 0.094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_NoAR 40 0.0943 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_NoAR 32 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_NoAR 24 0.1156 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_NoAR 24 0.1159 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_NoAR 16 0.1159 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_NoAR 24 0.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_NoAR 24 0.1614 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_NoAR 16 0.1614 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_NoAR 40 0.1265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_NoAR 40 0.1255 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_NoAR 32 0.1255 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_NoAR 72 0.1255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_NoAR 72 0.4091 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_NoAR 56 0.1298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_NoAR 56 0.1288 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_NoAR 48 0.1288 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_NoAR 56 0.1776 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_NoAR 56 0.3946 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_NoAR 48 0.1742 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_NoAR 40 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_NoAR 40 0.6651 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_NoAR 32 2183869.37 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_NoAR 72 2183870.1252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.0951 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_NoAR 56 2183870.2071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_NoAR 56 2183870.0849 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_NoAR 48 2183870.0849 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_NoAR 56 2183869.9651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_NoAR 56 2183870.1427 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_NoAR 48 2183870.1427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_NoAR 40 0.9788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_NoAR 40 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_NoAR 72 0.0962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_NoAR 72 0.0991 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_NoAR 56 0.4956 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_NoAR 56 0.4856 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_NoAR 48 0.4856 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_NoAR 56 0.2101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_NoAR 56 0.2138 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_NoAR 48 0.2138 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 4.9404847621917725 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.6240010261535645 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -110,32 +142,33 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9896 MASE_Forecast=0.9685 MASE_Test=0.9593 INFO:pyaf.std:MODEL_L1 L1_Fit=18.5 L1_Forecast=35.875 L1_Test=43.166666666666664 INFO:pyaf.std:MODEL_L2 L2_Fit=23.21143755421739 L2_Forecast=43.983424908329575 L2_Test=50.37525847741792 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 112 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 35.437644481658936 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.47940683364868164 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.060072422027588 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.7695584297180176 Split Transformation ... TestMAPE TestMASE -1 None _AirPassengers ... 0.1000 0.9593 -0 None _AirPassengers ... 0.0995 0.9512 -2 None CumSum_AirPassengers ... 0.1000 0.9593 -3 None Diff_AirPassengers ... 0.1000 0.9593 -4 None RelDiff_AirPassengers ... 0.1000 0.9593 - -[5 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.0995 0.9512 +0 None _AirPassengers ... 0.1025 0.9824 1 None _AirPassengers ... 0.1000 0.9593 2 None CumSum_AirPassengers ... 0.1000 0.9593 3 None Diff_AirPassengers ... 0.1000 0.9593 @@ -202,31 +235,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "35.875", - "MAPE": "0.0951", - "MASE": "0.9685", - "RMSE": "43.983424908329575" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "35.875", + "MAPE": "0.0951", + "MASE": "0.9685", + "RMSE": "43.983424908329575" + } } } diff --git a/tests/references/neuralnet_test_air_passengers_rnn_only.log b/tests/references/neuralnet_test_air_passengers_rnn_only.log index 38fc1cadb..f65d866bd 100644 --- a/tests/references/neuralnet_test_air_passengers_rnn_only.log +++ b/tests/references/neuralnet_test_air_passengers_rnn_only.log @@ -7,39 +7,71 @@ Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_NoAR 8 0.4168 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.4897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.4897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.1279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.1279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.2078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.2078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.1378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.1497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.3494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.7002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 48 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 48 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.2691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.4166 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_NoAR 0 0.4166 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_NoAR 40 0.094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_NoAR 40 0.0943 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_NoAR 32 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_NoAR 24 0.1156 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_NoAR 24 0.1159 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_NoAR 16 0.1159 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_NoAR 24 0.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_NoAR 24 0.1614 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_NoAR 16 0.1614 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_NoAR 40 0.1265 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_NoAR 40 0.1255 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_NoAR 32 0.1255 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_NoAR 72 0.1255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_NoAR 72 0.4091 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_NoAR 56 0.1298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_NoAR 56 0.1288 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_NoAR 48 0.1288 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_NoAR 56 0.1776 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_NoAR 56 0.3946 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_NoAR 48 0.1742 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_NoAR 40 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_NoAR 40 0.6651 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_NoAR 32 2183869.37 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_NoAR 72 2183870.1252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_NoAR 72 0.0951 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_NoAR 56 2183870.2071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_NoAR 56 2183870.0849 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_NoAR 48 2183870.0849 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_NoAR 56 2183869.9651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_NoAR 56 2183870.1427 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_NoAR 48 2183870.1427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_NoAR 40 0.9788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_NoAR 40 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_NoAR 72 0.0962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_NoAR 72 0.0991 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_NoAR 64 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_NoAR 56 0.4956 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_NoAR 56 0.4856 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_NoAR 48 0.4856 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_NoAR 56 0.2101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_NoAR 56 0.2138 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_NoAR 48 0.2138 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 7.156605958938599 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 7.358797073364258 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -54,32 +86,33 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9896 MASE_Forecast=0.9685 MASE_Test=0.9593 INFO:pyaf.std:MODEL_L1 L1_Fit=18.5 L1_Forecast=35.875 L1_Test=43.166666666666664 INFO:pyaf.std:MODEL_L2 L2_Fit=23.21143755421739 L2_Forecast=43.983424908329575 L2_Test=50.37525847741792 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 112 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 34.94760346412659 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.9085476398468018 - Split Transformation ... TestMAPE TestMASE -1 None _AirPassengers ... 0.1000 0.9593 -0 None _AirPassengers ... 0.0995 0.9512 -2 None CumSum_AirPassengers ... 0.1000 0.9593 -3 None Diff_AirPassengers ... 0.1000 0.9593 -4 None RelDiff_AirPassengers ... 0.1000 0.9593 - -[5 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.571272373199463 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.5450975894927979 Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.0995 0.9512 +0 None _AirPassengers ... 0.1025 0.9824 1 None _AirPassengers ... 0.1000 0.9593 2 None CumSum_AirPassengers ... 0.1000 0.9593 3 None Diff_AirPassengers ... 0.1000 0.9593 @@ -146,31 +179,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "35.875", - "MAPE": "0.0951", - "MASE": "0.9685", - "RMSE": "43.983424908329575" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "35.875", + "MAPE": "0.0951", + "MASE": "0.9685", + "RMSE": "43.983424908329575" + } } } diff --git a/tests/references/neuralnet_test_air_passengers_tensorflow.log b/tests/references/neuralnet_test_air_passengers_tensorflow.log index 377e5d2c0..0d156d13f 100644 --- a/tests/references/neuralnet_test_air_passengers_tensorflow.log +++ b/tests/references/neuralnet_test_air_passengers_tensorflow.log @@ -1,44 +1,81 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_NoAR 0 0.4166 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 6.057518720626831 +WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1623: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. +Instructions for updating: +If using Keras pass *_constraint arguments to layers. +WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs. +2020-07-29 18:34:43.529635: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 +2020-07-29 18:34:43.535783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-29 18:34:43.536226: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: +name: GeForce GT 730 major: 3 minor: 5 memoryClockRate(GHz): 0.9015 +pciBusID: 0000:0f:00.0 +2020-07-29 18:34:43.536480: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.2'; dlerror: libcudart.so.10.2: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-29 18:34:43.539886: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 +2020-07-29 18:34:43.543153: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 +2020-07-29 18:34:43.543960: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 +2020-07-29 18:34:43.547670: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 +2020-07-29 18:34:43.549802: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 +2020-07-29 18:34:43.550097: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-29 18:34:43.550128: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. +Skipping registering GPU devices... +2020-07-29 18:34:43.551164: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 +To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags. +2020-07-29 18:34:43.575825: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2660215000 Hz +2020-07-29 18:34:43.584095: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x674dd90 initialized for platform Host (this does not guarantee that XLA will be used). Devices: +2020-07-29 18:34:43.584197: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version +2020-07-29 18:34:44.015010: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-29 18:34:44.015960: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x67b0090 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: +2020-07-29 18:34:44.016051: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GT 730, Compute Capability 3.5 +2020-07-29 18:34:44.016234: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: +2020-07-29 18:34:44.016264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] +2020-07-29 18:34:44.016361: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 24. Tune using inter_op_parallelism_threads for best performance. +WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead. + +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(7) 7 0.489 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_LSTM(7) 7 0.4157 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 22.086617469787598 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(7)' [ConstantTrend + NoCycle + LSTM(7)] INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3089 MAPE_Forecast=0.4166 MAPE_Test=0.4897 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2789 SMAPE_Forecast=0.5335 SMAPE_Test=0.6549 -INFO:pyaf.std:MODEL_MASE MASE_Fit=3.1599 MASE_Forecast=4.3462 MASE_Test=4.7694 -INFO:pyaf.std:MODEL_L1 L1_Fit=59.07378472222222 L1_Forecast=161.0 L1_Test=214.62499999999997 -INFO:pyaf.std:MODEL_L2 L2_Fit=71.54266161218463 L2_Forecast=171.47946406850653 L2_Test=224.79719641020634 -INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(7)' [LSTM(7)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3031 MAPE_Forecast=0.4157 MAPE_Test=0.489 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2749 SMAPE_Forecast=0.5321 SMAPE_Test=0.6536 +INFO:pyaf.std:MODEL_MASE MASE_Fit=3.1121 MASE_Forecast=4.3376 MASE_Test=4.7624 +INFO:pyaf.std:MODEL_L1 L1_Fit=58.18080847436148 L1_Forecast=160.68024763589105 L1_Test=214.30654821296528 +INFO:pyaf.std:MODEL_L2 L2_Fit=70.74218465256418 L2_Forecast=171.17002779726153 L2_Test=224.48413096866847 +INFO:pyaf.std:MODEL_COMPLEXITY 7 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 213.70833333333334 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 35.91489791870117 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.7216689586639404 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.008764266967773 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.9376730918884277 Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.4897 4.7694 - -[1 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.4897 4.7694 +0 None _AirPassengers ... 0.489 4.7624 [1 rows x 20 columns] Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', @@ -46,8 +83,8 @@ Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized '_AirPassengers_ConstantTrend_residue', '_AirPassengers_ConstantTrend_residue_zeroCycle', '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', - '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR', - '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(7)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(7)_residue', '_AirPassengers_Trend', '_AirPassengers_Trend_residue', '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', '_AirPassengers_AR', '_AirPassengers_AR_residue', @@ -83,49 +120,51 @@ Forecasts 129 1959.750000 ... NaN 130 1959.833333 ... NaN 131 1959.916667 ... NaN -132 1960.000000 ... 549.808083 -133 1960.083333 ... 549.808083 -134 1960.166667 ... 549.808083 -135 1960.250000 ... 549.808083 -136 1960.333333 ... 549.808083 -137 1960.416667 ... 549.808083 -138 1960.500000 ... 549.808083 -139 1960.583333 ... 549.808083 -140 1960.666667 ... 549.808083 -141 1960.750000 ... 549.808083 -142 1960.833333 ... 549.808083 -143 1960.916667 ... 549.808083 +132 1960.000000 ... 549.491233 +133 1960.083333 ... 550.149269 +134 1960.166667 ... 550.571344 +135 1960.250000 ... 550.710263 +136 1960.333333 ... 550.783062 +137 1960.416667 ... 550.656374 +138 1960.500000 ... 550.614539 +139 1960.583333 ... 550.416500 +140 1960.666667 ... 550.351127 +141 1960.750000 ... 550.335517 +142 1960.833333 ... 550.269106 +143 1960.916667 ... 550.248637 [24 rows x 5 columns] { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "LSTM(7)", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(7)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "161.0", - "MAPE": "0.4166", - "MASE": "4.3462", - "RMSE": "171.47946406850653" + "Model_Performance": { + "COMPLEXITY": "7", + "MAE": "160.68024763589105", + "MAPE": "0.4157", + "MASE": "4.3376", + "RMSE": "171.17002779726153" + } } } @@ -134,7 +173,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":213.7083333333,"121":213.7083333333,"122":213.7083333333,"123":213.7083333333,"124":213.7083333333,"125":213.7083333333,"126":213.7083333333,"127":213.7083333333,"128":213.7083333333,"129":213.7083333333,"130":213.7083333333,"131":213.7083333333,"132":213.7083333333,"133":213.7083333333,"134":213.7083333333,"135":213.7083333333,"136":213.7083333333,"137":213.7083333333,"138":213.7083333333,"139":213.7083333333,"140":213.7083333333,"141":213.7083333333,"142":213.7083333333,"143":213.7083333333},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":-122.3914162409,"133":-122.3914162409,"134":-122.3914162409,"135":-122.3914162409,"136":-122.3914162409,"137":-122.3914162409,"138":-122.3914162409,"139":-122.3914162409,"140":-122.3914162409,"141":-122.3914162409,"142":-122.3914162409,"143":-122.3914162409},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":549.8080829076,"133":549.8080829076,"134":549.8080829076,"135":549.8080829076,"136":549.8080829076,"137":549.8080829076,"138":549.8080829076,"139":549.8080829076,"140":549.8080829076,"141":549.8080829076,"142":549.8080829076,"143":549.8080829076}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":213.9979785581,"121":213.9982652565,"122":214.0798063179,"123":214.0797006985,"124":214.0792912145,"125":213.9980399509,"126":214.0797586342,"127":214.0798063179,"128":214.0797731777,"129":214.0455419918,"130":213.9336151977,"131":213.8698441287,"132":213.9979785581,"133":213.9028331538,"134":213.9979783197,"135":213.9979785581,"136":214.0117547413,"137":213.8600750466,"138":213.8075464865,"139":213.6052374815,"140":213.5380055358,"141":213.5216736098,"142":213.4549358686,"143":213.4343378445},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":-121.4952759245,"133":-122.3436021924,"134":-122.5753868679,"135":-122.7143056747,"136":-122.7595522167,"137":-122.9362240216,"138":-122.9994460018,"139":-123.2060254856,"140":-123.275116264,"141":-123.292169952,"142":-123.3592339875,"143":-123.3799617816},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":549.4912330408,"133":550.1492685,"134":550.5713435074,"135":550.710262791,"136":550.7830616992,"137":550.6563741148,"138":550.6145389747,"139":550.4165004486,"140":550.3511273356,"141":550.3355171716,"142":550.2691057247,"143":550.2486374705}} diff --git a/tests/references/neuralnet_test_ozone__CPU_theano.log b/tests/references/neuralnet_test_ozone__CPU_theano.log index 4d8acce71..e7f661b16 100644 --- a/tests/references/neuralnet_test_ozone__CPU_theano.log +++ b/tests/references/neuralnet_test_ozone__CPU_theano.log @@ -2,85 +2,263 @@ INFO:pyaf.std:START_TRAINING 'Ozone' Using Theano backend. /home/antoine/.local/lib/python3.8/site-packages/theano/configdefaults.py:1952: UserWarning: Theano does not recognise this flag: lib.cnmem warnings.warn('Theano does not recognise this flag: {0}'.format(key)) -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 12.123361825942993 +WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20180') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 55 0.3228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 55 0.349 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 59 0.3506 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 59 0.3201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 51 0.2964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 51 0.2349 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.2429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.2269 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.2562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 83 0.2612 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 83 0.2552 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.2369 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.2537 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.2326 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.1922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 67 0.2181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 67 0.2432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.3398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.3867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.3672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.2592 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 67 0.3459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 67 0.3537 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.4199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.2485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.3588 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.9682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 2.1686 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 5.406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 1.732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 1.7069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.2537 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 1.8213 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 2.5386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.9365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 6.2099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.4038 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 3.2414 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.3312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 1.1145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 2.4513 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 2.3964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 7.0377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 1.5478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 2.8629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 3.8353 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.4561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 2.7165 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 4.2855 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 4781.7999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.4634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.5823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.4639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 7.998 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5374 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 9.7949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 97.5453 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 18.3999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 67040794.9705 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 19420884.6831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 190.9901 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 67040795.2719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 128901.1483 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 1.7484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 3.1025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 1.5452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 2.1402 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.9086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.5418 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.2414 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.3973 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.4056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.1851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.2867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.5569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.5184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5261 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.1725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.6048 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 0.1879 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.44 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 1.296 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 1.2203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 55 0.185 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 55 0.202 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_LSTM(51) 59 0.1792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_MLP(51) 59 0.2164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_LSTM(51) 51 0.1703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_MLP(51) 51 0.1894 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 87 0.2084 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 87 0.1836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_LSTM(51) 91 0.214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_MLP(51) 91 0.2077 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_LSTM(51) 83 0.2021 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_MLP(51) 83 0.2217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_LSTM(51) 75 0.2436 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_MLP(51) 75 0.1913 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_LSTM(51) 67 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_MLP(51) 67 0.1965 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1613 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1835 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_LSTM(51) 75 0.1831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_MLP(51) 75 0.1525 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_LSTM(51) 67 0.1971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_MLP(51) 67 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 0.1528 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 0.3101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_LSTM(51) 91 0.9605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_MLP(51) 91 0.29 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_LSTM(51) 83 0.5655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_MLP(51) 83 2.0427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 4.348 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 1.3877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_LSTM(51) 123 0.9375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_MLP(51) 123 0.2489 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_LSTM(51) 115 1.6017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_MLP(51) 115 1.824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 1.3596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 4.0548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_LSTM(51) 107 0.1753 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_MLP(51) 107 2.16 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_LSTM(51) 99 0.4668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_MLP(51) 99 0.6952 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.4899 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.6458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_LSTM(51) 107 3.2074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_MLP(51) 107 1.2263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_LSTM(51) 99 0.5474 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_MLP(51) 99 0.877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 0.5615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 0.5681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_LSTM(51) 91 0.5668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_MLP(51) 91 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_LSTM(51) 83 75.6919 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_MLP(51) 83 2.803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 46.9157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_LSTM(51) 123 0.4998 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_MLP(51) 123 24.8743 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_LSTM(51) 115 0.5686 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_MLP(51) 115 0.5683 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 2.5911 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_LSTM(51) 107 2.963 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_MLP(51) 107 43.4915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_LSTM(51) 99 562.5496 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_MLP(51) 99 5011.0756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 6178888.9596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 52667817.101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_LSTM(51) 107 3393474.6247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_MLP(51) 107 242.24 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_LSTM(51) 99 13002318.4871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_MLP(51) 99 17995.7341 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 1.8454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 2.7423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_LSTM(51) 91 1.63 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_MLP(51) 91 1.8441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_LSTM(51) 83 1.0783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_MLP(51) 83 0.6186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.2632 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.2856 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_LSTM(51) 123 0.2663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_MLP(51) 123 0.3064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_LSTM(51) 115 0.2108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_MLP(51) 115 0.2435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.2759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.3073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_LSTM(51) 107 0.3471 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_MLP(51) 107 0.2072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_LSTM(51) 99 0.2875 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_MLP(51) 99 0.2107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.5539 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.3537 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_LSTM(51) 107 0.7497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_MLP(51) 107 0.2556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_LSTM(51) 99 0.7387 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_MLP(51) 99 0.2799 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 1309.1332459449768 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1865 MAPE_Forecast=0.1796 MAPE_Test=0.2567 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1767 SMAPE_Forecast=0.1922 SMAPE_Test=0.2534 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7009 MASE_Test=1.2245 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6949908566782953 L1_Forecast=0.5441457301742297 L1_Test=0.5788565919613983 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8963893991467234 L2_Forecast=0.6418882311790933 L2_Test=0.6786653138108626 -INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)' [PolyTrend + Seasonal_MonthOfYear + LSTM(51)] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)' [LSTM(51)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1019 MAPE_Forecast=0.1613 MAPE_Test=0.3398 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0972 SMAPE_Forecast=0.1542 SMAPE_Test=0.2883 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4211 MASE_Forecast=0.6403 MASE_Test=1.7897 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.37011499425023997 L1_Forecast=0.4970492879526981 L1_Test=0.8460567430838161 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.5549147083016078 L2_Forecast=0.6173755653473005 L2_Test=0.9531228457629786 +INFO:pyaf.std:MODEL_COMPLEXITY 71 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.161127322430005, array([-2.3971979 , -0.44987431, 1.1799632 ])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear -0.027148841187878858 {1: -1.712786405574908, 2: -1.4674755026528925, 3: -0.8891695609010877, 4: -0.22811041171681623, 5: -0.12272742114544766, 6: 0.7107545164158466, 7: 1.3676800999398635, 8: 1.3990582743641302, 9: 1.0239538864888074, 10: 0.8859338153602634, 11: -0.5063790111465141, 12: -1.397296313966326} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 36.95231819152832 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5899813175201416 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 12.95119833946228 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.1198012828826904 INFO:pyaf.std:START_TRAINING 'Ozone' Month Ozone Time 0 1955-01 2.7 1955-01-01 @@ -89,24 +267,20 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 - -[2 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 -2 None _Ozone ... 0.2001 0.9051 -3 None _Ozone ... 0.2001 0.9051 -4 None CumSum_Ozone ... 0.2144 0.9560 +0 None _Ozone ... 0.2592 1.4177 +1 None Diff_Ozone ... 0.4199 1.9538 +2 None _Ozone ... 0.3398 1.7897 +3 None _Ozone ... 0.2964 1.5503 +4 None _Ozone ... 0.2369 1.0869 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', - '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -126,47 +300,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.851717 -205 1972-02-01 NaN 1.074794 -206 1972-03-01 NaN 1.759105 -207 1972-04-01 NaN 2.312951 -208 1972-05-01 NaN 2.428336 -209 1972-06-01 NaN 3.328336 -210 1972-07-01 NaN 3.820644 -211 1972-08-01 NaN 3.736028 -212 1972-09-01 NaN 3.859105 -213 1972-10-01 NaN 3.349165 -214 1972-11-01 NaN 2.207498 -215 1972-12-01 NaN 1.282498 +204 1972-01-01 NaN 2.553197 +205 1972-02-01 NaN 2.855487 +206 1972-03-01 NaN 3.460138 +207 1972-04-01 NaN 4.024380 +208 1972-05-01 NaN 4.172608 +209 1972-06-01 NaN 4.966478 +210 1972-07-01 NaN 5.686246 +211 1972-08-01 NaN 5.685405 +212 1972-09-01 NaN 5.249028 +213 1972-10-01 NaN 5.195516 +214 1972-11-01 NaN 3.865777 +215 1972-12-01 NaN 2.931199 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5441457301742297", - "MAPE": "0.1796", - "MASE": "0.7009", - "RMSE": "0.6418882311790933" + "Model": { + "AR_Model": "LSTM(51)", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "71", + "MAE": "0.4970492879526981", + "MAPE": "0.1613", + "MASE": "0.6403", + "RMSE": "0.6173755653473005" + } } } @@ -175,7 +351,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.995880736,"193":1.2189576591,"194":1.9036641905,"195":2.4575103444,"196":2.5728949598,"197":3.4728949598,"198":3.9652026521,"199":3.8805872675,"200":4.0036641905,"201":3.4937237523,"202":2.3520570856,"203":1.4270570856,"204":0.8517168408,"205":1.0747937639,"206":1.7591053258,"207":2.3129514796,"208":2.428336095,"209":3.328336095,"210":3.8206437873,"211":3.7360284027,"212":3.8591053258,"213":3.3491648875,"214":2.2074982208,"215":1.2824982208}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":2.2866025215,"193":2.5598511083,"194":3.1025994673,"195":3.7748461898,"196":3.8193939076,"197":4.8642668749,"198":5.3990388908,"199":5.5803580328,"200":5.3456233707,"201":4.9882558734,"202":3.6976891058,"203":3.0773060883,"204":2.5531969033,"205":2.8554870322,"206":3.4601382459,"207":4.0243803587,"208":4.1726079736,"209":4.966478428,"210":5.686246246,"211":5.6854051694,"212":5.2490277514,"213":5.1955158556,"214":3.8657771893,"215":2.9311987141}} @@ -185,94 +361,242 @@ Forecasts 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.348668575286865 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 55 0.3551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 55 0.2577 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 59 0.335 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 59 0.4336 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 51 0.3527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 51 0.3683 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.2255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.2198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.2567 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.2263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 83 0.2606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 83 0.2907 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.2327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.2582 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.2317 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 67 0.2423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 67 0.258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.3488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.4554 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.3703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.3675 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 67 0.4187 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 67 0.2376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.5158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 1.748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.2627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 8.2814 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.4879 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 1.4158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 2.3375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.5332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.6949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.3008 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.7104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 2.9124 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.5421 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 2.9742 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 1.2602 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 5.0805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.2347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 1.5091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 2.2717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 1.503 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 3.0998 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 8.3962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 2.3146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 5.8912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 496.5805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.4606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 364.3 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 5175.868 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.4634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.4635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.4635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.4975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 8.6002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 255.7064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 20937.9237 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5555 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 218.4862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 638.4753 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 25731926.2848 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 35376.4158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 110.4213 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 37365560.4282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 22109751.6262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 2.1917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 2.6733 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 1.7731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 2.0212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 1.2732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.5834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.2406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.4133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.2753 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.4114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.5398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.1833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.6632 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 0.1849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.1173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 1.2628 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.2697 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 1.2888 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 0.2224 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 55 0.1886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 55 0.1907 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_LSTM(51) 59 0.1755 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_MLP(51) 59 0.3044 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_LSTM(51) 51 0.1822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_MLP(51) 51 0.2343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 87 0.2064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 87 0.1739 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_LSTM(51) 91 0.217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_MLP(51) 91 0.2122 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_LSTM(51) 83 0.2093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_MLP(51) 83 0.2059 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1776 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_LSTM(51) 75 0.2461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_MLP(51) 75 0.1304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_LSTM(51) 67 0.2465 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_MLP(51) 67 0.2159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1608 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_MLP(51) 71 0.2168 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_LSTM(51) 75 0.1847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_MLP(51) 75 0.1763 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_LSTM(51) 67 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_MLP(51) 67 0.1642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 0.1831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 1.5999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_LSTM(51) 91 0.7827 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_MLP(51) 91 5.3155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_LSTM(51) 83 0.3218 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_MLP(51) 83 1.5254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 1.1258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.2279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_LSTM(51) 123 0.4601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_MLP(51) 123 0.2872 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_LSTM(51) 115 0.4232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_MLP(51) 115 2.0737 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.4134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 1.8004 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_LSTM(51) 107 1.1188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_MLP(51) 107 3.4442 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_LSTM(51) 99 0.2969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_MLP(51) 99 0.9047 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.4099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.9125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_LSTM(51) 107 0.5858 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_MLP(51) 107 3.4631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_LSTM(51) 99 0.5835 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_MLP(51) 99 2.1703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 1012.4542 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 0.6909 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_LSTM(51) 91 5576.6192 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_MLP(51) 91 462.1249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_LSTM(51) 83 0.4594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_MLP(51) 83 0.5781 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.589 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_LSTM(51) 123 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_MLP(51) 123 0.5683 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_LSTM(51) 115 0.5754 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_MLP(51) 115 4.1696 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 70.8872 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 1589.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_LSTM(51) 107 25.7601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_MLP(51) 107 1.5652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_LSTM(51) 99 588.4892 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_MLP(51) 99 2005.5927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 42807160.2037 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 9873383.5357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_LSTM(51) 107 1626.3264 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_MLP(51) 107 202.8211 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_LSTM(51) 99 12475851.4268 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_MLP(51) 99 6555113.4984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 2.0805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 2.3625 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_LSTM(51) 91 1.8591 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_MLP(51) 91 1.745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_LSTM(51) 83 1.1087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_MLP(51) 83 0.6673 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.2663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.2625 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_LSTM(51) 123 0.2756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_MLP(51) 123 0.2536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_LSTM(51) 115 0.2205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_MLP(51) 115 0.2427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.2616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.2809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_LSTM(51) 107 0.3416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_MLP(51) 107 0.2149 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_LSTM(51) 99 0.3499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_MLP(51) 99 0.2021 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.6793 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.3574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_LSTM(51) 107 0.75 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_MLP(51) 107 0.2695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_LSTM(51) 99 0.7498 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_MLP(51) 99 0.2268 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 242.65574836730957 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)' [LinearTrend + Cycle_None + MLP(51)] INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1865 MAPE_Forecast=0.1796 MAPE_Test=0.2567 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1767 SMAPE_Forecast=0.1922 SMAPE_Test=0.2534 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7009 MASE_Test=1.2245 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6949908566782953 L1_Forecast=0.5441457301742297 L1_Test=0.5788565919613983 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8963893991467234 L2_Forecast=0.6418882311790933 L2_Test=0.6786653138108626 -INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_bestCycle_byMAPE' [Cycle_None] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)' [MLP(51)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.7411 MAPE_Forecast=0.7659 MAPE_Test=0.74 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.6008 SMAPE_Forecast=0.8688 SMAPE_Test=0.8437 +INFO:pyaf.std:MODEL_MASE MASE_Fit=2.8272 MASE_Forecast=3.0615 MASE_Test=3.8603 +INFO:pyaf.std:MODEL_L1 L1_Fit=2.484975673837583 L1_Forecast=2.3766678924940257 L1_Test=1.824867000768849 +INFO:pyaf.std:MODEL_L2 L2_Fit=2.9784368695519468 L2_Forecast=2.908296284707063 L2_Test=2.1635683110116113 +INFO:pyaf.std:MODEL_COMPLEXITY 75 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Ozone_LinearTrend_residue_bestCycle_byMAPE None 0.012969125577120266 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 12.97557282447815 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.7330615520477295 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 - -[2 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.280886888504028 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.7102987766265869 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 -2 None _Ozone ... 0.2001 0.9051 -3 None _Ozone ... 0.2001 0.9051 -4 None CumSum_Ozone ... 0.2144 0.9560 +0 None _Ozone ... 0.1765 0.8466 +1 None _Ozone ... 0.3488 1.8337 +2 None _Ozone ... 0.2376 1.2498 +3 None _Ozone ... 0.2198 1.0488 +4 None _Ozone ... 0.3350 1.6763 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_LinearTrend_residue_bestCycle_byMAPE', + '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue', + '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)', + '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -292,47 +616,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.851717 -205 1972-02-01 NaN 1.074794 -206 1972-03-01 NaN 1.759105 -207 1972-04-01 NaN 2.312951 -208 1972-05-01 NaN 2.428336 -209 1972-06-01 NaN 3.328336 -210 1972-07-01 NaN 3.820644 -211 1972-08-01 NaN 3.736028 -212 1972-09-01 NaN 3.859105 -213 1972-10-01 NaN 3.349165 -214 1972-11-01 NaN 2.207498 -215 1972-12-01 NaN 1.282498 +204 1972-01-01 NaN 0.834821 +205 1972-02-01 NaN 3.076219 +206 1972-03-01 NaN 2.542779 +207 1972-04-01 NaN 3.716504 +208 1972-05-01 NaN 3.742330 +209 1972-06-01 NaN 4.151718 +210 1972-07-01 NaN 2.887562 +211 1972-08-01 NaN 2.784342 +212 1972-09-01 NaN 1.807982 +213 1972-10-01 NaN 0.919284 +214 1972-11-01 NaN 0.215240 +215 1972-12-01 NaN 2.316296 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "MLP(51)", + "Best_Decomposition": "_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)", + "Cycle": "Cycle_None", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5441457301742297", - "MAPE": "0.1796", - "MASE": "0.7009", - "RMSE": "0.6418882311790933" + "Model_Performance": { + "COMPLEXITY": "75", + "MAE": "2.3766678924940257", + "MAPE": "0.7659", + "MASE": "3.0615", + "RMSE": "2.908296284707063" + } } } @@ -341,7 +667,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.995880736,"193":1.2189576591,"194":1.9036641905,"195":2.4575103444,"196":2.5728949598,"197":3.4728949598,"198":3.9652026521,"199":3.8805872675,"200":4.0036641905,"201":3.4937237523,"202":2.3520570856,"203":1.4270570856,"204":0.8517168408,"205":1.0747937639,"206":1.7591053258,"207":2.3129514796,"208":2.428336095,"209":3.328336095,"210":3.8206437873,"211":3.7360284027,"212":3.8591053258,"213":3.3491648875,"214":2.2074982208,"215":1.2824982208}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.1726430276,"193":1.6119011747,"194":2.4964373983,"195":2.577899681,"196":6.0493591176,"197":3.9395795698,"198":4.5494668898,"199":4.1020230954,"200":2.0002328945,"201":1.4943175093,"202":1.84887084,"203":0.82862471,"204":0.8348213765,"205":3.0762186867,"206":2.5427786209,"207":3.7165038571,"208":3.7423299121,"209":4.1517183836,"210":2.8875618217,"211":2.7843424207,"212":1.8079817905,"213":0.9192838863,"214":0.2152404273,"215":2.3162956502}} diff --git a/tests/references/neuralnet_test_ozone__GPU_tensorflow.log b/tests/references/neuralnet_test_ozone__GPU_tensorflow.log index df41304eb..269ac04d1 100644 --- a/tests/references/neuralnet_test_ozone__GPU_tensorflow.log +++ b/tests/references/neuralnet_test_ozone__GPU_tensorflow.log @@ -1,85 +1,269 @@ INFO:pyaf.std:START_TRAINING 'Ozone' Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 15.019721269607544 +WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1623: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version. +Instructions for updating: +If using Keras pass *_constraint arguments to layers. +WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs. +2020-07-29 18:34:38.325329: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 +2020-07-29 18:34:38.348303: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-29 18:34:38.348768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: +name: GeForce GT 730 major: 3 minor: 5 memoryClockRate(GHz): 0.9015 +pciBusID: 0000:0f:00.0 +2020-07-29 18:34:38.349075: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.2'; dlerror: libcudart.so.10.2: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-29 18:34:38.429154: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 +2020-07-29 18:34:38.469127: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 +2020-07-29 18:34:38.481580: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 +2020-07-29 18:34:38.564760: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 +2020-07-29 18:34:38.577991: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 +2020-07-29 18:34:38.578722: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-29 18:34:38.578784: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. +Skipping registering GPU devices... +2020-07-29 18:34:38.583523: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 +To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags. +2020-07-29 18:34:38.650109: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2660215000 Hz +2020-07-29 18:34:38.657026: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5567d50 initialized for platform Host (this does not guarantee that XLA will be used). Devices: +2020-07-29 18:34:38.657154: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version +2020-07-29 18:34:38.955514: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-29 18:34:38.956790: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55ca050 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: +2020-07-29 18:34:38.956888: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GT 730, Compute Capability 3.5 +2020-07-29 18:34:38.957456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: +2020-07-29 18:34:38.957514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] +2020-07-29 18:34:38.957760: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 24. Tune using inter_op_parallelism_threads for best performance. +WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead. + +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 55 0.3192 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 55 0.2533 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 59 0.4129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 59 0.1998 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 51 0.3671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 51 0.3171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.2556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.1901 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.2133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 83 0.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 83 0.2597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.2252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.2336 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.2599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 67 0.2461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 67 0.1567 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.3614 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.3588 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.4281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.2503 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 67 0.4111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 67 0.3473 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 1.2357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 2.8175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 1.1556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 5.8871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.2372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 1.7162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 3.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 1.7508 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 1.527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 2.2299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 2.1982 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.4345 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.5502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 3.1181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 2.9205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 3.1061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 4.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 7.8454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 2.2364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 2.4728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.8763 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 1.7902 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 0.6053 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 0.5912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 347.5034 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 17506.9606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.4641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.4904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.4523 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.4638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 11.5421 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 14855340.2556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 459.3921 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 410.2324 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 38584488.7406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 67040794.9758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 3466580.556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 10413.0421 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 1119.3703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 17977.5258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 2.3628 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 2.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 1.7251 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 2.592 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 1.1169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.373 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.2129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.3316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.2208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.2293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.2128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.5238 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.3792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.3861 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.4833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 0.3311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.0865 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.5139 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 1.2826 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.4389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 1.096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 0.3472 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 55 0.1837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 55 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_LSTM(51) 59 0.1841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_MLP(51) 59 0.1843 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_LSTM(51) 51 0.1768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_MLP(51) 51 0.2274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 87 0.205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 87 0.2031 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_LSTM(51) 91 0.2303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_MLP(51) 91 0.1987 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_LSTM(51) 83 0.2162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_MLP(51) 83 0.1841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1697 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_LSTM(51) 75 0.2444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_MLP(51) 75 0.2533 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_LSTM(51) 67 0.2705 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_MLP(51) 67 0.1639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_MLP(51) 71 0.185 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_LSTM(51) 75 0.2019 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_MLP(51) 75 0.1817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_LSTM(51) 67 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_MLP(51) 67 0.1918 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 0.6547 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 1.6566 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_LSTM(51) 91 1.1523 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_MLP(51) 91 4.6581 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_LSTM(51) 83 0.4891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_MLP(51) 83 1.7164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 2.4195 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 1.2995 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_LSTM(51) 123 1.8556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_MLP(51) 123 1.5866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_LSTM(51) 115 0.9149 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_MLP(51) 115 0.3041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 1.8641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_LSTM(51) 107 1.8493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_MLP(51) 107 1.8057 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_LSTM(51) 99 2.7045 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_MLP(51) 99 5.1691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.4195 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.7176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_LSTM(51) 107 0.9946 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_MLP(51) 107 0.6096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_LSTM(51) 99 1.8047 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_MLP(51) 99 2.3973 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 14507.9344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 2801.996 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_LSTM(51) 91 0.5933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_MLP(51) 91 0.5685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_LSTM(51) 83 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_MLP(51) 83 0.5593 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.8236 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_LSTM(51) 123 0.5311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_MLP(51) 123 3.8694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_LSTM(51) 115 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_MLP(51) 115 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 8202651.4012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 297.4638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_LSTM(51) 107 0.5198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_MLP(51) 107 0.5683 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_LSTM(51) 99 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_MLP(51) 99 53.7676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 1063636.564 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 50408693.3384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_LSTM(51) 107 170737.9743 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_MLP(51) 107 140.2695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_LSTM(51) 99 26.1286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_MLP(51) 99 2768.8924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 1.7225 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 1.7418 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_LSTM(51) 91 2.2061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_MLP(51) 91 1.6192 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_LSTM(51) 83 1.1751 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_MLP(51) 83 0.4144 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.2659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.2665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_LSTM(51) 123 0.272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_MLP(51) 123 0.2568 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_LSTM(51) 115 0.222 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_MLP(51) 115 0.3036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_LSTM(51) 107 0.349 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_MLP(51) 107 0.4194 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_LSTM(51) 99 0.3536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_MLP(51) 99 0.3866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.6023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.5465 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_LSTM(51) 107 0.7618 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_MLP(51) 107 0.4395 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_LSTM(51) 99 0.7255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_MLP(51) 99 0.3768 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 3049.726697206497 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)' [LinearTrend + NoCycle + MLP(51)] INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1865 MAPE_Forecast=0.1796 MAPE_Test=0.2567 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1767 SMAPE_Forecast=0.1922 SMAPE_Test=0.2534 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7009 MASE_Test=1.2245 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6949908566782953 L1_Forecast=0.5441457301742297 L1_Test=0.5788565919613983 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8963893991467234 L2_Forecast=0.6418882311790933 L2_Test=0.6786653138108626 -INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)' [MLP(51)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1687 MAPE_Forecast=0.1639 MAPE_Test=0.1567 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1683 SMAPE_Forecast=0.1753 SMAPE_Test=0.1615 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7493 MASE_Forecast=0.7197 MASE_Test=0.8033 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6586355788483753 L1_Forecast=0.5587062373581303 L1_Test=0.3797353034108995 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8752025308367766 L2_Forecast=0.7910656412368469 L2_Test=0.4939399117915722 +INFO:pyaf.std:MODEL_COMPLEXITY 67 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 35.645649433135986 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5381739139556885 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 39.332757234573364 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.9381024837493896 INFO:pyaf.std:START_TRAINING 'Ozone' Month Ozone Time 0 1955-01 2.7 1955-01-01 @@ -88,24 +272,19 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 - -[2 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 -2 None _Ozone ... 0.2001 0.9051 -3 None _Ozone ... 0.2001 0.9051 -4 None CumSum_Ozone ... 0.2144 0.9560 +0 None _Ozone ... 0.1567 0.8033 +1 None _Ozone ... 0.2252 1.0656 +2 None _Ozone ... 0.3614 1.8722 +3 None _Ozone ... 0.2533 1.1945 +4 None _Ozone ... 0.3671 1.8576 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', - '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_LinearTrend_residue_zeroCycle', + '_Ozone_LinearTrend_residue_zeroCycle_residue', + '_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)', + '_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -125,47 +304,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.851717 -205 1972-02-01 NaN 1.074794 -206 1972-03-01 NaN 1.759105 -207 1972-04-01 NaN 2.312951 -208 1972-05-01 NaN 2.428336 -209 1972-06-01 NaN 3.328336 -210 1972-07-01 NaN 3.820644 -211 1972-08-01 NaN 3.736028 -212 1972-09-01 NaN 3.859105 -213 1972-10-01 NaN 3.349165 -214 1972-11-01 NaN 2.207498 -215 1972-12-01 NaN 1.282498 +204 1972-01-01 NaN 1.891454 +205 1972-02-01 NaN 1.177052 +206 1972-03-01 NaN 2.023464 +207 1972-04-01 NaN 1.448514 +208 1972-05-01 NaN 2.196539 +209 1972-06-01 NaN 3.122091 +210 1972-07-01 NaN 2.558237 +211 1972-08-01 NaN 2.724912 +212 1972-09-01 NaN 2.539563 +213 1972-10-01 NaN 3.104818 +214 1972-11-01 NaN 2.525602 +215 1972-12-01 NaN 2.321056 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5441457301742297", - "MAPE": "0.1796", - "MASE": "0.7009", - "RMSE": "0.6418882311790933" + "Model": { + "AR_Model": "MLP(51)", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "67", + "MAE": "0.5587062373581303", + "MAPE": "0.1639", + "MASE": "0.7197", + "RMSE": "0.7910656412368469" + } } } @@ -174,7 +355,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.995880736,"193":1.2189576591,"194":1.9036641905,"195":2.4575103444,"196":2.5728949598,"197":3.4728949598,"198":3.9652026521,"199":3.8805872675,"200":4.0036641905,"201":3.4937237523,"202":2.3520570856,"203":1.4270570856,"204":0.8517168408,"205":1.0747937639,"206":1.7591053258,"207":2.3129514796,"208":2.428336095,"209":3.328336095,"210":3.8206437873,"211":3.7360284027,"212":3.8591053258,"213":3.3491648875,"214":2.2074982208,"215":1.2824982208}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":3.104583464,"193":1.7771393167,"194":2.2539975024,"195":1.2679369017,"196":2.7008478018,"197":3.5899213786,"198":0.6911588129,"199":2.9524658327,"200":3.0154017998,"201":2.9551085606,"202":3.8816919588,"203":1.8707408155,"204":1.8914537165,"205":1.1770522218,"206":2.0234643914,"207":1.4485137448,"208":2.1965388226,"209":3.1220913585,"210":2.5582370547,"211":2.7249121791,"212":2.5395631208,"213":3.1048177079,"214":2.525602349,"215":2.3210562732}} @@ -184,55 +365,103 @@ Forecasts 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.584223031997681 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 2.191995143890381 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -241,29 +470,33 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYe INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1865 MAPE_Forecast=0.1796 MAPE_Test=0.2567 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1767 SMAPE_Forecast=0.1922 SMAPE_Test=0.2534 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7009 MASE_Test=1.2245 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6949908566782953 L1_Forecast=0.5441457301742297 L1_Test=0.5788565919613983 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8963893991467234 L2_Forecast=0.6418882311790933 L2_Test=0.6786653138108626 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1761 MAPE_Forecast=0.1765 MAPE_Test=0.2209 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1712 SMAPE_Forecast=0.1941 SMAPE_Test=0.2249 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7728 MASE_Forecast=0.7151 MASE_Test=1.0918 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.679243722119012 L1_Forecast=0.5551666232747684 L1_Test=0.5161409468920186 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9118183531362629 L2_Forecast=0.662953224721834 L2_Test=0.5961504841809397 INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.012969125577120266 {1: -1.6355903804183052, 2: -1.4774474242490654, 3: -0.8776655161662057, 4: -0.2654214593143367, 5: -0.1765218216970199, 6: 0.7586716848135344, 7: 1.2705207720895366, 8: 1.359815379282721, 9: 0.9720594361345896, 10: 0.8345785406856634, 11: -0.442570179696129, 12: -1.4421958172494698} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 12.566904067993164 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5694143772125244 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 - -[2 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 3.4409282207489014 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.28487157821655273 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 -2 None _Ozone ... 0.2001 0.9051 -3 None _Ozone ... 0.2001 0.9051 -4 None CumSum_Ozone ... 0.2144 0.9560 +0 None _Ozone ... 0.2209 1.0918 +1 None _Ozone ... 0.5734 2.8013 +2 None _Ozone ... 0.1221 0.6258 +3 None CumSum_Ozone ... 0.1962 0.9343 +4 None CumSum_Ozone ... 0.1962 0.9343 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -291,47 +524,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.851717 -205 1972-02-01 NaN 1.074794 -206 1972-03-01 NaN 1.759105 -207 1972-04-01 NaN 2.312951 -208 1972-05-01 NaN 2.428336 -209 1972-06-01 NaN 3.328336 -210 1972-07-01 NaN 3.820644 -211 1972-08-01 NaN 3.736028 -212 1972-09-01 NaN 3.859105 -213 1972-10-01 NaN 3.349165 -214 1972-11-01 NaN 2.207498 -215 1972-12-01 NaN 1.282498 +204 1972-01-01 NaN 0.934622 +205 1972-02-01 NaN 1.080521 +206 1972-03-01 NaN 1.668848 +207 1972-04-01 NaN 2.268848 +208 1972-05-01 NaN 2.345899 +209 1972-06-01 NaN 3.268848 +210 1972-07-01 NaN 3.768848 +211 1972-08-01 NaN 3.845899 +212 1972-09-01 NaN 3.445899 +213 1972-10-01 NaN 3.296569 +214 1972-11-01 NaN 2.007176 +215 1972-12-01 NaN 0.995701 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5441457301742297", - "MAPE": "0.1796", - "MASE": "0.7009", - "RMSE": "0.6418882311790933" + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5551666232747684", + "MAPE": "0.1765", + "MASE": "0.7151", + "RMSE": "0.662953224721834" + } } } @@ -340,7 +575,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.995880736,"193":1.2189576591,"194":1.9036641905,"195":2.4575103444,"196":2.5728949598,"197":3.4728949598,"198":3.9652026521,"199":3.8805872675,"200":4.0036641905,"201":3.4937237523,"202":2.3520570856,"203":1.4270570856,"204":0.8517168408,"205":1.0747937639,"206":1.7591053258,"207":2.3129514796,"208":2.428336095,"209":3.328336095,"210":3.8206437873,"211":3.7360284027,"212":3.8591053258,"213":3.3491648875,"214":2.2074982208,"215":1.2824982208}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0787855545,"193":1.2246844538,"194":1.8134072137,"195":2.4134072137,"196":2.4904577641,"197":3.4134072137,"198":3.9134072137,"199":3.9904577641,"200":3.5904577641,"201":3.4411277814,"202":2.1517350041,"203":1.1402602793,"204":0.9346216593,"205":1.0805205586,"206":1.668848349,"207":2.268848349,"208":2.3458988993,"209":3.268848349,"210":3.768848349,"211":3.8458988993,"212":3.4458988993,"213":3.2965689166,"214":2.0071761394,"215":0.9957014145}} diff --git a/tests/references/neuralnet_test_ozone__GPU_theano.log b/tests/references/neuralnet_test_ozone__GPU_theano.log index bf34f1099..7844487d5 100644 --- a/tests/references/neuralnet_test_ozone__GPU_theano.log +++ b/tests/references/neuralnet_test_ozone__GPU_theano.log @@ -95,55 +95,103 @@ Using Theano backend. Using Theano backend. Using Theano backend. Using Theano backend. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 9.916084051132202 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 9.117366075515747 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -152,28 +200,37 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYe INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1865 MAPE_Forecast=0.1796 MAPE_Test=0.2567 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1767 SMAPE_Forecast=0.1922 SMAPE_Test=0.2534 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7009 MASE_Test=1.2245 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6949908566782953 L1_Forecast=0.5441457301742297 L1_Test=0.5788565919613983 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8963893991467234 L2_Forecast=0.6418882311790933 L2_Test=0.6786653138108626 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1761 MAPE_Forecast=0.1765 MAPE_Test=0.2209 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1712 SMAPE_Forecast=0.1941 SMAPE_Test=0.2249 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7728 MASE_Forecast=0.7151 MASE_Test=1.0918 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.679243722119012 L1_Forecast=0.5551666232747684 L1_Test=0.5161409468920186 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9118183531362629 L2_Forecast=0.662953224721834 L2_Test=0.5961504841809397 INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.012969125577120266 {1: -1.6355903804183052, 2: -1.4774474242490654, 3: -0.8776655161662057, 4: -0.2654214593143367, 5: -0.1765218216970199, 6: 0.7586716848135344, 7: 1.2705207720895366, 8: 1.359815379282721, 9: 0.9720594361345896, 10: 0.8345785406856634, 11: -0.442570179696129, 12: -1.4421958172494698} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 36.13044595718384 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.697596549987793 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.6069438457489 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.873598575592041 INFO:pyaf.std:START_TRAINING 'Ozone' Month Ozone Time 0 1955-01 2.7 1955-01-01 @@ -182,16 +239,11 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 - -[2 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 -2 None _Ozone ... 0.2001 0.9051 -3 None _Ozone ... 0.2001 0.9051 -4 None CumSum_Ozone ... 0.2144 0.9560 +0 None _Ozone ... 0.2209 1.0918 +1 None _Ozone ... 0.5734 2.8013 +2 None _Ozone ... 0.1221 0.6258 +3 None CumSum_Ozone ... 0.1962 0.9343 +4 None CumSum_Ozone ... 0.1962 0.9343 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -219,47 +271,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.851717 -205 1972-02-01 NaN 1.074794 -206 1972-03-01 NaN 1.759105 -207 1972-04-01 NaN 2.312951 -208 1972-05-01 NaN 2.428336 -209 1972-06-01 NaN 3.328336 -210 1972-07-01 NaN 3.820644 -211 1972-08-01 NaN 3.736028 -212 1972-09-01 NaN 3.859105 -213 1972-10-01 NaN 3.349165 -214 1972-11-01 NaN 2.207498 -215 1972-12-01 NaN 1.282498 +204 1972-01-01 NaN 0.934622 +205 1972-02-01 NaN 1.080521 +206 1972-03-01 NaN 1.668848 +207 1972-04-01 NaN 2.268848 +208 1972-05-01 NaN 2.345899 +209 1972-06-01 NaN 3.268848 +210 1972-07-01 NaN 3.768848 +211 1972-08-01 NaN 3.845899 +212 1972-09-01 NaN 3.445899 +213 1972-10-01 NaN 3.296569 +214 1972-11-01 NaN 2.007176 +215 1972-12-01 NaN 0.995701 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5441457301742297", - "MAPE": "0.1796", - "MASE": "0.7009", - "RMSE": "0.6418882311790933" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5551666232747684", + "MAPE": "0.1765", + "MASE": "0.7151", + "RMSE": "0.662953224721834" + } } } @@ -268,7 +322,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.995880736,"193":1.2189576591,"194":1.9036641905,"195":2.4575103444,"196":2.5728949598,"197":3.4728949598,"198":3.9652026521,"199":3.8805872675,"200":4.0036641905,"201":3.4937237523,"202":2.3520570856,"203":1.4270570856,"204":0.8517168408,"205":1.0747937639,"206":1.7591053258,"207":2.3129514796,"208":2.428336095,"209":3.328336095,"210":3.8206437873,"211":3.7360284027,"212":3.8591053258,"213":3.3491648875,"214":2.2074982208,"215":1.2824982208}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0787855545,"193":1.2246844538,"194":1.8134072137,"195":2.4134072137,"196":2.4904577641,"197":3.4134072137,"198":3.9134072137,"199":3.9904577641,"200":3.5904577641,"201":3.4411277814,"202":2.1517350041,"203":1.1402602793,"204":0.9346216593,"205":1.0805205586,"206":1.668848349,"207":2.268848349,"208":2.3458988993,"209":3.268848349,"210":3.768848349,"211":3.8458988993,"212":3.4458988993,"213":3.2965689166,"214":2.0071761394,"215":0.9957014145}} @@ -374,55 +428,103 @@ Using Theano backend. Using Theano backend. Using Theano backend. Using Theano backend. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.617425918579102 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.7891669273376465 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -431,29 +533,33 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYe INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1865 MAPE_Forecast=0.1796 MAPE_Test=0.2567 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1767 SMAPE_Forecast=0.1922 SMAPE_Test=0.2534 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7009 MASE_Test=1.2245 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6949908566782953 L1_Forecast=0.5441457301742297 L1_Test=0.5788565919613983 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8963893991467234 L2_Forecast=0.6418882311790933 L2_Test=0.6786653138108626 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1761 MAPE_Forecast=0.1765 MAPE_Test=0.2209 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1712 SMAPE_Forecast=0.1941 SMAPE_Test=0.2249 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7728 MASE_Forecast=0.7151 MASE_Test=1.0918 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.679243722119012 L1_Forecast=0.5551666232747684 L1_Test=0.5161409468920186 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9118183531362629 L2_Forecast=0.662953224721834 L2_Test=0.5961504841809397 INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.012969125577120266 {1: -1.6355903804183052, 2: -1.4774474242490654, 3: -0.8776655161662057, 4: -0.2654214593143367, 5: -0.1765218216970199, 6: 0.7586716848135344, 7: 1.2705207720895366, 8: 1.359815379282721, 9: 0.9720594361345896, 10: 0.8345785406856634, 11: -0.442570179696129, 12: -1.4421958172494698} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 13.091899156570435 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.665806770324707 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 - -[2 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 8.18468952178955 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.7309262752532959 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 -2 None _Ozone ... 0.2001 0.9051 -3 None _Ozone ... 0.2001 0.9051 -4 None CumSum_Ozone ... 0.2144 0.9560 +0 None _Ozone ... 0.2209 1.0918 +1 None _Ozone ... 0.5734 2.8013 +2 None _Ozone ... 0.1221 0.6258 +3 None CumSum_Ozone ... 0.1962 0.9343 +4 None CumSum_Ozone ... 0.1962 0.9343 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -481,47 +587,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.851717 -205 1972-02-01 NaN 1.074794 -206 1972-03-01 NaN 1.759105 -207 1972-04-01 NaN 2.312951 -208 1972-05-01 NaN 2.428336 -209 1972-06-01 NaN 3.328336 -210 1972-07-01 NaN 3.820644 -211 1972-08-01 NaN 3.736028 -212 1972-09-01 NaN 3.859105 -213 1972-10-01 NaN 3.349165 -214 1972-11-01 NaN 2.207498 -215 1972-12-01 NaN 1.282498 +204 1972-01-01 NaN 0.934622 +205 1972-02-01 NaN 1.080521 +206 1972-03-01 NaN 1.668848 +207 1972-04-01 NaN 2.268848 +208 1972-05-01 NaN 2.345899 +209 1972-06-01 NaN 3.268848 +210 1972-07-01 NaN 3.768848 +211 1972-08-01 NaN 3.845899 +212 1972-09-01 NaN 3.445899 +213 1972-10-01 NaN 3.296569 +214 1972-11-01 NaN 2.007176 +215 1972-12-01 NaN 0.995701 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5441457301742297", - "MAPE": "0.1796", - "MASE": "0.7009", - "RMSE": "0.6418882311790933" + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5551666232747684", + "MAPE": "0.1765", + "MASE": "0.7151", + "RMSE": "0.662953224721834" + } } } @@ -530,7 +638,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.995880736,"193":1.2189576591,"194":1.9036641905,"195":2.4575103444,"196":2.5728949598,"197":3.4728949598,"198":3.9652026521,"199":3.8805872675,"200":4.0036641905,"201":3.4937237523,"202":2.3520570856,"203":1.4270570856,"204":0.8517168408,"205":1.0747937639,"206":1.7591053258,"207":2.3129514796,"208":2.428336095,"209":3.328336095,"210":3.8206437873,"211":3.7360284027,"212":3.8591053258,"213":3.3491648875,"214":2.2074982208,"215":1.2824982208}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0787855545,"193":1.2246844538,"194":1.8134072137,"195":2.4134072137,"196":2.4904577641,"197":3.4134072137,"198":3.9134072137,"199":3.9904577641,"200":3.5904577641,"201":3.4411277814,"202":2.1517350041,"203":1.1402602793,"204":0.9346216593,"205":1.0805205586,"206":1.668848349,"207":2.268848349,"208":2.3458988993,"209":3.268848349,"210":3.768848349,"211":3.8458988993,"212":3.4458988993,"213":3.2965689166,"214":2.0071761394,"215":0.9957014145}} diff --git a/tests/references/neuralnet_test_ozone_rnn_only.log b/tests/references/neuralnet_test_ozone_rnn_only.log index c796dd0b8..f9b088539 100644 --- a/tests/references/neuralnet_test_ozone_rnn_only.log +++ b/tests/references/neuralnet_test_ozone_rnn_only.log @@ -13,55 +13,103 @@ Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 10.21243143081665 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 10.201696872711182 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -70,39 +118,43 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYe INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1865 MAPE_Forecast=0.1796 MAPE_Test=0.2567 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1767 SMAPE_Forecast=0.1922 SMAPE_Test=0.2534 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7009 MASE_Test=1.2245 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6949908566782953 L1_Forecast=0.5441457301742297 L1_Test=0.5788565919613983 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8963893991467234 L2_Forecast=0.6418882311790933 L2_Test=0.6786653138108626 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1761 MAPE_Forecast=0.1765 MAPE_Test=0.2209 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1712 SMAPE_Forecast=0.1941 SMAPE_Test=0.2249 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7728 MASE_Forecast=0.7151 MASE_Test=1.0918 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.679243722119012 L1_Forecast=0.5551666232747684 L1_Test=0.5161409468920186 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9118183531362629 L2_Forecast=0.662953224721834 L2_Test=0.5961504841809397 INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.012969125577120266 {1: -1.6355903804183052, 2: -1.4774474242490654, 3: -0.8776655161662057, 4: -0.2654214593143367, 5: -0.1765218216970199, 6: 0.7586716848135344, 7: 1.2705207720895366, 8: 1.359815379282721, 9: 0.9720594361345896, 10: 0.8345785406856634, 11: -0.442570179696129, 12: -1.4421958172494698} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 34.54066228866577 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.1412391662597656 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 - -[2 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.830084562301636 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.0565168857574463 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 -2 None _Ozone ... 0.2001 0.9051 -3 None _Ozone ... 0.2001 0.9051 -4 None CumSum_Ozone ... 0.2144 0.9560 +0 None _Ozone ... 0.2209 1.0918 +1 None _Ozone ... 0.5734 2.8013 +2 None _Ozone ... 0.1221 0.6258 +3 None CumSum_Ozone ... 0.1962 0.9343 +4 None CumSum_Ozone ... 0.1962 0.9343 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -130,47 +182,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.851717 -205 1972-02-01 NaN 1.074794 -206 1972-03-01 NaN 1.759105 -207 1972-04-01 NaN 2.312951 -208 1972-05-01 NaN 2.428336 -209 1972-06-01 NaN 3.328336 -210 1972-07-01 NaN 3.820644 -211 1972-08-01 NaN 3.736028 -212 1972-09-01 NaN 3.859105 -213 1972-10-01 NaN 3.349165 -214 1972-11-01 NaN 2.207498 -215 1972-12-01 NaN 1.282498 +204 1972-01-01 NaN 0.934622 +205 1972-02-01 NaN 1.080521 +206 1972-03-01 NaN 1.668848 +207 1972-04-01 NaN 2.268848 +208 1972-05-01 NaN 2.345899 +209 1972-06-01 NaN 3.268848 +210 1972-07-01 NaN 3.768848 +211 1972-08-01 NaN 3.845899 +212 1972-09-01 NaN 3.445899 +213 1972-10-01 NaN 3.296569 +214 1972-11-01 NaN 2.007176 +215 1972-12-01 NaN 0.995701 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5441457301742297", - "MAPE": "0.1796", - "MASE": "0.7009", - "RMSE": "0.6418882311790933" + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5551666232747684", + "MAPE": "0.1765", + "MASE": "0.7151", + "RMSE": "0.662953224721834" + } } } @@ -179,7 +233,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.995880736,"193":1.2189576591,"194":1.9036641905,"195":2.4575103444,"196":2.5728949598,"197":3.4728949598,"198":3.9652026521,"199":3.8805872675,"200":4.0036641905,"201":3.4937237523,"202":2.3520570856,"203":1.4270570856,"204":0.8517168408,"205":1.0747937639,"206":1.7591053258,"207":2.3129514796,"208":2.428336095,"209":3.328336095,"210":3.8206437873,"211":3.7360284027,"212":3.8591053258,"213":3.3491648875,"214":2.2074982208,"215":1.2824982208}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0787855545,"193":1.2246844538,"194":1.8134072137,"195":2.4134072137,"196":2.4904577641,"197":3.4134072137,"198":3.9134072137,"199":3.9904577641,"200":3.5904577641,"201":3.4411277814,"202":2.1517350041,"203":1.1402602793,"204":0.9346216593,"205":1.0805205586,"206":1.668848349,"207":2.268848349,"208":2.3458988993,"209":3.268848349,"210":3.768848349,"211":3.8458988993,"212":3.4458988993,"213":3.2965689166,"214":2.0071761394,"215":0.9957014145}} diff --git a/tests/references/neuralnet_test_ozone_rnn_only_LSTM.log b/tests/references/neuralnet_test_ozone_rnn_only_LSTM.log index 792ccf138..37ac4bd7f 100644 --- a/tests/references/neuralnet_test_ozone_rnn_only_LSTM.log +++ b/tests/references/neuralnet_test_ozone_rnn_only_LSTM.log @@ -13,55 +13,103 @@ Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 10.140774965286255 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 8.971206426620483 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -70,39 +118,43 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYe INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1865 MAPE_Forecast=0.1796 MAPE_Test=0.2567 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1767 SMAPE_Forecast=0.1922 SMAPE_Test=0.2534 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7009 MASE_Test=1.2245 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6949908566782953 L1_Forecast=0.5441457301742297 L1_Test=0.5788565919613983 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8963893991467234 L2_Forecast=0.6418882311790933 L2_Test=0.6786653138108626 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1761 MAPE_Forecast=0.1765 MAPE_Test=0.2209 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1712 SMAPE_Forecast=0.1941 SMAPE_Test=0.2249 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7728 MASE_Forecast=0.7151 MASE_Test=1.0918 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.679243722119012 L1_Forecast=0.5551666232747684 L1_Test=0.5161409468920186 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9118183531362629 L2_Forecast=0.662953224721834 L2_Test=0.5961504841809397 INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.012969125577120266 {1: -1.6355903804183052, 2: -1.4774474242490654, 3: -0.8776655161662057, 4: -0.2654214593143367, 5: -0.1765218216970199, 6: 0.7586716848135344, 7: 1.2705207720895366, 8: 1.359815379282721, 9: 0.9720594361345896, 10: 0.8345785406856634, 11: -0.442570179696129, 12: -1.4421958172494698} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 35.01141929626465 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.9820101261138916 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 23.153382301330566 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.3683485984802246 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 - -[2 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 -2 None _Ozone ... 0.2001 0.9051 -3 None _Ozone ... 0.2001 0.9051 -4 None CumSum_Ozone ... 0.2144 0.9560 +0 None _Ozone ... 0.2209 1.0918 +1 None _Ozone ... 0.5734 2.8013 +2 None _Ozone ... 0.1221 0.6258 +3 None CumSum_Ozone ... 0.1962 0.9343 +4 None CumSum_Ozone ... 0.1962 0.9343 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -130,47 +182,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.851717 -205 1972-02-01 NaN 1.074794 -206 1972-03-01 NaN 1.759105 -207 1972-04-01 NaN 2.312951 -208 1972-05-01 NaN 2.428336 -209 1972-06-01 NaN 3.328336 -210 1972-07-01 NaN 3.820644 -211 1972-08-01 NaN 3.736028 -212 1972-09-01 NaN 3.859105 -213 1972-10-01 NaN 3.349165 -214 1972-11-01 NaN 2.207498 -215 1972-12-01 NaN 1.282498 +204 1972-01-01 NaN 0.934622 +205 1972-02-01 NaN 1.080521 +206 1972-03-01 NaN 1.668848 +207 1972-04-01 NaN 2.268848 +208 1972-05-01 NaN 2.345899 +209 1972-06-01 NaN 3.268848 +210 1972-07-01 NaN 3.768848 +211 1972-08-01 NaN 3.845899 +212 1972-09-01 NaN 3.445899 +213 1972-10-01 NaN 3.296569 +214 1972-11-01 NaN 2.007176 +215 1972-12-01 NaN 0.995701 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5441457301742297", - "MAPE": "0.1796", - "MASE": "0.7009", - "RMSE": "0.6418882311790933" + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5551666232747684", + "MAPE": "0.1765", + "MASE": "0.7151", + "RMSE": "0.662953224721834" + } } } @@ -179,7 +233,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.995880736,"193":1.2189576591,"194":1.9036641905,"195":2.4575103444,"196":2.5728949598,"197":3.4728949598,"198":3.9652026521,"199":3.8805872675,"200":4.0036641905,"201":3.4937237523,"202":2.3520570856,"203":1.4270570856,"204":0.8517168408,"205":1.0747937639,"206":1.7591053258,"207":2.3129514796,"208":2.428336095,"209":3.328336095,"210":3.8206437873,"211":3.7360284027,"212":3.8591053258,"213":3.3491648875,"214":2.2074982208,"215":1.2824982208}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0787855545,"193":1.2246844538,"194":1.8134072137,"195":2.4134072137,"196":2.4904577641,"197":3.4134072137,"198":3.9134072137,"199":3.9904577641,"200":3.5904577641,"201":3.4411277814,"202":2.1517350041,"203":1.1402602793,"204":0.9346216593,"205":1.0805205586,"206":1.668848349,"207":2.268848349,"208":2.3458988993,"209":3.268848349,"210":3.768848349,"211":3.8458988993,"212":3.4458988993,"213":3.2965689166,"214":2.0071761394,"215":0.9957014145}} diff --git a/tests/references/neuralnet_test_ozone_rnn_only_MLP.log b/tests/references/neuralnet_test_ozone_rnn_only_MLP.log index 2ba4b8309..4ddaff346 100644 --- a/tests/references/neuralnet_test_ozone_rnn_only_MLP.log +++ b/tests/references/neuralnet_test_ozone_rnn_only_MLP.log @@ -13,55 +13,103 @@ Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 10.626964807510376 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 9.139781713485718 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -70,39 +118,43 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYe INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1865 MAPE_Forecast=0.1796 MAPE_Test=0.2567 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1767 SMAPE_Forecast=0.1922 SMAPE_Test=0.2534 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7907 MASE_Forecast=0.7009 MASE_Test=1.2245 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6949908566782953 L1_Forecast=0.5441457301742297 L1_Test=0.5788565919613983 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8963893991467234 L2_Forecast=0.6418882311790933 L2_Test=0.6786653138108626 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1761 MAPE_Forecast=0.1765 MAPE_Test=0.2209 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1712 SMAPE_Forecast=0.1941 SMAPE_Test=0.2249 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7728 MASE_Forecast=0.7151 MASE_Test=1.0918 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.679243722119012 L1_Forecast=0.5551666232747684 L1_Test=0.5161409468920186 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9118183531362629 L2_Forecast=0.662953224721834 L2_Test=0.5961504841809397 INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.012969125577120266 {1: -1.6355903804183052, 2: -1.4774474242490654, 3: -0.8776655161662057, 4: -0.2654214593143367, 5: -0.1765218216970199, 6: 0.7586716848135344, 7: 1.2705207720895366, 8: 1.359815379282721, 9: 0.9720594361345896, 10: 0.8345785406856634, 11: -0.442570179696129, 12: -1.4421958172494698} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 36.65963172912598 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.6685371398925781 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 23.64109778404236 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.6232447624206543 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 - -[2 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2567 1.2245 -1 None _Ozone ... 0.2567 1.2245 -2 None _Ozone ... 0.2001 0.9051 -3 None _Ozone ... 0.2001 0.9051 -4 None CumSum_Ozone ... 0.2144 0.9560 +0 None _Ozone ... 0.2209 1.0918 +1 None _Ozone ... 0.5734 2.8013 +2 None _Ozone ... 0.1221 0.6258 +3 None CumSum_Ozone ... 0.1962 0.9343 +4 None CumSum_Ozone ... 0.1962 0.9343 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -130,47 +182,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.851717 -205 1972-02-01 NaN 1.074794 -206 1972-03-01 NaN 1.759105 -207 1972-04-01 NaN 2.312951 -208 1972-05-01 NaN 2.428336 -209 1972-06-01 NaN 3.328336 -210 1972-07-01 NaN 3.820644 -211 1972-08-01 NaN 3.736028 -212 1972-09-01 NaN 3.859105 -213 1972-10-01 NaN 3.349165 -214 1972-11-01 NaN 2.207498 -215 1972-12-01 NaN 1.282498 +204 1972-01-01 NaN 0.934622 +205 1972-02-01 NaN 1.080521 +206 1972-03-01 NaN 1.668848 +207 1972-04-01 NaN 2.268848 +208 1972-05-01 NaN 2.345899 +209 1972-06-01 NaN 3.268848 +210 1972-07-01 NaN 3.768848 +211 1972-08-01 NaN 3.845899 +212 1972-09-01 NaN 3.445899 +213 1972-10-01 NaN 3.296569 +214 1972-11-01 NaN 2.007176 +215 1972-12-01 NaN 0.995701 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5441457301742297", - "MAPE": "0.1796", - "MASE": "0.7009", - "RMSE": "0.6418882311790933" + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5551666232747684", + "MAPE": "0.1765", + "MASE": "0.7151", + "RMSE": "0.662953224721834" + } } } @@ -179,7 +233,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.995880736,"193":1.2189576591,"194":1.9036641905,"195":2.4575103444,"196":2.5728949598,"197":3.4728949598,"198":3.9652026521,"199":3.8805872675,"200":4.0036641905,"201":3.4937237523,"202":2.3520570856,"203":1.4270570856,"204":0.8517168408,"205":1.0747937639,"206":1.7591053258,"207":2.3129514796,"208":2.428336095,"209":3.328336095,"210":3.8206437873,"211":3.7360284027,"212":3.8591053258,"213":3.3491648875,"214":2.2074982208,"215":1.2824982208}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0787855545,"193":1.2246844538,"194":1.8134072137,"195":2.4134072137,"196":2.4904577641,"197":3.4134072137,"198":3.9134072137,"199":3.9904577641,"200":3.5904577641,"201":3.4411277814,"202":2.1517350041,"203":1.1402602793,"204":0.9346216593,"205":1.0805205586,"206":1.668848349,"207":2.268848349,"208":2.3458988993,"209":3.268848349,"210":3.768848349,"211":3.8458988993,"212":3.4458988993,"213":3.2965689166,"214":2.0071761394,"215":0.9957014145}} diff --git a/tests/references/neuralnet_test_ozone_tensorflow.log b/tests/references/neuralnet_test_ozone_tensorflow.log index 0927d3efb..28cfd4ce2 100644 --- a/tests/references/neuralnet_test_ozone_tensorflow.log +++ b/tests/references/neuralnet_test_ozone_tensorflow.log @@ -1,15 +1,11 @@ WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. +Using TensorFlow backend. Month Ozone Time 0 1955-01 2.7 1955-01-01 1 1955-02 2.0 1955-02-01 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -Using TensorFlow backend. - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.8365 3.4569 - -[1 rows x 20 columns] Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.8365 3.4569 @@ -55,31 +51,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "1.2105245517010224", - "MAPE": "0.5344", - "MASE": "1.5593", - "RMSE": "1.426654943722196" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "1.2105245517010224", + "MAPE": "0.5344", + "MASE": "1.5593", + "RMSE": "1.426654943722196" + } } } diff --git a/tests/references/perf_test_cycles_full_long.log b/tests/references/perf_test_cycles_full_long.log index 159ec4417..d34bfb7b9 100644 --- a/tests/references/perf_test_cycles_full_long.log +++ b/tests/references/perf_test_cycles_full_long.log @@ -2,7 +2,7 @@ INFO:pyaf.std:START_TRAINING 'Signal' TEST_CYCLES_START 2 GENERATING_RANDOM_DATASET Signal_3200_D_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 20.00537109375 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 25.410181283950806 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2006-12-21T00:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3188 Min=1.0 Max=1.6918516806566242 Mean=1.3084905627071874 StdDev=0.09724607963907347 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.6918516806566242 Mean=1.3084905627071874 StdDev=0.09724607963907347 @@ -17,10 +17,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.705 MASE_Forecast=0.7245 MASE_Test=0.5487 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07787643938515318 L1_Forecast=0.07689438584701809 L1_Test=0.0890380041746075 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09747168892989223 L2_Forecast=0.09621695564368646 L2_Test=0.11867029187119144 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3095485336643529 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.6572098731994629 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.6596658229827881 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -54,31 +63,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2008-09-22 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2008-09-22 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 3188 }, - "Training_Signal_Length": 3188 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07689438584701809", - "MAPE": "0.0597", - "MASE": "0.7245", - "RMSE": "0.09621695564368646" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07689438584701809", + "MAPE": "0.0597", + "MASE": "0.7245", + "RMSE": "0.09621695564368646" + } } } @@ -95,32 +106,41 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 6 GENERATING_RANDOM_DATASET Signal_3200_D_0_constant_6_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 24.127174854278564 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 26.4510657787323 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2006-12-14T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3188 Min=1.0 Max=8.327669954926717 Mean=4.6510242046773405 StdDev=2.1545192781934377 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.327669954926717 Mean=4.6510242046773405 StdDev=2.1545192781934377 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0244 MAPE_Forecast=0.023 MAPE_Test=0.0216 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0243 SMAPE_Forecast=0.0229 SMAPE_Test=0.0214 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.024 MASE_Forecast=0.0231 MASE_Test=0.022 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07995556112124821 L1_Forecast=0.07695781156837461 L1_Test=0.07511872579772423 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10115467385011985 L2_Forecast=0.09597500888001685 L2_Test=0.09202584806122338 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0245 MAPE_Forecast=0.023 MAPE_Test=0.0225 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0244 SMAPE_Forecast=0.0229 SMAPE_Test=0.0223 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0241 MASE_Forecast=0.0231 MASE_Test=0.0227 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.08024722422502871 L1_Forecast=0.0767896196639691 L1_Test=0.07772593424271941 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10164905277197747 L2_Forecast=0.09611489577982618 L2_Test=0.09378606334201746 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.650905275759967 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 6 0.003484160563571592 {0: 0.010020599631053173, 1: -3.3317234003141785, 2: -0.004461678565777838, 3: 3.333732373170897, 4: -1.6640151703030175, 5: 1.6571302344308636} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.1663281917572021 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.232421875 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -139,47 +159,49 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 75.1 KB None Forecasts - [[Timestamp('2008-09-23 00:00:00') nan 4.627980539992525] - [Timestamp('2008-09-24 00:00:00') nan 7.987742767022709] - [Timestamp('2008-09-25 00:00:00') nan 2.975312191069686] - [Timestamp('2008-09-26 00:00:00') nan 6.307477309780987] - [Timestamp('2008-09-27 00:00:00') nan 4.645230791804538] - [Timestamp('2008-09-28 00:00:00') nan 1.3121037797992523] - [Timestamp('2008-09-29 00:00:00') nan 4.659212309250135] - [Timestamp('2008-09-30 00:00:00') nan 8.000959921442321] - [Timestamp('2008-10-01 00:00:00') nan 2.995858679441651] - [Timestamp('2008-10-02 00:00:00') nan 6.312619854445119] - [Timestamp('2008-10-03 00:00:00') nan 4.660593994407987] - [Timestamp('2008-10-04 00:00:00') nan 1.317221944486544]] + [[Timestamp('2008-09-23 00:00:00') nan 4.646443597194189] + [Timestamp('2008-09-24 00:00:00') nan 7.984637648930864] + [Timestamp('2008-09-25 00:00:00') nan 2.986890105456949] + [Timestamp('2008-09-26 00:00:00') nan 6.30803551019083] + [Timestamp('2008-09-27 00:00:00') nan 4.66092587539102] + [Timestamp('2008-09-28 00:00:00') nan 1.3191818754457882] + [Timestamp('2008-09-29 00:00:00') nan 4.646443597194189] + [Timestamp('2008-09-30 00:00:00') nan 7.984637648930864] + [Timestamp('2008-10-01 00:00:00') nan 2.986890105456949] + [Timestamp('2008-10-02 00:00:00') nan 6.30803551019083] + [Timestamp('2008-10-03 00:00:00') nan 4.66092587539102] + [Timestamp('2008-10-04 00:00:00') nan 1.3191818754457882]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2008-09-22 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2008-09-22 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 3188 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 3188 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.07695781156837461", - "MAPE": "0.023", - "MASE": "0.0231", - "RMSE": "0.09597500888001685" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.0767896196639691", + "MAPE": "0.023", + "MASE": "0.0231", + "RMSE": "0.09611489577982618" + } } } @@ -188,7 +210,7 @@ Forecasts -{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null},"Signal_Forecast":{"3188":4.62798054,"3189":7.987742767,"3190":2.9753121911,"3191":6.3074773098,"3192":4.6452307918,"3193":1.3121037798,"3194":4.6592123093,"3195":8.0009599214,"3196":2.9958586794,"3197":6.3126198544,"3198":4.6605939944,"3199":1.3172219445}} +{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null},"Signal_Forecast":{"3188":4.6464435972,"3189":7.9846376489,"3190":2.9868901055,"3191":6.3080355102,"3192":4.6609258754,"3193":1.3191818754,"3194":4.6464435972,"3195":7.9846376489,"3196":2.9868901055,"3197":6.3080355102,"3198":4.6609258754,"3199":1.3191818754}} @@ -196,32 +218,41 @@ TEST_CYCLES_END 6 TEST_CYCLES_START 10 GENERATING_RANDOM_DATASET Signal_3200_D_0_constant_10_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 16.48681402206421 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 22.111087560653687 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2006-12-08T00:00:00.000000 TimeDelta= Horizon=20 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3188 Min=1.0033963819081042 Max=8.550429886853314 Mean=4.653333789489017 StdDev=2.2480813664442665 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0033963819081042 Max=8.550429886853314 Mean=4.653333789489017 StdDev=2.2480813664442665 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0236 MAPE_Forecast=0.0231 MAPE_Test=0.0285 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0235 SMAPE_Forecast=0.023 SMAPE_Test=0.0286 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0257 MASE_Forecast=0.0256 MASE_Test=0.0304 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07720277331480584 L1_Forecast=0.07688436808872283 L1_Test=0.09656876494152662 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09677158283200568 L2_Forecast=0.09572654854089305 L2_Test=0.11470262435583392 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0236 MAPE_Forecast=0.0232 MAPE_Test=0.0278 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0236 SMAPE_Forecast=0.0231 SMAPE_Test=0.028 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0259 MASE_Forecast=0.0257 MASE_Test=0.03 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07760648080040061 L1_Forecast=0.07715233015040658 L1_Test=0.09509647850796588 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.0973444082102933 L2_Forecast=0.09618141209854945 L2_Test=0.11401593492803022 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.655327106168687 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 10 0.07688459421341554 {0: -2.3991625852232903, 1: 1.607598693409534, 2: 3.5956784199520913, 3: -2.4025408195287943, 4: -0.39379969783698243, 5: -3.4112890886011957, 6: -1.4076520959343037, 7: 1.610788353851678, 8: 0.6006152347961402, 9: 2.5996133006827913} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.172985315322876 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.383918046951294 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -240,55 +271,57 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 75.3 KB None Forecasts - [[Timestamp('2008-09-23 00:00:00') nan 5.263884886848744] - [Timestamp('2008-09-24 00:00:00') nan 7.251650163412713] - [Timestamp('2008-09-25 00:00:00') nan 2.2606036003178285] - [Timestamp('2008-09-26 00:00:00') nan 6.237163819209922] - [Timestamp('2008-09-27 00:00:00') nan 8.241128550168376] - [Timestamp('2008-09-28 00:00:00') nan 2.26474562522178] - [Timestamp('2008-09-29 00:00:00') nan 4.274452731675141] - [Timestamp('2008-09-30 00:00:00') nan 1.2483309986592936] - [Timestamp('2008-10-01 00:00:00') nan 3.2453518654305142] - [Timestamp('2008-10-02 00:00:00') nan 6.264183802392768] - [Timestamp('2008-10-03 00:00:00') nan 5.2570191893682345] - [Timestamp('2008-10-04 00:00:00') nan 7.256481043451538] - [Timestamp('2008-10-05 00:00:00') nan 2.258751462902122] - [Timestamp('2008-10-06 00:00:00') nan 6.282477435342348] - [Timestamp('2008-10-07 00:00:00') nan 8.259945198540183] - [Timestamp('2008-10-08 00:00:00') nan 2.2403624239089472] - [Timestamp('2008-10-09 00:00:00') nan 4.254355930224932] - [Timestamp('2008-10-10 00:00:00') nan 1.2545358554314667] - [Timestamp('2008-10-11 00:00:00') nan 3.2580318504800134] - [Timestamp('2008-10-12 00:00:00') nan 6.24190837305313]] + [[Timestamp('2008-09-23 00:00:00') nan 5.255942340964827] + [Timestamp('2008-09-24 00:00:00') nan 7.254940406851478] + [Timestamp('2008-09-25 00:00:00') nan 2.2561645209453967] + [Timestamp('2008-09-26 00:00:00') nan 6.2629257995782215] + [Timestamp('2008-09-27 00:00:00') nan 8.251005526120778] + [Timestamp('2008-09-28 00:00:00') nan 2.2527862866398927] + [Timestamp('2008-09-29 00:00:00') nan 4.2615274083317045] + [Timestamp('2008-09-30 00:00:00') nan 1.2440380175674912] + [Timestamp('2008-10-01 00:00:00') nan 3.247675010234383] + [Timestamp('2008-10-02 00:00:00') nan 6.266115460020365] + [Timestamp('2008-10-03 00:00:00') nan 5.255942340964827] + [Timestamp('2008-10-04 00:00:00') nan 7.254940406851478] + [Timestamp('2008-10-05 00:00:00') nan 2.2561645209453967] + [Timestamp('2008-10-06 00:00:00') nan 6.2629257995782215] + [Timestamp('2008-10-07 00:00:00') nan 8.251005526120778] + [Timestamp('2008-10-08 00:00:00') nan 2.2527862866398927] + [Timestamp('2008-10-09 00:00:00') nan 4.2615274083317045] + [Timestamp('2008-10-10 00:00:00') nan 1.2440380175674912] + [Timestamp('2008-10-11 00:00:00') nan 3.247675010234383] + [Timestamp('2008-10-12 00:00:00') nan 6.266115460020365]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 20, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2008-09-22 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 20, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2008-09-22 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 3188 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 3188 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.07688436808872283", - "MAPE": "0.0231", - "MASE": "0.0256", - "RMSE": "0.09572654854089305" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.07715233015040658", + "MAPE": "0.0232", + "MASE": "0.0257", + "RMSE": "0.09618141209854945" + } } } @@ -297,7 +330,7 @@ Forecasts -{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z","3200":"2008-10-05T00:00:00.000Z","3201":"2008-10-06T00:00:00.000Z","3202":"2008-10-07T00:00:00.000Z","3203":"2008-10-08T00:00:00.000Z","3204":"2008-10-09T00:00:00.000Z","3205":"2008-10-10T00:00:00.000Z","3206":"2008-10-11T00:00:00.000Z","3207":"2008-10-12T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null,"3200":null,"3201":null,"3202":null,"3203":null,"3204":null,"3205":null,"3206":null,"3207":null},"Signal_Forecast":{"3188":5.2638848868,"3189":7.2516501634,"3190":2.2606036003,"3191":6.2371638192,"3192":8.2411285502,"3193":2.2647456252,"3194":4.2744527317,"3195":1.2483309987,"3196":3.2453518654,"3197":6.2641838024,"3198":5.2570191894,"3199":7.2564810435,"3200":2.2587514629,"3201":6.2824774353,"3202":8.2599451985,"3203":2.2403624239,"3204":4.2543559302,"3205":1.2545358554,"3206":3.2580318505,"3207":6.2419083731}} +{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z","3200":"2008-10-05T00:00:00.000Z","3201":"2008-10-06T00:00:00.000Z","3202":"2008-10-07T00:00:00.000Z","3203":"2008-10-08T00:00:00.000Z","3204":"2008-10-09T00:00:00.000Z","3205":"2008-10-10T00:00:00.000Z","3206":"2008-10-11T00:00:00.000Z","3207":"2008-10-12T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null,"3200":null,"3201":null,"3202":null,"3203":null,"3204":null,"3205":null,"3206":null,"3207":null},"Signal_Forecast":{"3188":5.255942341,"3189":7.2549404069,"3190":2.2561645209,"3191":6.2629257996,"3192":8.2510055261,"3193":2.2527862866,"3194":4.2615274083,"3195":1.2440380176,"3196":3.2476750102,"3197":6.26611546,"3198":5.255942341,"3199":7.2549404069,"3200":2.2561645209,"3201":6.2629257996,"3202":8.2510055261,"3203":2.2527862866,"3204":4.2615274083,"3205":1.2440380176,"3206":3.2476750102,"3207":6.26611546}} @@ -305,32 +338,41 @@ TEST_CYCLES_END 10 TEST_CYCLES_START 14 GENERATING_RANDOM_DATASET Signal_3200_D_0_constant_14_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 21.479366540908813 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 26.326515197753906 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2006-12-02T00:00:00.000000 TimeDelta= Horizon=28 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3188 Min=1.0 Max=9.386998338541824 Mean=4.88329535745014 StdDev=2.2186411120039975 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=9.386998338541824 Mean=4.88329535745014 StdDev=2.2186411120039975 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0215 MAPE_Forecast=0.021 MAPE_Test=0.0224 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0215 SMAPE_Forecast=0.021 SMAPE_Test=0.0224 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.036 MASE_Forecast=0.0357 MASE_Test=0.0386 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07750244038248051 L1_Forecast=0.07656763535805101 L1_Test=0.08616539356505705 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.0967668022921602 L2_Forecast=0.09600460249012474 L2_Test=0.10620419465359497 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0216 MAPE_Forecast=0.0212 MAPE_Test=0.0226 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0215 SMAPE_Forecast=0.0211 SMAPE_Test=0.0226 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0361 MASE_Forecast=0.0359 MASE_Test=0.0385 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07774202198179155 L1_Forecast=0.07696621408769182 L1_Test=0.08588318646118744 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09743699899646677 L2_Forecast=0.09620513405170983 L2_Test=0.1059257415588015 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.883228376645396 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 14 0.2471220453733567 {0: -2.1920806509401105, 1: 0.6658608274874753, 2: 2.0951334766023413, 3: -2.19891007774077, 4: -0.7567108717955513, 5: -2.90051870264341, 6: -1.4708326510321101, 7: 0.6596254887136439, 8: -0.05929594420608719, 9: 1.3793823091655582, 10: 1.3766365776942844, 11: 4.237784053740896, 12: 2.8098448570379837, 13: -3.6132177628557294} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.5846459865570068 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.8960411548614502 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -349,63 +391,65 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 75.5 KB None Forecasts - [[Timestamp('2008-09-23 00:00:00') nan 6.257525021857456] - [Timestamp('2008-09-24 00:00:00') nan 9.127708400848697] - [Timestamp('2008-09-25 00:00:00') nan 7.673744395159904] - [Timestamp('2008-09-26 00:00:00') nan 1.2620671231259046] - [Timestamp('2008-09-27 00:00:00') nan 2.6929226282505794] - [Timestamp('2008-09-28 00:00:00') nan 5.558541789420289] - [Timestamp('2008-09-29 00:00:00') nan 6.974168231049931] - [Timestamp('2008-09-30 00:00:00') nan 2.6946321334089656] - [Timestamp('2008-10-01 00:00:00') nan 4.110149138693718] - [Timestamp('2008-10-02 00:00:00') nan 1.9893286491127262] - [Timestamp('2008-10-03 00:00:00') nan 3.417778333791437] - [Timestamp('2008-10-04 00:00:00') nan 5.546939968751736] - [Timestamp('2008-10-05 00:00:00') nan 4.814700250013071] - [Timestamp('2008-10-06 00:00:00') nan 6.263121922310072] - [Timestamp('2008-10-07 00:00:00') nan 6.261880908489239] - [Timestamp('2008-10-08 00:00:00') nan 9.111499294295045] - [Timestamp('2008-10-09 00:00:00') nan 7.703355368384564] - [Timestamp('2008-10-10 00:00:00') nan 1.2644230092210669] - [Timestamp('2008-10-11 00:00:00') nan 2.6871641613029165] - [Timestamp('2008-10-12 00:00:00') nan 5.525407722553604] - [Timestamp('2008-10-13 00:00:00') nan 6.990141191064593] - [Timestamp('2008-10-14 00:00:00') nan 2.6929087936086304] - [Timestamp('2008-10-15 00:00:00') nan 4.12289326116896] - [Timestamp('2008-10-16 00:00:00') nan 1.9818030179600745] - [Timestamp('2008-10-17 00:00:00') nan 3.4121994721382] - [Timestamp('2008-10-18 00:00:00') nan 5.523901577311456] - [Timestamp('2008-10-19 00:00:00') nan 4.835976321360585] - [Timestamp('2008-10-20 00:00:00') nan 6.273445086559055]] + [[Timestamp('2008-09-23 00:00:00') nan 6.25986495433968] + [Timestamp('2008-09-24 00:00:00') nan 9.12101243038629] + [Timestamp('2008-09-25 00:00:00') nan 7.693073233683379] + [Timestamp('2008-09-26 00:00:00') nan 1.2700106137896663] + [Timestamp('2008-09-27 00:00:00') nan 2.691147725705285] + [Timestamp('2008-09-28 00:00:00') nan 5.549089204132871] + [Timestamp('2008-09-29 00:00:00') nan 6.978361853247737] + [Timestamp('2008-09-30 00:00:00') nan 2.6843182989046257] + [Timestamp('2008-10-01 00:00:00') nan 4.126517504849844] + [Timestamp('2008-10-02 00:00:00') nan 1.9827096740019856] + [Timestamp('2008-10-03 00:00:00') nan 3.4123957256132855] + [Timestamp('2008-10-04 00:00:00') nan 5.54285386535904] + [Timestamp('2008-10-05 00:00:00') nan 4.8239324324393085] + [Timestamp('2008-10-06 00:00:00') nan 6.262610685810953] + [Timestamp('2008-10-07 00:00:00') nan 6.25986495433968] + [Timestamp('2008-10-08 00:00:00') nan 9.12101243038629] + [Timestamp('2008-10-09 00:00:00') nan 7.693073233683379] + [Timestamp('2008-10-10 00:00:00') nan 1.2700106137896663] + [Timestamp('2008-10-11 00:00:00') nan 2.691147725705285] + [Timestamp('2008-10-12 00:00:00') nan 5.549089204132871] + [Timestamp('2008-10-13 00:00:00') nan 6.978361853247737] + [Timestamp('2008-10-14 00:00:00') nan 2.6843182989046257] + [Timestamp('2008-10-15 00:00:00') nan 4.126517504849844] + [Timestamp('2008-10-16 00:00:00') nan 1.9827096740019856] + [Timestamp('2008-10-17 00:00:00') nan 3.4123957256132855] + [Timestamp('2008-10-18 00:00:00') nan 5.54285386535904] + [Timestamp('2008-10-19 00:00:00') nan 4.8239324324393085] + [Timestamp('2008-10-20 00:00:00') nan 6.262610685810953]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 28, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2008-09-22 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 28, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2008-09-22 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 3188 }, - "Training_Signal_Length": 3188 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.07656763535805101", - "MAPE": "0.021", - "MASE": "0.0357", - "RMSE": "0.09600460249012474" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.07696621408769182", + "MAPE": "0.0212", + "MASE": "0.0359", + "RMSE": "0.09620513405170983" + } } } @@ -414,7 +458,7 @@ Forecasts -{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z","3200":"2008-10-05T00:00:00.000Z","3201":"2008-10-06T00:00:00.000Z","3202":"2008-10-07T00:00:00.000Z","3203":"2008-10-08T00:00:00.000Z","3204":"2008-10-09T00:00:00.000Z","3205":"2008-10-10T00:00:00.000Z","3206":"2008-10-11T00:00:00.000Z","3207":"2008-10-12T00:00:00.000Z","3208":"2008-10-13T00:00:00.000Z","3209":"2008-10-14T00:00:00.000Z","3210":"2008-10-15T00:00:00.000Z","3211":"2008-10-16T00:00:00.000Z","3212":"2008-10-17T00:00:00.000Z","3213":"2008-10-18T00:00:00.000Z","3214":"2008-10-19T00:00:00.000Z","3215":"2008-10-20T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null,"3200":null,"3201":null,"3202":null,"3203":null,"3204":null,"3205":null,"3206":null,"3207":null,"3208":null,"3209":null,"3210":null,"3211":null,"3212":null,"3213":null,"3214":null,"3215":null},"Signal_Forecast":{"3188":6.2575250219,"3189":9.1277084008,"3190":7.6737443952,"3191":1.2620671231,"3192":2.6929226283,"3193":5.5585417894,"3194":6.974168231,"3195":2.6946321334,"3196":4.1101491387,"3197":1.9893286491,"3198":3.4177783338,"3199":5.5469399688,"3200":4.81470025,"3201":6.2631219223,"3202":6.2618809085,"3203":9.1114992943,"3204":7.7033553684,"3205":1.2644230092,"3206":2.6871641613,"3207":5.5254077226,"3208":6.9901411911,"3209":2.6929087936,"3210":4.1228932612,"3211":1.981803018,"3212":3.4121994721,"3213":5.5239015773,"3214":4.8359763214,"3215":6.2734450866}} +{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z","3200":"2008-10-05T00:00:00.000Z","3201":"2008-10-06T00:00:00.000Z","3202":"2008-10-07T00:00:00.000Z","3203":"2008-10-08T00:00:00.000Z","3204":"2008-10-09T00:00:00.000Z","3205":"2008-10-10T00:00:00.000Z","3206":"2008-10-11T00:00:00.000Z","3207":"2008-10-12T00:00:00.000Z","3208":"2008-10-13T00:00:00.000Z","3209":"2008-10-14T00:00:00.000Z","3210":"2008-10-15T00:00:00.000Z","3211":"2008-10-16T00:00:00.000Z","3212":"2008-10-17T00:00:00.000Z","3213":"2008-10-18T00:00:00.000Z","3214":"2008-10-19T00:00:00.000Z","3215":"2008-10-20T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null,"3200":null,"3201":null,"3202":null,"3203":null,"3204":null,"3205":null,"3206":null,"3207":null,"3208":null,"3209":null,"3210":null,"3211":null,"3212":null,"3213":null,"3214":null,"3215":null},"Signal_Forecast":{"3188":6.2598649543,"3189":9.1210124304,"3190":7.6930732337,"3191":1.2700106138,"3192":2.6911477257,"3193":5.5490892041,"3194":6.9783618532,"3195":2.6843182989,"3196":4.1265175048,"3197":1.982709674,"3198":3.4123957256,"3199":5.5428538654,"3200":4.8239324324,"3201":6.2626106858,"3202":6.2598649543,"3203":9.1210124304,"3204":7.6930732337,"3205":1.2700106138,"3206":2.6911477257,"3207":5.5490892041,"3208":6.9783618532,"3209":2.6843182989,"3210":4.1265175048,"3211":1.982709674,"3212":3.4123957256,"3213":5.5428538654,"3214":4.8239324324,"3215":6.2626106858}} @@ -422,32 +466,41 @@ TEST_CYCLES_END 14 TEST_CYCLES_START 18 GENERATING_RANDOM_DATASET Signal_3200_D_0_constant_18_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 20.188627004623413 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 24.647088766098022 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2006-11-25T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3188 Min=1.0 Max=10.576427076945718 Mean=5.6305033936318045 StdDev=2.5582966999257186 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.576427076945718 Mean=5.6305033936318045 StdDev=2.5582966999257186 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0184 MAPE_Forecast=0.0184 MAPE_Test=0.0185 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0184 SMAPE_Forecast=0.0183 SMAPE_Test=0.0184 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0226 MASE_Forecast=0.0228 MASE_Test=0.0221 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07822780988820052 L1_Forecast=0.07873870398852265 L1_Test=0.07895604672641972 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09781074568885911 L2_Forecast=0.09778874891626249 L2_Test=0.10152747691756217 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0184 MAPE_Forecast=0.0184 MAPE_Test=0.0193 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0184 SMAPE_Forecast=0.0183 SMAPE_Test=0.0191 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0226 MASE_Forecast=0.0229 MASE_Test=0.0231 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07823341660279452 L1_Forecast=0.07894995519032036 L1_Test=0.08273170542775829 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09832272885859455 L2_Forecast=0.0979083457914809 L2_Test=0.10567795277909689 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5.631224949660151 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 18 -0.708151278087958 {0: -3.171365959620535, 1: -0.9533336308126721, 2: 0.1566504446930228, 3: -2.6127328477701637, 4: -1.5260316657352182, 5: 1.8127599823977838, 6: -4.2895007429863, 7: -1.5212244806229016, 8: -0.9474896099607113, 9: 2.925379111487535, 10: 4.602851518608914, 11: -2.0607154624765522, 12: 2.386250992754503, 13: -0.3997420413092492, 14: 0.16854212666953394, 15: 4.045328672062976, 16: -2.049660590939836, 17: 3.4749457418070495} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.2839503288269043 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 2.3653249740600586 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -466,71 +519,73 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 75.7 KB None Forecasts - [[Timestamp('2008-09-23 00:00:00') nan 5.806179359785496] - [Timestamp('2008-09-24 00:00:00') nan 3.0044086227114994] - [Timestamp('2008-09-25 00:00:00') nan 4.108702650100903] - [Timestamp('2008-09-26 00:00:00') nan 7.4311436381459535] - [Timestamp('2008-09-27 00:00:00') nan 1.3581343816176075] - [Timestamp('2008-09-28 00:00:00') nan 4.115882497372725] - [Timestamp('2008-09-29 00:00:00') nan 4.6848636904841054] - [Timestamp('2008-09-30 00:00:00') nan 8.566945121079879] - [Timestamp('2008-10-01 00:00:00') nan 10.232742154001121] - [Timestamp('2008-10-02 00:00:00') nan 3.563888081807685] - [Timestamp('2008-10-03 00:00:00') nan 8.034817283174776] - [Timestamp('2008-10-04 00:00:00') nan 5.232421409504472] - [Timestamp('2008-10-05 00:00:00') nan 5.788982462686933] - [Timestamp('2008-10-06 00:00:00') nan 9.686159080288487] - [Timestamp('2008-10-07 00:00:00') nan 3.5717997249058526] - [Timestamp('2008-10-08 00:00:00') nan 9.115367356372925] - [Timestamp('2008-10-09 00:00:00') nan 2.482426732336247] - [Timestamp('2008-10-10 00:00:00') nan 4.675151319046721] - [Timestamp('2008-10-11 00:00:00') nan 5.780385009996802] - [Timestamp('2008-10-12 00:00:00') nan 3.015769496971012] - [Timestamp('2008-10-13 00:00:00') nan 4.109033849552386] - [Timestamp('2008-10-14 00:00:00') nan 7.4494831700383255] - [Timestamp('2008-10-15 00:00:00') nan 1.3444156717269067] - [Timestamp('2008-10-16 00:00:00') nan 4.100107631929082] - [Timestamp('2008-10-17 00:00:00') nan 4.6827141269500245] - [Timestamp('2008-10-18 00:00:00') nan 8.552843886522854] - [Timestamp('2008-10-19 00:00:00') nan 10.228679723959686] - [Timestamp('2008-10-20 00:00:00') nan 3.5577100582069385] - [Timestamp('2008-10-21 00:00:00') nan 8.008860257888403] - [Timestamp('2008-10-22 00:00:00') nan 5.223351753271663] - [Timestamp('2008-10-23 00:00:00') nan 5.786460006771854] - [Timestamp('2008-10-24 00:00:00') nan 9.668567302641854] - [Timestamp('2008-10-25 00:00:00') nan 3.569449280599662] - [Timestamp('2008-10-26 00:00:00') nan 9.106072259978383] - [Timestamp('2008-10-27 00:00:00') nan 2.443441264975164] - [Timestamp('2008-10-28 00:00:00') nan 4.681720702037099]] + [[Timestamp('2008-09-23 00:00:00') nan 5.787875394353174] + [Timestamp('2008-09-24 00:00:00') nan 3.0184921018899873] + [Timestamp('2008-09-25 00:00:00') nan 4.105193283924933] + [Timestamp('2008-09-26 00:00:00') nan 7.443984932057935] + [Timestamp('2008-09-27 00:00:00') nan 1.3417242066738506] + [Timestamp('2008-09-28 00:00:00') nan 4.110000469037249] + [Timestamp('2008-09-29 00:00:00') nan 4.68373533969944] + [Timestamp('2008-09-30 00:00:00') nan 8.556604061147686] + [Timestamp('2008-10-01 00:00:00') nan 10.234076468269066] + [Timestamp('2008-10-02 00:00:00') nan 3.5705094871835987] + [Timestamp('2008-10-03 00:00:00') nan 8.017475942414654] + [Timestamp('2008-10-04 00:00:00') nan 5.231482908350902] + [Timestamp('2008-10-05 00:00:00') nan 5.799767076329685] + [Timestamp('2008-10-06 00:00:00') nan 9.676553621723126] + [Timestamp('2008-10-07 00:00:00') nan 3.581564358720315] + [Timestamp('2008-10-08 00:00:00') nan 9.1061706914672] + [Timestamp('2008-10-09 00:00:00') nan 2.459858990039616] + [Timestamp('2008-10-10 00:00:00') nan 4.677891318847479] + [Timestamp('2008-10-11 00:00:00') nan 5.787875394353174] + [Timestamp('2008-10-12 00:00:00') nan 3.0184921018899873] + [Timestamp('2008-10-13 00:00:00') nan 4.105193283924933] + [Timestamp('2008-10-14 00:00:00') nan 7.443984932057935] + [Timestamp('2008-10-15 00:00:00') nan 1.3417242066738506] + [Timestamp('2008-10-16 00:00:00') nan 4.110000469037249] + [Timestamp('2008-10-17 00:00:00') nan 4.68373533969944] + [Timestamp('2008-10-18 00:00:00') nan 8.556604061147686] + [Timestamp('2008-10-19 00:00:00') nan 10.234076468269066] + [Timestamp('2008-10-20 00:00:00') nan 3.5705094871835987] + [Timestamp('2008-10-21 00:00:00') nan 8.017475942414654] + [Timestamp('2008-10-22 00:00:00') nan 5.231482908350902] + [Timestamp('2008-10-23 00:00:00') nan 5.799767076329685] + [Timestamp('2008-10-24 00:00:00') nan 9.676553621723126] + [Timestamp('2008-10-25 00:00:00') nan 3.581564358720315] + [Timestamp('2008-10-26 00:00:00') nan 9.1061706914672] + [Timestamp('2008-10-27 00:00:00') nan 2.459858990039616] + [Timestamp('2008-10-28 00:00:00') nan 4.677891318847479]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 36, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2008-09-22 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 36, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2008-09-22 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 3188 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 3188 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.07873870398852265", - "MAPE": "0.0184", - "MASE": "0.0228", - "RMSE": "0.09778874891626249" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.07894995519032036", + "MAPE": "0.0184", + "MASE": "0.0229", + "RMSE": "0.0979083457914809" + } } } @@ -539,7 +594,7 @@ Forecasts -{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z","3200":"2008-10-05T00:00:00.000Z","3201":"2008-10-06T00:00:00.000Z","3202":"2008-10-07T00:00:00.000Z","3203":"2008-10-08T00:00:00.000Z","3204":"2008-10-09T00:00:00.000Z","3205":"2008-10-10T00:00:00.000Z","3206":"2008-10-11T00:00:00.000Z","3207":"2008-10-12T00:00:00.000Z","3208":"2008-10-13T00:00:00.000Z","3209":"2008-10-14T00:00:00.000Z","3210":"2008-10-15T00:00:00.000Z","3211":"2008-10-16T00:00:00.000Z","3212":"2008-10-17T00:00:00.000Z","3213":"2008-10-18T00:00:00.000Z","3214":"2008-10-19T00:00:00.000Z","3215":"2008-10-20T00:00:00.000Z","3216":"2008-10-21T00:00:00.000Z","3217":"2008-10-22T00:00:00.000Z","3218":"2008-10-23T00:00:00.000Z","3219":"2008-10-24T00:00:00.000Z","3220":"2008-10-25T00:00:00.000Z","3221":"2008-10-26T00:00:00.000Z","3222":"2008-10-27T00:00:00.000Z","3223":"2008-10-28T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null,"3200":null,"3201":null,"3202":null,"3203":null,"3204":null,"3205":null,"3206":null,"3207":null,"3208":null,"3209":null,"3210":null,"3211":null,"3212":null,"3213":null,"3214":null,"3215":null,"3216":null,"3217":null,"3218":null,"3219":null,"3220":null,"3221":null,"3222":null,"3223":null},"Signal_Forecast":{"3188":5.8061793598,"3189":3.0044086227,"3190":4.1087026501,"3191":7.4311436381,"3192":1.3581343816,"3193":4.1158824974,"3194":4.6848636905,"3195":8.5669451211,"3196":10.232742154,"3197":3.5638880818,"3198":8.0348172832,"3199":5.2324214095,"3200":5.7889824627,"3201":9.6861590803,"3202":3.5717997249,"3203":9.1153673564,"3204":2.4824267323,"3205":4.675151319,"3206":5.78038501,"3207":3.015769497,"3208":4.1090338496,"3209":7.44948317,"3210":1.3444156717,"3211":4.1001076319,"3212":4.682714127,"3213":8.5528438865,"3214":10.228679724,"3215":3.5577100582,"3216":8.0088602579,"3217":5.2233517533,"3218":5.7864600068,"3219":9.6685673026,"3220":3.5694492806,"3221":9.10607226,"3222":2.443441265,"3223":4.681720702}} +{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z","3200":"2008-10-05T00:00:00.000Z","3201":"2008-10-06T00:00:00.000Z","3202":"2008-10-07T00:00:00.000Z","3203":"2008-10-08T00:00:00.000Z","3204":"2008-10-09T00:00:00.000Z","3205":"2008-10-10T00:00:00.000Z","3206":"2008-10-11T00:00:00.000Z","3207":"2008-10-12T00:00:00.000Z","3208":"2008-10-13T00:00:00.000Z","3209":"2008-10-14T00:00:00.000Z","3210":"2008-10-15T00:00:00.000Z","3211":"2008-10-16T00:00:00.000Z","3212":"2008-10-17T00:00:00.000Z","3213":"2008-10-18T00:00:00.000Z","3214":"2008-10-19T00:00:00.000Z","3215":"2008-10-20T00:00:00.000Z","3216":"2008-10-21T00:00:00.000Z","3217":"2008-10-22T00:00:00.000Z","3218":"2008-10-23T00:00:00.000Z","3219":"2008-10-24T00:00:00.000Z","3220":"2008-10-25T00:00:00.000Z","3221":"2008-10-26T00:00:00.000Z","3222":"2008-10-27T00:00:00.000Z","3223":"2008-10-28T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null,"3200":null,"3201":null,"3202":null,"3203":null,"3204":null,"3205":null,"3206":null,"3207":null,"3208":null,"3209":null,"3210":null,"3211":null,"3212":null,"3213":null,"3214":null,"3215":null,"3216":null,"3217":null,"3218":null,"3219":null,"3220":null,"3221":null,"3222":null,"3223":null},"Signal_Forecast":{"3188":5.7878753944,"3189":3.0184921019,"3190":4.1051932839,"3191":7.4439849321,"3192":1.3417242067,"3193":4.110000469,"3194":4.6837353397,"3195":8.5566040611,"3196":10.2340764683,"3197":3.5705094872,"3198":8.0174759424,"3199":5.2314829084,"3200":5.7997670763,"3201":9.6765536217,"3202":3.5815643587,"3203":9.1061706915,"3204":2.45985899,"3205":4.6778913188,"3206":5.7878753944,"3207":3.0184921019,"3208":4.1051932839,"3209":7.4439849321,"3210":1.3417242067,"3211":4.110000469,"3212":4.6837353397,"3213":8.5566040611,"3214":10.2340764683,"3215":3.5705094872,"3216":8.0174759424,"3217":5.2314829084,"3218":5.7997670763,"3219":9.6765536217,"3220":3.5815643587,"3221":9.1061706915,"3222":2.45985899,"3223":4.6778913188}} @@ -547,31 +602,40 @@ TEST_CYCLES_END 18 TEST_CYCLES_START 22 GENERATING_RANDOM_DATASET Signal_3200_D_0_constant_22_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 21.467981815338135 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 21.961642026901245 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2006-11-19T00:00:00.000000 TimeDelta= Horizon=44 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3188 Min=1.0 Max=10.6072690620626 Mean=6.035365559265394 StdDev=2.81170089800912 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.6072690620626 Mean=6.035365559265394 StdDev=2.81170089800912 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0182 MAPE_Forecast=0.0185 MAPE_Test=0.0172 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0182 SMAPE_Forecast=0.0186 SMAPE_Test=0.0172 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0299 MASE_Forecast=0.0299 MASE_Test=0.0285 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08035927682998005 L1_Forecast=0.08046165079884296 L1_Test=0.07660140958635667 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10058395866436214 L2_Forecast=0.09928341327668598 L2_Test=0.09488606854781868 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0182 MAPE_Forecast=0.0185 MAPE_Test=0.0174 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0181 SMAPE_Forecast=0.0186 SMAPE_Test=0.0174 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0298 MASE_Forecast=0.0299 MASE_Test=0.0287 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.08018373505592669 L1_Forecast=0.08043030142365945 L1_Test=0.07710201467289633 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10076172073005005 L2_Forecast=0.09918163458241755 L2_Test=0.0954729371789142 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.035892395324179 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 22 -0.8337871334798601 {0: -3.8565185103365085, 1: -2.043512848972768, 2: -1.1397564494944792, 3: 3.411901993395066, 4: 4.321764758929506, 5: 2.950249028400041, 6: -3.4026671446316312, 7: -2.496571497297764, 8: 0.23367614562734307, 9: -4.784501443187646, 10: -2.508881690842992, 11: -2.057749544419419, 12: 1.1322759579499762, 13: 2.490877245482375, 14: -2.9696880971880457, 15: 0.6986052974631343, 16: -1.5905526176874183, 17: -1.1429310301728437, 18: 3.8791019904668875, 19: 3.4153886861421334, 20: 2.041186309838279, 21: 3.4201797296292034} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.7269577980041504 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 3.4532644748687744 Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -590,79 +654,81 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 75.9 KB None Forecasts - [[Timestamp('2008-09-23 00:00:00') nan 8.075768360167942] - [Timestamp('2008-09-24 00:00:00') nan 9.442198516219904] - [Timestamp('2008-09-25 00:00:00') nan 2.1752258510207163] - [Timestamp('2008-09-26 00:00:00') nan 3.9903343961125572] - [Timestamp('2008-09-27 00:00:00') nan 4.899928284109783] - [Timestamp('2008-09-28 00:00:00') nan 9.457032054002124] - [Timestamp('2008-09-29 00:00:00') nan 10.352585214394006] - [Timestamp('2008-09-30 00:00:00') nan 8.982779101070935] - [Timestamp('2008-10-01 00:00:00') nan 2.6303587625585583] - [Timestamp('2008-10-02 00:00:00') nan 3.5348089927856456] - [Timestamp('2008-10-03 00:00:00') nan 6.265519864697333] - [Timestamp('2008-10-04 00:00:00') nan 1.245930323933739] - [Timestamp('2008-10-05 00:00:00') nan 3.532538332016488] - [Timestamp('2008-10-06 00:00:00') nan 3.9836872352346098] - [Timestamp('2008-10-07 00:00:00') nan 7.1728256994822654] - [Timestamp('2008-10-08 00:00:00') nan 8.5325182776875] - [Timestamp('2008-10-09 00:00:00') nan 3.078426408014989] - [Timestamp('2008-10-10 00:00:00') nan 6.732928136499495] - [Timestamp('2008-10-11 00:00:00') nan 4.443009740763331] - [Timestamp('2008-10-12 00:00:00') nan 4.897850370064029] - [Timestamp('2008-10-13 00:00:00') nan 9.90743434261275] - [Timestamp('2008-10-14 00:00:00') nan 9.453865513542883] - [Timestamp('2008-10-15 00:00:00') nan 8.075768360167942] - [Timestamp('2008-10-16 00:00:00') nan 9.442198516219904] - [Timestamp('2008-10-17 00:00:00') nan 2.1752258510207163] - [Timestamp('2008-10-18 00:00:00') nan 3.9903343961125572] - [Timestamp('2008-10-19 00:00:00') nan 4.899928284109783] - [Timestamp('2008-10-20 00:00:00') nan 9.457032054002124] - [Timestamp('2008-10-21 00:00:00') nan 10.352585214394006] - [Timestamp('2008-10-22 00:00:00') nan 8.982779101070935] - [Timestamp('2008-10-23 00:00:00') nan 2.6303587625585583] - [Timestamp('2008-10-24 00:00:00') nan 3.5348089927856456] - [Timestamp('2008-10-25 00:00:00') nan 6.265519864697333] - [Timestamp('2008-10-26 00:00:00') nan 1.245930323933739] - [Timestamp('2008-10-27 00:00:00') nan 3.532538332016488] - [Timestamp('2008-10-28 00:00:00') nan 3.9836872352346098] - [Timestamp('2008-10-29 00:00:00') nan 7.1728256994822654] - [Timestamp('2008-10-30 00:00:00') nan 8.5325182776875] - [Timestamp('2008-10-31 00:00:00') nan 3.078426408014989] - [Timestamp('2008-11-01 00:00:00') nan 6.732928136499495] - [Timestamp('2008-11-02 00:00:00') nan 4.443009740763331] - [Timestamp('2008-11-03 00:00:00') nan 4.897850370064029] - [Timestamp('2008-11-04 00:00:00') nan 9.90743434261275] - [Timestamp('2008-11-05 00:00:00') nan 9.453865513542883]] + [[Timestamp('2008-09-23 00:00:00') nan 8.077078705162458] + [Timestamp('2008-09-24 00:00:00') nan 9.456072124953383] + [Timestamp('2008-09-25 00:00:00') nan 2.1793738849876707] + [Timestamp('2008-09-26 00:00:00') nan 3.9923795463514113] + [Timestamp('2008-09-27 00:00:00') nan 4.8961359458297] + [Timestamp('2008-09-28 00:00:00') nan 9.447794388719245] + [Timestamp('2008-09-29 00:00:00') nan 10.357657154253685] + [Timestamp('2008-09-30 00:00:00') nan 8.98614142372422] + [Timestamp('2008-10-01 00:00:00') nan 2.633225250692548] + [Timestamp('2008-10-02 00:00:00') nan 3.539320898026415] + [Timestamp('2008-10-03 00:00:00') nan 6.269568540951522] + [Timestamp('2008-10-04 00:00:00') nan 1.251390952136533] + [Timestamp('2008-10-05 00:00:00') nan 3.5270107044811874] + [Timestamp('2008-10-06 00:00:00') nan 3.97814285090476] + [Timestamp('2008-10-07 00:00:00') nan 7.168168353274155] + [Timestamp('2008-10-08 00:00:00') nan 8.526769640806554] + [Timestamp('2008-10-09 00:00:00') nan 3.0662042981361335] + [Timestamp('2008-10-10 00:00:00') nan 6.734497692787313] + [Timestamp('2008-10-11 00:00:00') nan 4.445339777636761] + [Timestamp('2008-10-12 00:00:00') nan 4.8929613651513355] + [Timestamp('2008-10-13 00:00:00') nan 9.914994385791067] + [Timestamp('2008-10-14 00:00:00') nan 9.451281081466313] + [Timestamp('2008-10-15 00:00:00') nan 8.077078705162458] + [Timestamp('2008-10-16 00:00:00') nan 9.456072124953383] + [Timestamp('2008-10-17 00:00:00') nan 2.1793738849876707] + [Timestamp('2008-10-18 00:00:00') nan 3.9923795463514113] + [Timestamp('2008-10-19 00:00:00') nan 4.8961359458297] + [Timestamp('2008-10-20 00:00:00') nan 9.447794388719245] + [Timestamp('2008-10-21 00:00:00') nan 10.357657154253685] + [Timestamp('2008-10-22 00:00:00') nan 8.98614142372422] + [Timestamp('2008-10-23 00:00:00') nan 2.633225250692548] + [Timestamp('2008-10-24 00:00:00') nan 3.539320898026415] + [Timestamp('2008-10-25 00:00:00') nan 6.269568540951522] + [Timestamp('2008-10-26 00:00:00') nan 1.251390952136533] + [Timestamp('2008-10-27 00:00:00') nan 3.5270107044811874] + [Timestamp('2008-10-28 00:00:00') nan 3.97814285090476] + [Timestamp('2008-10-29 00:00:00') nan 7.168168353274155] + [Timestamp('2008-10-30 00:00:00') nan 8.526769640806554] + [Timestamp('2008-10-31 00:00:00') nan 3.0662042981361335] + [Timestamp('2008-11-01 00:00:00') nan 6.734497692787313] + [Timestamp('2008-11-02 00:00:00') nan 4.445339777636761] + [Timestamp('2008-11-03 00:00:00') nan 4.8929613651513355] + [Timestamp('2008-11-04 00:00:00') nan 9.914994385791067] + [Timestamp('2008-11-05 00:00:00') nan 9.451281081466313]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 44, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2008-09-22 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 44, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2008-09-22 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 3188 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 3188 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08046165079884296", - "MAPE": "0.0185", - "MASE": "0.0299", - "RMSE": "0.09928341327668598" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08043030142365945", + "MAPE": "0.0185", + "MASE": "0.0299", + "RMSE": "0.09918163458241755" + } } } @@ -671,7 +737,7 @@ Forecasts -{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z","3200":"2008-10-05T00:00:00.000Z","3201":"2008-10-06T00:00:00.000Z","3202":"2008-10-07T00:00:00.000Z","3203":"2008-10-08T00:00:00.000Z","3204":"2008-10-09T00:00:00.000Z","3205":"2008-10-10T00:00:00.000Z","3206":"2008-10-11T00:00:00.000Z","3207":"2008-10-12T00:00:00.000Z","3208":"2008-10-13T00:00:00.000Z","3209":"2008-10-14T00:00:00.000Z","3210":"2008-10-15T00:00:00.000Z","3211":"2008-10-16T00:00:00.000Z","3212":"2008-10-17T00:00:00.000Z","3213":"2008-10-18T00:00:00.000Z","3214":"2008-10-19T00:00:00.000Z","3215":"2008-10-20T00:00:00.000Z","3216":"2008-10-21T00:00:00.000Z","3217":"2008-10-22T00:00:00.000Z","3218":"2008-10-23T00:00:00.000Z","3219":"2008-10-24T00:00:00.000Z","3220":"2008-10-25T00:00:00.000Z","3221":"2008-10-26T00:00:00.000Z","3222":"2008-10-27T00:00:00.000Z","3223":"2008-10-28T00:00:00.000Z","3224":"2008-10-29T00:00:00.000Z","3225":"2008-10-30T00:00:00.000Z","3226":"2008-10-31T00:00:00.000Z","3227":"2008-11-01T00:00:00.000Z","3228":"2008-11-02T00:00:00.000Z","3229":"2008-11-03T00:00:00.000Z","3230":"2008-11-04T00:00:00.000Z","3231":"2008-11-05T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null,"3200":null,"3201":null,"3202":null,"3203":null,"3204":null,"3205":null,"3206":null,"3207":null,"3208":null,"3209":null,"3210":null,"3211":null,"3212":null,"3213":null,"3214":null,"3215":null,"3216":null,"3217":null,"3218":null,"3219":null,"3220":null,"3221":null,"3222":null,"3223":null,"3224":null,"3225":null,"3226":null,"3227":null,"3228":null,"3229":null,"3230":null,"3231":null},"Signal_Forecast":{"3188":8.0757683602,"3189":9.4421985162,"3190":2.175225851,"3191":3.9903343961,"3192":4.8999282841,"3193":9.457032054,"3194":10.3525852144,"3195":8.9827791011,"3196":2.6303587626,"3197":3.5348089928,"3198":6.2655198647,"3199":1.2459303239,"3200":3.532538332,"3201":3.9836872352,"3202":7.1728256995,"3203":8.5325182777,"3204":3.078426408,"3205":6.7329281365,"3206":4.4430097408,"3207":4.8978503701,"3208":9.9074343426,"3209":9.4538655135,"3210":8.0757683602,"3211":9.4421985162,"3212":2.175225851,"3213":3.9903343961,"3214":4.8999282841,"3215":9.457032054,"3216":10.3525852144,"3217":8.9827791011,"3218":2.6303587626,"3219":3.5348089928,"3220":6.2655198647,"3221":1.2459303239,"3222":3.532538332,"3223":3.9836872352,"3224":7.1728256995,"3225":8.5325182777,"3226":3.078426408,"3227":6.7329281365,"3228":4.4430097408,"3229":4.8978503701,"3230":9.9074343426,"3231":9.4538655135}} +{"Date":{"3188":"2008-09-23T00:00:00.000Z","3189":"2008-09-24T00:00:00.000Z","3190":"2008-09-25T00:00:00.000Z","3191":"2008-09-26T00:00:00.000Z","3192":"2008-09-27T00:00:00.000Z","3193":"2008-09-28T00:00:00.000Z","3194":"2008-09-29T00:00:00.000Z","3195":"2008-09-30T00:00:00.000Z","3196":"2008-10-01T00:00:00.000Z","3197":"2008-10-02T00:00:00.000Z","3198":"2008-10-03T00:00:00.000Z","3199":"2008-10-04T00:00:00.000Z","3200":"2008-10-05T00:00:00.000Z","3201":"2008-10-06T00:00:00.000Z","3202":"2008-10-07T00:00:00.000Z","3203":"2008-10-08T00:00:00.000Z","3204":"2008-10-09T00:00:00.000Z","3205":"2008-10-10T00:00:00.000Z","3206":"2008-10-11T00:00:00.000Z","3207":"2008-10-12T00:00:00.000Z","3208":"2008-10-13T00:00:00.000Z","3209":"2008-10-14T00:00:00.000Z","3210":"2008-10-15T00:00:00.000Z","3211":"2008-10-16T00:00:00.000Z","3212":"2008-10-17T00:00:00.000Z","3213":"2008-10-18T00:00:00.000Z","3214":"2008-10-19T00:00:00.000Z","3215":"2008-10-20T00:00:00.000Z","3216":"2008-10-21T00:00:00.000Z","3217":"2008-10-22T00:00:00.000Z","3218":"2008-10-23T00:00:00.000Z","3219":"2008-10-24T00:00:00.000Z","3220":"2008-10-25T00:00:00.000Z","3221":"2008-10-26T00:00:00.000Z","3222":"2008-10-27T00:00:00.000Z","3223":"2008-10-28T00:00:00.000Z","3224":"2008-10-29T00:00:00.000Z","3225":"2008-10-30T00:00:00.000Z","3226":"2008-10-31T00:00:00.000Z","3227":"2008-11-01T00:00:00.000Z","3228":"2008-11-02T00:00:00.000Z","3229":"2008-11-03T00:00:00.000Z","3230":"2008-11-04T00:00:00.000Z","3231":"2008-11-05T00:00:00.000Z"},"Signal":{"3188":null,"3189":null,"3190":null,"3191":null,"3192":null,"3193":null,"3194":null,"3195":null,"3196":null,"3197":null,"3198":null,"3199":null,"3200":null,"3201":null,"3202":null,"3203":null,"3204":null,"3205":null,"3206":null,"3207":null,"3208":null,"3209":null,"3210":null,"3211":null,"3212":null,"3213":null,"3214":null,"3215":null,"3216":null,"3217":null,"3218":null,"3219":null,"3220":null,"3221":null,"3222":null,"3223":null,"3224":null,"3225":null,"3226":null,"3227":null,"3228":null,"3229":null,"3230":null,"3231":null},"Signal_Forecast":{"3188":8.0770787052,"3189":9.456072125,"3190":2.179373885,"3191":3.9923795464,"3192":4.8961359458,"3193":9.4477943887,"3194":10.3576571543,"3195":8.9861414237,"3196":2.6332252507,"3197":3.539320898,"3198":6.269568541,"3199":1.2513909521,"3200":3.5270107045,"3201":3.9781428509,"3202":7.1681683533,"3203":8.5267696408,"3204":3.0662042981,"3205":6.7344976928,"3206":4.4453397776,"3207":4.8929613652,"3208":9.9149943858,"3209":9.4512810815,"3210":8.0770787052,"3211":9.456072125,"3212":2.179373885,"3213":3.9923795464,"3214":4.8961359458,"3215":9.4477943887,"3216":10.3576571543,"3217":8.9861414237,"3218":2.6332252507,"3219":3.539320898,"3220":6.269568541,"3221":1.2513909521,"3222":3.5270107045,"3223":3.9781428509,"3224":7.1681683533,"3225":8.5267696408,"3226":3.0662042981,"3227":6.7344976928,"3228":4.4453397776,"3229":4.8929613652,"3230":9.9149943858,"3231":9.4512810815}} diff --git a/tests/references/perf_test_cycles_full_long_long.log b/tests/references/perf_test_cycles_full_long_long.log index 41d863fef..1d9486215 100644 --- a/tests/references/perf_test_cycles_full_long_long.log +++ b/tests/references/perf_test_cycles_full_long_long.log @@ -2,7 +2,7 @@ INFO:pyaf.std:START_TRAINING 'Signal' TEST_CYCLES_START 2 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 58.30080986022949 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 110.21578073501587 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-02T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=1.8491743129800016 Mean=1.465622874891886 StdDev=0.09943794145139832 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.8491743129800016 Mean=1.465622874891886 StdDev=0.09943794145139832 @@ -17,10 +17,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7069 MASE_Forecast=0.7113 MASE_Test=0.6606 INFO:pyaf.std:MODEL_L1 L1_Fit=0.0788026742868748 L1_Forecast=0.08061744963149645 L1_Test=0.06906760489553276 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09905795697203003 L2_Forecast=0.10094976147793763 L2_Test=0.0891003145657369 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4657142713807132 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 5.996977090835571 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 5.727567434310913 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -54,31 +63,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.08061744963149645", - "MAPE": "0.0556", - "MASE": "0.7113", - "RMSE": "0.10094976147793763" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.08061744963149645", + "MAPE": "0.0556", + "MASE": "0.7113", + "RMSE": "0.10094976147793763" + } } } @@ -95,7 +106,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 6 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_6_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 52.6409707069397 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 76.19726920127869 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T19:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=8.333821360571514 Mean=4.6514340347620795 StdDev=2.1536083159682464 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.333821360571514 Mean=4.6514340347620795 StdDev=2.1536083159682464 @@ -104,16 +115,25 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_Hour_r INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour' [Seasonal_Hour] INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0242 MAPE_Forecast=0.0243 MAPE_Test=0.0297 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0241 SMAPE_Forecast=0.0242 SMAPE_Test=0.0296 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0241 MASE_Forecast=0.024 MASE_Test=0.0237 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08017385895829468 L1_Forecast=0.0800841636633722 L1_Test=0.0789298116593309 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10037169161740217 L2_Forecast=0.10034821933850727 L2_Test=0.09050151793877745 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0241 MAPE_Forecast=0.0243 MAPE_Test=0.03 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.024 SMAPE_Forecast=0.0242 SMAPE_Test=0.0299 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.024 MASE_Forecast=0.024 MASE_Test=0.0238 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.08014185956783082 L1_Forecast=0.08013380214010248 L1_Test=0.07923594759930998 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10041282577628087 L2_Forecast=0.10041392569948236 L2_Test=0.09087476936115621 INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.651309216902765 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_Hour 0.0005642027933792093 {0: 0.00112145507781225, 1: -3.3318496867926393, 2: -0.0018319620987155005, 3: 3.340988602011187, 4: -1.6749864737829494, 5: 1.6657038775346944, 6: 0.0022965144751019295, 7: -3.327901785222622, 8: -0.0025588018864155515, 9: 3.3283914344642094, 10: -1.666704273052028, 11: 1.6633980485526036, 12: 0.005034145528322931, 13: -3.3389129989187687, 14: -0.005518464672188195, 15: 3.335847325764391, 16: -1.6693022437105134, 17: 1.6671641840878966, 18: 0.0038605214739781957, 19: -3.326517819482879, 20: 0.0049681482250516495, 21: 3.327643434132467, 22: -1.6661372996024961, 23: 1.6586381732888729} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 7.92017674446106 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 6.876359701156616 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -139,47 +159,49 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.1 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 4.653405461010779] - [Timestamp('2003-08-25 21:00:00') nan 7.984028325237326] - [Timestamp('2003-08-25 22:00:00') nan 2.98533398180741] - [Timestamp('2003-08-25 23:00:00') nan 6.3149379222122395] - [Timestamp('2003-08-26 00:00:00') nan 4.6506634205867385] - [Timestamp('2003-08-26 01:00:00') nan 1.3170180001147713] - [Timestamp('2003-08-26 02:00:00') nan 4.649844652508211] - [Timestamp('2003-08-26 03:00:00') nan 7.987823896560785] - [Timestamp('2003-08-26 04:00:00') nan 2.980441699923875] - [Timestamp('2003-08-26 05:00:00') nan 6.317921298813156] - [Timestamp('2003-08-26 06:00:00') nan 4.651567705019989] - [Timestamp('2003-08-26 07:00:00') nan 1.3252349293702048]] + [[Timestamp('2003-08-25 20:00:00') nan 4.656277365127817] + [Timestamp('2003-08-25 21:00:00') nan 7.978952651035232] + [Timestamp('2003-08-25 22:00:00') nan 2.9851719173002693] + [Timestamp('2003-08-25 23:00:00') nan 6.309947390191638] + [Timestamp('2003-08-26 00:00:00') nan 4.652430671980578] + [Timestamp('2003-08-26 01:00:00') nan 1.3194595301101262] + [Timestamp('2003-08-26 02:00:00') nan 4.64947725480405] + [Timestamp('2003-08-26 03:00:00') nan 7.992297818913952] + [Timestamp('2003-08-26 04:00:00') nan 2.976322743119816] + [Timestamp('2003-08-26 05:00:00') nan 6.31701309443746] + [Timestamp('2003-08-26 06:00:00') nan 4.653605731377867] + [Timestamp('2003-08-26 07:00:00') nan 1.3234074316801436]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR", + "Cycle": "Seasonal_Hour", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR", - "Cycle": "Seasonal_Hour", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "0.0800841636633722", - "MAPE": "0.0243", - "MASE": "0.024", - "RMSE": "0.10034821933850727" + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.08013380214010248", + "MAPE": "0.0243", + "MASE": "0.024", + "RMSE": "0.10041392569948236" + } } } @@ -188,7 +210,7 @@ Forecasts -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null},"Signal_Forecast":{"31988":4.653405461,"31989":7.9840283252,"31990":2.9853339818,"31991":6.3149379222,"31992":4.6506634206,"31993":1.3170180001,"31994":4.6498446525,"31995":7.9878238966,"31996":2.9804416999,"31997":6.3179212988,"31998":4.651567705,"31999":1.3252349294}} +{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null},"Signal_Forecast":{"31988":4.6562773651,"31989":7.978952651,"31990":2.9851719173,"31991":6.3099473902,"31992":4.652430672,"31993":1.3194595301,"31994":4.6494772548,"31995":7.9922978189,"31996":2.9763227431,"31997":6.3170130944,"31998":4.6536057314,"31999":1.3234074317}} @@ -196,32 +218,41 @@ TEST_CYCLES_END 6 TEST_CYCLES_START 10 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_10_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 53.66698384284973 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 103.08982467651367 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T13:00:00.000000 TimeDelta= Horizon=20 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=8.700169238172657 Mean=4.751653298073094 StdDev=2.247677208865058 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.700169238172657 Mean=4.751653298073094 StdDev=2.247677208865058 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0228 MAPE_Forecast=0.0233 MAPE_Test=0.0221 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0227 SMAPE_Forecast=0.0232 SMAPE_Test=0.0218 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0263 MASE_Forecast=0.0269 MASE_Test=0.0243 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.078789925042696 L1_Forecast=0.08066801728649253 L1_Test=0.07576694082457418 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09903340370074636 L2_Forecast=0.10100139614354746 L2_Test=0.09544243176063513 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07878495938794243 L1_Forecast=0.08064743691160367 L1_Test=0.07585865642956542 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09903920885077316 L2_Forecast=0.10097896423433593 L2_Test=0.0954013346310342 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.751863564238395 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 10 0.1146752547368104 {0: -2.4013563201199983, 1: 1.602211122589586, 2: 3.599158544297013, 3: -2.398889422062649, 4: -0.40125388302339715, 5: -3.4021823361080994, 6: -1.4036668502047256, 7: 1.5970111896443058, 8: 0.6021353433222743, 9: 2.6017161805354796} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 7.543086290359497 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 7.690162420272827 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -240,55 +271,57 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.3 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 5.3558670995493065] - [Timestamp('2003-08-25 21:00:00') nan 7.351919707979566] - [Timestamp('2003-08-25 22:00:00') nan 2.35085274066433] - [Timestamp('2003-08-25 23:00:00') nan 6.3551729256245535] - [Timestamp('2003-08-26 00:00:00') nan 8.351547764631942] - [Timestamp('2003-08-26 01:00:00') nan 2.353923101918168] - [Timestamp('2003-08-26 02:00:00') nan 4.3502614448506804] - [Timestamp('2003-08-26 03:00:00') nan 1.3507617134694518] - [Timestamp('2003-08-26 04:00:00') nan 3.348091682713169] - [Timestamp('2003-08-26 05:00:00') nan 6.350079446795888] - [Timestamp('2003-08-26 06:00:00') nan 5.3558670995493065] - [Timestamp('2003-08-26 07:00:00') nan 7.351919707979566] - [Timestamp('2003-08-26 08:00:00') nan 2.35085274066433] - [Timestamp('2003-08-26 09:00:00') nan 6.3551729256245535] - [Timestamp('2003-08-26 10:00:00') nan 8.351547764631942] - [Timestamp('2003-08-26 11:00:00') nan 2.353923101918168] - [Timestamp('2003-08-26 12:00:00') nan 4.3502614448506804] - [Timestamp('2003-08-26 13:00:00') nan 1.3507617134694518] - [Timestamp('2003-08-26 14:00:00') nan 3.348091682713169] - [Timestamp('2003-08-26 15:00:00') nan 6.350079446795888]] + [[Timestamp('2003-08-25 20:00:00') nan 5.353998907560669] + [Timestamp('2003-08-25 21:00:00') nan 7.353579744773874] + [Timestamp('2003-08-25 22:00:00') nan 2.3505072441183965] + [Timestamp('2003-08-25 23:00:00') nan 6.354074686827981] + [Timestamp('2003-08-26 00:00:00') nan 8.351022108535407] + [Timestamp('2003-08-26 01:00:00') nan 2.352974142175746] + [Timestamp('2003-08-26 02:00:00') nan 4.350609681214998] + [Timestamp('2003-08-26 03:00:00') nan 1.3496812281302955] + [Timestamp('2003-08-26 04:00:00') nan 3.3481967140336693] + [Timestamp('2003-08-26 05:00:00') nan 6.348874753882701] + [Timestamp('2003-08-26 06:00:00') nan 5.353998907560669] + [Timestamp('2003-08-26 07:00:00') nan 7.353579744773874] + [Timestamp('2003-08-26 08:00:00') nan 2.3505072441183965] + [Timestamp('2003-08-26 09:00:00') nan 6.354074686827981] + [Timestamp('2003-08-26 10:00:00') nan 8.351022108535407] + [Timestamp('2003-08-26 11:00:00') nan 2.352974142175746] + [Timestamp('2003-08-26 12:00:00') nan 4.350609681214998] + [Timestamp('2003-08-26 13:00:00') nan 1.3496812281302955] + [Timestamp('2003-08-26 14:00:00') nan 3.3481967140336693] + [Timestamp('2003-08-26 15:00:00') nan 6.348874753882701]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 20, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 20, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08066801728649253", - "MAPE": "0.0233", - "MASE": "0.0269", - "RMSE": "0.10100139614354746" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08064743691160367", + "MAPE": "0.0233", + "MASE": "0.0269", + "RMSE": "0.10097896423433593" + } } } @@ -297,7 +330,7 @@ Forecasts -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null},"Signal_Forecast":{"31988":5.3558670995,"31989":7.351919708,"31990":2.3508527407,"31991":6.3551729256,"31992":8.3515477646,"31993":2.3539231019,"31994":4.3502614449,"31995":1.3507617135,"31996":3.3480916827,"31997":6.3500794468,"31998":5.3558670995,"31999":7.351919708,"32000":2.3508527407,"32001":6.3551729256,"32002":8.3515477646,"32003":2.3539231019,"32004":4.3502614449,"32005":1.3507617135,"32006":3.3480916827,"32007":6.3500794468}} +{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null},"Signal_Forecast":{"31988":5.3539989076,"31989":7.3535797448,"31990":2.3505072441,"31991":6.3540746868,"31992":8.3510221085,"31993":2.3529741422,"31994":4.3506096812,"31995":1.3496812281,"31996":3.348196714,"31997":6.3488747539,"31998":5.3539989076,"31999":7.3535797448,"32000":2.3505072441,"32001":6.3540746868,"32002":8.3510221085,"32003":2.3529741422,"32004":4.3506096812,"32005":1.3496812281,"32006":3.348196714,"32007":6.3488747539}} @@ -305,32 +338,41 @@ TEST_CYCLES_END 10 TEST_CYCLES_START 14 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_14_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 54.948216915130615 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 76.55838632583618 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T07:00:00.000000 TimeDelta= Horizon=28 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=9.498336323016646 Mean=4.914127790036362 StdDev=2.21938241641426 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=9.498336323016646 Mean=4.914127790036362 StdDev=2.21938241641426 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR' [ConstantTrend + Seasonal_HourOfWeek + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0215 MAPE_Forecast=0.0222 MAPE_Test=0.0173 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0214 SMAPE_Forecast=0.0221 SMAPE_Test=0.0173 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0366 MASE_Forecast=0.0375 MASE_Test=0.0299 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07879207580928814 L1_Forecast=0.08070664717092943 L1_Test=0.06470131886925524 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09904274122031652 L2_Forecast=0.10102433007959907 L2_Test=0.08592266975028022 -INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek' [Seasonal_HourOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL 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113: 0.6466818309486579, 114: -0.059040163255775546, 115: 1.389416846410355, 116: 1.3853591101027876, 117: 4.234362471007696, 118: 2.8041453118689033, 119: -3.6233961401698798} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 7.417515277862549 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 7.375721216201782 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek', + '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue', + '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR', + '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -349,63 +391,65 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.5 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 7.716779541001199] - [Timestamp('2003-08-25 21:00:00') nan 1.2918727971268784] - [Timestamp('2003-08-25 22:00:00') nan 2.719435238730687] - [Timestamp('2003-08-25 23:00:00') nan 5.576697909984164] - [Timestamp('2003-08-26 00:00:00') nan 7.0064048844872175] - [Timestamp('2003-08-26 01:00:00') nan 2.7232909276901958] - [Timestamp('2003-08-26 02:00:00') nan 4.150654951891211] - [Timestamp('2003-08-26 03:00:00') nan 2.0077658443535538] - [Timestamp('2003-08-26 04:00:00') nan 3.436589194947015] - [Timestamp('2003-08-26 05:00:00') nan 5.576229214923491] - [Timestamp('2003-08-26 06:00:00') nan 4.863228480739073] - [Timestamp('2003-08-26 07:00:00') nan 6.292000843663519] - [Timestamp('2003-08-26 08:00:00') nan 6.288438291685888] - [Timestamp('2003-08-26 09:00:00') nan 9.149403492230894] - [Timestamp('2003-08-26 10:00:00') nan 7.716779541001199] - [Timestamp('2003-08-26 11:00:00') nan 1.2918727971268784] - [Timestamp('2003-08-26 12:00:00') nan 2.719435238730687] - [Timestamp('2003-08-26 13:00:00') nan 5.576697909984164] - [Timestamp('2003-08-26 14:00:00') nan 7.0064048844872175] - [Timestamp('2003-08-26 15:00:00') nan 2.7232909276901958] - [Timestamp('2003-08-26 16:00:00') nan 4.150654951891211] - [Timestamp('2003-08-26 17:00:00') nan 2.0077658443535538] - [Timestamp('2003-08-26 18:00:00') nan 3.436589194947015] - [Timestamp('2003-08-26 19:00:00') nan 5.576229214923491] - [Timestamp('2003-08-26 20:00:00') nan 4.863228480739073] - [Timestamp('2003-08-26 21:00:00') nan 6.292000843663519] - [Timestamp('2003-08-26 22:00:00') nan 6.288438291685888] - [Timestamp('2003-08-26 23:00:00') nan 9.149403492230894]] + [[Timestamp('2003-08-25 20:00:00') nan 7.717147828648914] + [Timestamp('2003-08-25 21:00:00') nan 1.2933139104814266] + [Timestamp('2003-08-25 22:00:00') nan 2.7236232159522675] + [Timestamp('2003-08-25 23:00:00') nan 5.591697124222082] + [Timestamp('2003-08-26 00:00:00') nan 7.008624582516546] + [Timestamp('2003-08-26 01:00:00') nan 2.714348496482085] + [Timestamp('2003-08-26 02:00:00') nan 4.151050185084388] + [Timestamp('2003-08-26 03:00:00') nan 2.00184785125818] + [Timestamp('2003-08-26 04:00:00') nan 3.431419672994254] + [Timestamp('2003-08-26 05:00:00') nan 5.568502443278032] + [Timestamp('2003-08-26 06:00:00') nan 4.864240099171656] + [Timestamp('2003-08-26 07:00:00') nan 6.288243687752381] + [Timestamp('2003-08-26 08:00:00') nan 6.29614579502398] + [Timestamp('2003-08-26 09:00:00') nan 9.15684187142951] + [Timestamp('2003-08-26 10:00:00') nan 7.71828125847931] + [Timestamp('2003-08-26 11:00:00') nan 1.2884162444152008] + [Timestamp('2003-08-26 12:00:00') nan 2.7137216400608395] + [Timestamp('2003-08-26 13:00:00') nan 5.576918865475979] + [Timestamp('2003-08-26 14:00:00') nan 7.008096744787299] + [Timestamp('2003-08-26 15:00:00') nan 2.725705095531281] + [Timestamp('2003-08-26 16:00:00') nan 4.1442788461224165] + [Timestamp('2003-08-26 17:00:00') nan 2.014860219949029] + [Timestamp('2003-08-26 18:00:00') nan 3.4552919877871044] + [Timestamp('2003-08-26 19:00:00') nan 5.579804774081413] + [Timestamp('2003-08-26 20:00:00') nan 4.842070680562014] + [Timestamp('2003-08-26 21:00:00') nan 6.288141491587831] + [Timestamp('2003-08-26 22:00:00') nan 6.300583287298524] + [Timestamp('2003-08-26 23:00:00') nan 9.15731294483159]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 28, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 28, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR", + "Cycle": "Seasonal_HourOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08070664717092943", - "MAPE": "0.0222", - "MASE": "0.0375", - "RMSE": "0.10102433007959907" + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "0.08106350300805767", + "MAPE": "0.0223", + "MASE": "0.0377", + "RMSE": "0.10150581080497878" + } } } @@ -414,7 +458,7 @@ Forecasts -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null},"Signal_Forecast":{"31988":7.716779541,"31989":1.2918727971,"31990":2.7194352387,"31991":5.57669791,"31992":7.0064048845,"31993":2.7232909277,"31994":4.1506549519,"31995":2.0077658444,"31996":3.4365891949,"31997":5.5762292149,"31998":4.8632284807,"31999":6.2920008437,"32000":6.2884382917,"32001":9.1494034922,"32002":7.716779541,"32003":1.2918727971,"32004":2.7194352387,"32005":5.57669791,"32006":7.0064048845,"32007":2.7232909277,"32008":4.1506549519,"32009":2.0077658444,"32010":3.4365891949,"32011":5.5762292149,"32012":4.8632284807,"32013":6.2920008437,"32014":6.2884382917,"32015":9.1494034922}} +{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null},"Signal_Forecast":{"31988":7.7171478286,"31989":1.2933139105,"31990":2.723623216,"31991":5.5916971242,"31992":7.0086245825,"31993":2.7143484965,"31994":4.1510501851,"31995":2.0018478513,"31996":3.431419673,"31997":5.5685024433,"31998":4.8642400992,"31999":6.2882436878,"32000":6.296145795,"32001":9.1568418714,"32002":7.7182812585,"32003":1.2884162444,"32004":2.7137216401,"32005":5.5769188655,"32006":7.0080967448,"32007":2.7257050955,"32008":4.1442788461,"32009":2.0148602199,"32010":3.4552919878,"32011":5.5798047741,"32012":4.8420706806,"32013":6.2881414916,"32014":6.3005832873,"32015":9.1573129448}} @@ -422,32 +466,41 @@ TEST_CYCLES_END 14 TEST_CYCLES_START 18 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_18_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 41.2237868309021 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 107.67006850242615 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=10.576427076945718 Mean=5.633355844209139 StdDev=2.5597257798105417 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.576427076945718 Mean=5.633355844209139 StdDev=2.5597257798105417 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0185 MAPE_Forecast=0.0181 MAPE_Test=0.0189 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0185 SMAPE_Forecast=0.0181 SMAPE_Test=0.0184 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.023 MASE_Forecast=0.0226 MASE_Test=0.0202 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07950354912814159 L1_Forecast=0.0782535805622656 L1_Test=0.07113849940016446 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.0999744396283691 L2_Forecast=0.09823029320966337 L2_Test=0.08636308805383552 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0185 MAPE_Forecast=0.0181 MAPE_Test=0.0185 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0185 SMAPE_Forecast=0.0181 SMAPE_Test=0.0181 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.023 MASE_Forecast=0.0227 MASE_Test=0.0198 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07958924340299912 L1_Forecast=0.07832565396296981 L1_Test=0.07002568368804168 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10011698257279802 L2_Forecast=0.09829060464825726 L2_Test=0.0852649926930095 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5.633453794953687 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 18 -0.6718146495871293 {0: -3.1791669103965314, 1: -0.9577375212383066, 2: 0.146711186111816, 3: -2.622064593343656, 4: -1.5152844165964323, 5: 1.8191817883665733, 6: -4.290428403648969, 7: -1.5097390376254975, 8: -0.9561993521071699, 9: 2.935819719171061, 10: 4.599750234741594, 11: -2.0663613648920727, 12: 2.379912394752645, 13: -0.406214935857629, 14: 0.15951855376602087, 15: 4.044687276052934, 16: -2.061640301708385, 17: 3.4861072437956375} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 6.692098140716553 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 9.716177463531494 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -466,71 +519,73 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.7 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 5.788418344125107] - [Timestamp('2003-08-25 21:00:00') nan 3.0101117841588496] - [Timestamp('2003-08-25 22:00:00') nan 4.1092264357885835] - [Timestamp('2003-08-25 23:00:00') nan 7.448158075388571] - [Timestamp('2003-08-26 00:00:00') nan 1.3511247728046154] - [Timestamp('2003-08-26 01:00:00') nan 4.128653701408584] - [Timestamp('2003-08-26 02:00:00') nan 4.669469081029887] - [Timestamp('2003-08-26 03:00:00') nan 8.570324634505875] - [Timestamp('2003-08-26 04:00:00') nan 10.229335798050279] - [Timestamp('2003-08-26 05:00:00') nan 3.5664661715914248] - [Timestamp('2003-08-26 06:00:00') nan 8.016445790025644] - [Timestamp('2003-08-26 07:00:00') nan 5.213866445905249] - [Timestamp('2003-08-26 08:00:00') nan 5.789888275914795] - [Timestamp('2003-08-26 09:00:00') nan 9.67762249640452] - [Timestamp('2003-08-26 10:00:00') nan 3.571184940962243] - [Timestamp('2003-08-26 11:00:00') nan 9.109504241452747] - [Timestamp('2003-08-26 12:00:00') nan 2.45215872629142] - [Timestamp('2003-08-26 13:00:00') nan 4.6732908141459575] - [Timestamp('2003-08-26 14:00:00') nan 5.769652925477868] - [Timestamp('2003-08-26 15:00:00') nan 3.0052983882795914] - [Timestamp('2003-08-26 16:00:00') nan 4.1243901161998995] - [Timestamp('2003-08-26 17:00:00') nan 7.454607491669816] - [Timestamp('2003-08-26 18:00:00') nan 1.3464872328715884] - [Timestamp('2003-08-26 19:00:00') nan 4.120466453406708] - [Timestamp('2003-08-26 20:00:00') nan 4.686654032061969] - [Timestamp('2003-08-26 21:00:00') nan 8.567513688868868] - [Timestamp('2003-08-26 22:00:00') nan 10.229771023702636] - [Timestamp('2003-08-26 23:00:00') nan 3.576885969712121] - [Timestamp('2003-08-27 00:00:00') nan 8.012253232171728] - [Timestamp('2003-08-27 01:00:00') nan 5.223467005597552] - [Timestamp('2003-08-27 02:00:00') nan 5.784752154380072] - [Timestamp('2003-08-27 03:00:00') nan 9.677738708684743] - [Timestamp('2003-08-27 04:00:00') nan 3.571549419113958] - [Timestamp('2003-08-27 05:00:00') nan 9.118073178385938] - [Timestamp('2003-08-27 06:00:00') nan 2.4592149675902384] - [Timestamp('2003-08-27 07:00:00') nan 4.675235422551577]] + [[Timestamp('2003-08-25 20:00:00') nan 5.780164981065504] + [Timestamp('2003-08-25 21:00:00') nan 3.011389201610031] + [Timestamp('2003-08-25 22:00:00') nan 4.118169378357255] + [Timestamp('2003-08-25 23:00:00') nan 7.45263558332026] + [Timestamp('2003-08-26 00:00:00') nan 1.3430253913047183] + [Timestamp('2003-08-26 01:00:00') nan 4.123714757328189] + [Timestamp('2003-08-26 02:00:00') nan 4.677254442846517] + [Timestamp('2003-08-26 03:00:00') nan 8.569273514124749] + [Timestamp('2003-08-26 04:00:00') nan 10.233204029695282] + [Timestamp('2003-08-26 05:00:00') nan 3.5670924300616145] + [Timestamp('2003-08-26 06:00:00') nan 8.013366189706332] + [Timestamp('2003-08-26 07:00:00') nan 5.227238859096058] + [Timestamp('2003-08-26 08:00:00') nan 5.792972348719708] + [Timestamp('2003-08-26 09:00:00') nan 9.678141071006621] + [Timestamp('2003-08-26 10:00:00') nan 3.571813493245302] + [Timestamp('2003-08-26 11:00:00') nan 9.119561038749325] + [Timestamp('2003-08-26 12:00:00') nan 2.4542868845571557] + [Timestamp('2003-08-26 13:00:00') nan 4.675716273715381] + [Timestamp('2003-08-26 14:00:00') nan 5.780164981065504] + [Timestamp('2003-08-26 15:00:00') nan 3.011389201610031] + [Timestamp('2003-08-26 16:00:00') nan 4.118169378357255] + [Timestamp('2003-08-26 17:00:00') nan 7.45263558332026] + [Timestamp('2003-08-26 18:00:00') nan 1.3430253913047183] + [Timestamp('2003-08-26 19:00:00') nan 4.123714757328189] + [Timestamp('2003-08-26 20:00:00') nan 4.677254442846517] + [Timestamp('2003-08-26 21:00:00') nan 8.569273514124749] + [Timestamp('2003-08-26 22:00:00') nan 10.233204029695282] + [Timestamp('2003-08-26 23:00:00') nan 3.5670924300616145] + [Timestamp('2003-08-27 00:00:00') nan 8.013366189706332] + [Timestamp('2003-08-27 01:00:00') nan 5.227238859096058] + [Timestamp('2003-08-27 02:00:00') nan 5.792972348719708] + [Timestamp('2003-08-27 03:00:00') nan 9.678141071006621] + [Timestamp('2003-08-27 04:00:00') nan 3.571813493245302] + [Timestamp('2003-08-27 05:00:00') nan 9.119561038749325] + [Timestamp('2003-08-27 06:00:00') nan 2.4542868845571557] + [Timestamp('2003-08-27 07:00:00') nan 4.675716273715381]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 36, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 36, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.0782535805622656", - "MAPE": "0.0181", - "MASE": "0.0226", - "RMSE": "0.09823029320966337" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.07832565396296981", + "MAPE": "0.0181", + "MASE": "0.0227", + "RMSE": "0.09829060464825726" + } } } @@ -539,7 +594,7 @@ Forecasts -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z","32016":"2003-08-27T00:00:00.000Z","32017":"2003-08-27T01:00:00.000Z","32018":"2003-08-27T02:00:00.000Z","32019":"2003-08-27T03:00:00.000Z","32020":"2003-08-27T04:00:00.000Z","32021":"2003-08-27T05:00:00.000Z","32022":"2003-08-27T06:00:00.000Z","32023":"2003-08-27T07:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null,"32016":null,"32017":null,"32018":null,"32019":null,"32020":null,"32021":null,"32022":null,"32023":null},"Signal_Forecast":{"31988":5.7884183441,"31989":3.0101117842,"31990":4.1092264358,"31991":7.4481580754,"31992":1.3511247728,"31993":4.1286537014,"31994":4.669469081,"31995":8.5703246345,"31996":10.2293357981,"31997":3.5664661716,"31998":8.01644579,"31999":5.2138664459,"32000":5.7898882759,"32001":9.6776224964,"32002":3.571184941,"32003":9.1095042415,"32004":2.4521587263,"32005":4.6732908141,"32006":5.7696529255,"32007":3.0052983883,"32008":4.1243901162,"32009":7.4546074917,"32010":1.3464872329,"32011":4.1204664534,"32012":4.6866540321,"32013":8.5675136889,"32014":10.2297710237,"32015":3.5768859697,"32016":8.0122532322,"32017":5.2234670056,"32018":5.7847521544,"32019":9.6777387087,"32020":3.5715494191,"32021":9.1180731784,"32022":2.4592149676,"32023":4.6752354226}} +{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z","32016":"2003-08-27T00:00:00.000Z","32017":"2003-08-27T01:00:00.000Z","32018":"2003-08-27T02:00:00.000Z","32019":"2003-08-27T03:00:00.000Z","32020":"2003-08-27T04:00:00.000Z","32021":"2003-08-27T05:00:00.000Z","32022":"2003-08-27T06:00:00.000Z","32023":"2003-08-27T07:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null,"32016":null,"32017":null,"32018":null,"32019":null,"32020":null,"32021":null,"32022":null,"32023":null},"Signal_Forecast":{"31988":5.7801649811,"31989":3.0113892016,"31990":4.1181693784,"31991":7.4526355833,"31992":1.3430253913,"31993":4.1237147573,"31994":4.6772544428,"31995":8.5692735141,"31996":10.2332040297,"31997":3.5670924301,"31998":8.0133661897,"31999":5.2272388591,"32000":5.7929723487,"32001":9.678141071,"32002":3.5718134932,"32003":9.1195610387,"32004":2.4542868846,"32005":4.6757162737,"32006":5.7801649811,"32007":3.0113892016,"32008":4.1181693784,"32009":7.4526355833,"32010":1.3430253913,"32011":4.1237147573,"32012":4.6772544428,"32013":8.5692735141,"32014":10.2332040297,"32015":3.5670924301,"32016":8.0133661897,"32017":5.2272388591,"32018":5.7929723487,"32019":9.678141071,"32020":3.5718134932,"32021":9.1195610387,"32022":2.4542868846,"32023":4.6757162737}} @@ -547,31 +602,40 @@ TEST_CYCLES_END 18 TEST_CYCLES_START 22 GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_22_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 37.875866651535034 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 101.25743079185486 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-11-30T18:00:00.000000 TimeDelta= Horizon=44 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=10.758520015803596 Mean=6.098222819806447 StdDev=2.810646854893992 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.758520015803596 Mean=6.098222819806447 StdDev=2.810646854893992 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0179 MAPE_Forecast=0.018 MAPE_Test=0.0223 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0179 SMAPE_Forecast=0.0179 SMAPE_Test=0.0222 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0299 MASE_Forecast=0.03 MASE_Test=0.0355 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08037054640188437 L1_Forecast=0.08065873696180399 L1_Test=0.0908536290242984 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10073418401778016 L2_Forecast=0.10083100124303364 L2_Test=0.11556693323710295 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0179 MAPE_Forecast=0.018 MAPE_Test=0.0222 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0178 SMAPE_Forecast=0.0179 SMAPE_Test=0.0221 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0299 MASE_Forecast=0.03 MASE_Test=0.0353 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.08032611138875244 L1_Forecast=0.08075293102223974 L1_Test=0.09032240650280009 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10078902909585007 L2_Forecast=0.10091825923290786 L2_Test=0.1149716067777537 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.0981444673224665 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 22 -0.8289203680368509 {0: -3.8584048930599213, 1: -2.044771209716883, 2: -1.1313973381021545, 3: 3.4104909287428056, 4: 4.3291513599874065, 5: 2.958284496745141, 6: -3.4118851906278436, 7: -2.498326554648907, 8: 0.2208718697573886, 9: -4.774180091357353, 10: -2.499232322795973, 11: -2.051079991143092, 12: 1.1293139040958398, 13: 2.500372795885049, 14: -2.958150138832557, 15: 0.6773172828592138, 16: -1.583588645659888, 17: -1.12902668929003, 18: 3.864863385068819, 19: 3.4013741031993536, 20: 2.0444213681217, 21: 3.411227384830518} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 5.665000915527344 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 12.542189121246338 Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -590,79 +654,81 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 750.9 KB None Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 2.2382608505038615] - [Timestamp('2003-08-25 21:00:00') nan 4.054464386821573] - [Timestamp('2003-08-25 22:00:00') nan 4.964740805683372] - [Timestamp('2003-08-25 23:00:00') nan 9.509472424714387] - [Timestamp('2003-08-26 00:00:00') nan 10.421657788635786] - [Timestamp('2003-08-26 01:00:00') nan 9.05993047427913] - [Timestamp('2003-08-26 02:00:00') nan 2.688807941098492] - [Timestamp('2003-08-26 03:00:00') nan 3.597045553119314] - [Timestamp('2003-08-26 04:00:00') nan 6.321125684665563] - [Timestamp('2003-08-26 05:00:00') nan 1.3179039753830226] - [Timestamp('2003-08-26 06:00:00') nan 3.5990967615138207] - [Timestamp('2003-08-26 07:00:00') nan 4.050299921933755] - [Timestamp('2003-08-26 08:00:00') nan 7.228097682961997] - [Timestamp('2003-08-26 09:00:00') nan 8.598890858436441] - [Timestamp('2003-08-26 10:00:00') nan 3.1451402885829647] - [Timestamp('2003-08-26 11:00:00') nan 6.779839229691158] - [Timestamp('2003-08-26 12:00:00') nan 4.511119616872964] - [Timestamp('2003-08-26 13:00:00') nan 4.965135346238603] - [Timestamp('2003-08-26 14:00:00') nan 9.960774113308226] - [Timestamp('2003-08-26 15:00:00') nan 9.505338636775136] - [Timestamp('2003-08-26 16:00:00') nan 8.144452038702253] - [Timestamp('2003-08-26 17:00:00') nan 9.506390941503097] - [Timestamp('2003-08-26 18:00:00') nan 2.2382608505038615] - [Timestamp('2003-08-26 19:00:00') nan 4.054464386821573] - [Timestamp('2003-08-26 20:00:00') nan 4.964740805683372] - [Timestamp('2003-08-26 21:00:00') nan 9.509472424714387] - [Timestamp('2003-08-26 22:00:00') nan 10.421657788635786] - [Timestamp('2003-08-26 23:00:00') nan 9.05993047427913] - [Timestamp('2003-08-27 00:00:00') nan 2.688807941098492] - [Timestamp('2003-08-27 01:00:00') nan 3.597045553119314] - [Timestamp('2003-08-27 02:00:00') nan 6.321125684665563] - [Timestamp('2003-08-27 03:00:00') nan 1.3179039753830226] - [Timestamp('2003-08-27 04:00:00') nan 3.5990967615138207] - [Timestamp('2003-08-27 05:00:00') nan 4.050299921933755] - [Timestamp('2003-08-27 06:00:00') nan 7.228097682961997] - [Timestamp('2003-08-27 07:00:00') nan 8.598890858436441] - [Timestamp('2003-08-27 08:00:00') nan 3.1451402885829647] - [Timestamp('2003-08-27 09:00:00') nan 6.779839229691158] - [Timestamp('2003-08-27 10:00:00') nan 4.511119616872964] - [Timestamp('2003-08-27 11:00:00') nan 4.965135346238603] - [Timestamp('2003-08-27 12:00:00') nan 9.960774113308226] - [Timestamp('2003-08-27 13:00:00') nan 9.505338636775136] - [Timestamp('2003-08-27 14:00:00') nan 8.144452038702253] - [Timestamp('2003-08-27 15:00:00') nan 9.506390941503097]] + [[Timestamp('2003-08-25 20:00:00') nan 2.239739574262545] + [Timestamp('2003-08-25 21:00:00') nan 4.053373257605584] + [Timestamp('2003-08-25 22:00:00') nan 4.9667471292203125] + [Timestamp('2003-08-25 23:00:00') nan 9.508635396065273] + [Timestamp('2003-08-26 00:00:00') nan 10.427295827309873] + [Timestamp('2003-08-26 01:00:00') nan 9.056428964067607] + [Timestamp('2003-08-26 02:00:00') nan 2.686259276694623] + [Timestamp('2003-08-26 03:00:00') nan 3.5998179126735597] + [Timestamp('2003-08-26 04:00:00') nan 6.319016337079855] + [Timestamp('2003-08-26 05:00:00') nan 1.3239643759651134] + [Timestamp('2003-08-26 06:00:00') nan 3.5989121445264933] + [Timestamp('2003-08-26 07:00:00') nan 4.047064476179374] + [Timestamp('2003-08-26 08:00:00') nan 7.227458371418306] + [Timestamp('2003-08-26 09:00:00') nan 8.598517263207516] + [Timestamp('2003-08-26 10:00:00') nan 3.1399943284899097] + [Timestamp('2003-08-26 11:00:00') nan 6.77546175018168] + [Timestamp('2003-08-26 12:00:00') nan 4.5145558216625785] + [Timestamp('2003-08-26 13:00:00') nan 4.9691177780324365] + [Timestamp('2003-08-26 14:00:00') nan 9.963007852391286] + [Timestamp('2003-08-26 15:00:00') nan 9.49951857052182] + [Timestamp('2003-08-26 16:00:00') nan 8.142565835444167] + [Timestamp('2003-08-26 17:00:00') nan 9.509371852152984] + [Timestamp('2003-08-26 18:00:00') nan 2.239739574262545] + [Timestamp('2003-08-26 19:00:00') nan 4.053373257605584] + [Timestamp('2003-08-26 20:00:00') nan 4.9667471292203125] + [Timestamp('2003-08-26 21:00:00') nan 9.508635396065273] + [Timestamp('2003-08-26 22:00:00') nan 10.427295827309873] + [Timestamp('2003-08-26 23:00:00') nan 9.056428964067607] + [Timestamp('2003-08-27 00:00:00') nan 2.686259276694623] + [Timestamp('2003-08-27 01:00:00') nan 3.5998179126735597] + [Timestamp('2003-08-27 02:00:00') nan 6.319016337079855] + [Timestamp('2003-08-27 03:00:00') nan 1.3239643759651134] + [Timestamp('2003-08-27 04:00:00') nan 3.5989121445264933] + [Timestamp('2003-08-27 05:00:00') nan 4.047064476179374] + [Timestamp('2003-08-27 06:00:00') nan 7.227458371418306] + [Timestamp('2003-08-27 07:00:00') nan 8.598517263207516] + [Timestamp('2003-08-27 08:00:00') nan 3.1399943284899097] + [Timestamp('2003-08-27 09:00:00') nan 6.77546175018168] + [Timestamp('2003-08-27 10:00:00') nan 4.5145558216625785] + [Timestamp('2003-08-27 11:00:00') nan 4.9691177780324365] + [Timestamp('2003-08-27 12:00:00') nan 9.963007852391286] + [Timestamp('2003-08-27 13:00:00') nan 9.49951857052182] + [Timestamp('2003-08-27 14:00:00') nan 8.142565835444167] + [Timestamp('2003-08-27 15:00:00') nan 9.509371852152984]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 44, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 44, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-08-25 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 31988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.08065873696180399", - "MAPE": "0.018", - "MASE": "0.03", - "RMSE": "0.10083100124303364" + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08075293102223974", + "MAPE": "0.018", + "MASE": "0.03", + "RMSE": "0.10091825923290786" + } } } @@ -671,7 +737,7 @@ Forecasts -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z","32016":"2003-08-27T00:00:00.000Z","32017":"2003-08-27T01:00:00.000Z","32018":"2003-08-27T02:00:00.000Z","32019":"2003-08-27T03:00:00.000Z","32020":"2003-08-27T04:00:00.000Z","32021":"2003-08-27T05:00:00.000Z","32022":"2003-08-27T06:00:00.000Z","32023":"2003-08-27T07:00:00.000Z","32024":"2003-08-27T08:00:00.000Z","32025":"2003-08-27T09:00:00.000Z","32026":"2003-08-27T10:00:00.000Z","32027":"2003-08-27T11:00:00.000Z","32028":"2003-08-27T12:00:00.000Z","32029":"2003-08-27T13:00:00.000Z","32030":"2003-08-27T14:00:00.000Z","32031":"2003-08-27T15:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null,"32016":null,"32017":null,"32018":null,"32019":null,"32020":null,"32021":null,"32022":null,"32023":null,"32024":null,"32025":null,"32026":null,"32027":null,"32028":null,"32029":null,"32030":null,"32031":null},"Signal_Forecast":{"31988":2.2382608505,"31989":4.0544643868,"31990":4.9647408057,"31991":9.5094724247,"31992":10.4216577886,"31993":9.0599304743,"31994":2.6888079411,"31995":3.5970455531,"31996":6.3211256847,"31997":1.3179039754,"31998":3.5990967615,"31999":4.0502999219,"32000":7.228097683,"32001":8.5988908584,"32002":3.1451402886,"32003":6.7798392297,"32004":4.5111196169,"32005":4.9651353462,"32006":9.9607741133,"32007":9.5053386368,"32008":8.1444520387,"32009":9.5063909415,"32010":2.2382608505,"32011":4.0544643868,"32012":4.9647408057,"32013":9.5094724247,"32014":10.4216577886,"32015":9.0599304743,"32016":2.6888079411,"32017":3.5970455531,"32018":6.3211256847,"32019":1.3179039754,"32020":3.5990967615,"32021":4.0502999219,"32022":7.228097683,"32023":8.5988908584,"32024":3.1451402886,"32025":6.7798392297,"32026":4.5111196169,"32027":4.9651353462,"32028":9.9607741133,"32029":9.5053386368,"32030":8.1444520387,"32031":9.5063909415}} +{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z","32000":"2003-08-26T08:00:00.000Z","32001":"2003-08-26T09:00:00.000Z","32002":"2003-08-26T10:00:00.000Z","32003":"2003-08-26T11:00:00.000Z","32004":"2003-08-26T12:00:00.000Z","32005":"2003-08-26T13:00:00.000Z","32006":"2003-08-26T14:00:00.000Z","32007":"2003-08-26T15:00:00.000Z","32008":"2003-08-26T16:00:00.000Z","32009":"2003-08-26T17:00:00.000Z","32010":"2003-08-26T18:00:00.000Z","32011":"2003-08-26T19:00:00.000Z","32012":"2003-08-26T20:00:00.000Z","32013":"2003-08-26T21:00:00.000Z","32014":"2003-08-26T22:00:00.000Z","32015":"2003-08-26T23:00:00.000Z","32016":"2003-08-27T00:00:00.000Z","32017":"2003-08-27T01:00:00.000Z","32018":"2003-08-27T02:00:00.000Z","32019":"2003-08-27T03:00:00.000Z","32020":"2003-08-27T04:00:00.000Z","32021":"2003-08-27T05:00:00.000Z","32022":"2003-08-27T06:00:00.000Z","32023":"2003-08-27T07:00:00.000Z","32024":"2003-08-27T08:00:00.000Z","32025":"2003-08-27T09:00:00.000Z","32026":"2003-08-27T10:00:00.000Z","32027":"2003-08-27T11:00:00.000Z","32028":"2003-08-27T12:00:00.000Z","32029":"2003-08-27T13:00:00.000Z","32030":"2003-08-27T14:00:00.000Z","32031":"2003-08-27T15:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null,"32000":null,"32001":null,"32002":null,"32003":null,"32004":null,"32005":null,"32006":null,"32007":null,"32008":null,"32009":null,"32010":null,"32011":null,"32012":null,"32013":null,"32014":null,"32015":null,"32016":null,"32017":null,"32018":null,"32019":null,"32020":null,"32021":null,"32022":null,"32023":null,"32024":null,"32025":null,"32026":null,"32027":null,"32028":null,"32029":null,"32030":null,"32031":null},"Signal_Forecast":{"31988":2.2397395743,"31989":4.0533732576,"31990":4.9667471292,"31991":9.5086353961,"31992":10.4272958273,"31993":9.0564289641,"31994":2.6862592767,"31995":3.5998179127,"31996":6.3190163371,"31997":1.323964376,"31998":3.5989121445,"31999":4.0470644762,"32000":7.2274583714,"32001":8.5985172632,"32002":3.1399943285,"32003":6.7754617502,"32004":4.5145558217,"32005":4.969117778,"32006":9.9630078524,"32007":9.4995185705,"32008":8.1425658354,"32009":9.5093718522,"32010":2.2397395743,"32011":4.0533732576,"32012":4.9667471292,"32013":9.5086353961,"32014":10.4272958273,"32015":9.0564289641,"32016":2.6862592767,"32017":3.5998179127,"32018":6.3190163371,"32019":1.323964376,"32020":3.5989121445,"32021":4.0470644762,"32022":7.2274583714,"32023":8.5985172632,"32024":3.1399943285,"32025":6.7754617502,"32026":4.5145558217,"32027":4.969117778,"32028":9.9630078524,"32029":9.4995185705,"32030":8.1425658354,"32031":9.5093718522}} diff --git a/tests/references/perf_test_cycles_full_long_long_2.log b/tests/references/perf_test_cycles_full_long_long_2.log index 4fadc69b5..b026c746f 100644 --- a/tests/references/perf_test_cycles_full_long_long_2.log +++ b/tests/references/perf_test_cycles_full_long_long_2.log @@ -2,7 +2,7 @@ INFO:pyaf.std:START_TRAINING 'Signal' TEST_CYCLES_START 1000 2 GENERATING_RANDOM_DATASET Signal_1000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 12.918746948242188 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 9.403700351715088 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-02-02T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=988 Min=1.0 Max=1.5805498168821508 Mean=1.299637182701963 StdDev=0.09868256890934886 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.5805498168821508 Mean=1.299637182701963 StdDev=0.09868256890934886 @@ -17,10 +17,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7036 MASE_Forecast=0.6868 MASE_Test=0.6852 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07933069003280466 L1_Forecast=0.0754756044876106 L1_Test=0.061552096762087394 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09946618816511571 L2_Forecast=0.09604506040385123 L2_Test=0.07941840031586232 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.2969193906134493 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.5580096244812012 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.2497539520263672 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -54,31 +63,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-02-11 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-02-11 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 988 }, - "Training_Signal_Length": 988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.0754756044876106", - "MAPE": "0.0571", - "MASE": "0.6868", - "RMSE": "0.09604506040385123" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.0754756044876106", + "MAPE": "0.0571", + "MASE": "0.6868", + "RMSE": "0.09604506040385123" + } } } @@ -95,7 +106,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 2000 2 GENERATING_RANDOM_DATASET Signal_2000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 16.235893726348877 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 15.901494264602661 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-03-07T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=1988 Min=1.0 Max=1.62171178280901 Mean=1.3027321147079434 StdDev=0.09768320075731325 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.62171178280901 Mean=1.3027321147079434 StdDev=0.09768320075731325 @@ -110,10 +121,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7032 MASE_Forecast=0.6834 MASE_Test=0.5396 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07776359355366923 L1_Forecast=0.0774335242915975 L1_Test=0.08427414277671641 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09782779950765252 L2_Forecast=0.09709086902935903 L2_Test=0.09892246327802084 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3025940993785818 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0447039604187012 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.3984379768371582 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -147,31 +167,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-03-23 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-03-23 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 1988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 1988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.0774335242915975", - "MAPE": "0.0598", - "MASE": "0.6834", - "RMSE": "0.09709086902935903" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.0774335242915975", + "MAPE": "0.0598", + "MASE": "0.6834", + "RMSE": "0.09709086902935903" + } } } @@ -188,7 +210,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 3000 2 GENERATING_RANDOM_DATASET Signal_3000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 22.979641914367676 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 31.356566905975342 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-04-09T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=2988 Min=1.0 Max=1.6287831364889307 Mean=1.3087108849732652 StdDev=0.09693880508050107 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.6287831364889307 Mean=1.3087108849732652 StdDev=0.09693880508050107 @@ -203,10 +225,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.701 MASE_Forecast=0.752 MASE_Test=0.9125 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07797215204476025 L1_Forecast=0.07628457300575657 L1_Test=0.094749992686054 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09777073138200282 L2_Forecast=0.09347743713691056 L2_Test=0.10679634818484866 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3093461861038145 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.7014601230621338 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.5481476783752441 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -240,31 +271,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-05-04 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-05-04 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 2988 }, - "Training_Signal_Length": 2988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07628457300575657", - "MAPE": "0.0592", - "MASE": "0.752", - "RMSE": "0.09347743713691056" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07628457300575657", + "MAPE": "0.0592", + "MASE": "0.752", + "RMSE": "0.09347743713691056" + } } } @@ -281,7 +314,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 4000 2 GENERATING_RANDOM_DATASET Signal_4000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 16.581966638565063 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 32.06446123123169 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-05-12T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3988 Min=1.0 Max=1.7541760852918893 Mean=1.371279600103362 StdDev=0.09811982911428974 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.371279600103362 StdDev=0.09811982911428974 @@ -296,10 +329,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7088 MASE_Forecast=0.7072 MASE_Test=0.6347 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07770031772580234 L1_Forecast=0.0813508989818061 L1_Test=0.07658618627547353 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09726088636179017 L2_Forecast=0.10152564813529025 L2_Test=0.08982133612851816 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3708202001282 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0192418098449707 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.5187101364135742 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -333,31 +375,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-06-15 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-06-15 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 3988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 3988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.0813508989818061", - "MAPE": "0.0598", - "MASE": "0.7072", - "RMSE": "0.10152564813529025" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.0813508989818061", + "MAPE": "0.0598", + "MASE": "0.7072", + "RMSE": "0.10152564813529025" + } } } @@ -374,7 +418,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 5000 2 GENERATING_RANDOM_DATASET Signal_5000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 20.884238719940186 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 40.41600799560547 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-06-15T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=4988 Min=1.0 Max=1.7541760852918893 Mean=1.372431473023716 StdDev=0.09851008065681871 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.372431473023716 StdDev=0.09851008065681871 @@ -389,10 +433,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7086 MASE_Forecast=0.7096 MASE_Test=0.7659 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07843399590315066 L1_Forecast=0.07992970516867312 L1_Test=0.08012773905583337 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09812747322581573 L2_Forecast=0.1001041694108186 L2_Test=0.0864772039811401 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3712644563025098 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.6055614948272705 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.8013243675231934 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -426,31 +479,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-07-26 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-07-26 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 4988 }, - "Training_Signal_Length": 4988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07992970516867312", - "MAPE": "0.0584", - "MASE": "0.7096", - "RMSE": "0.1001041694108186" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07992970516867312", + "MAPE": "0.0584", + "MASE": "0.7096", + "RMSE": "0.1001041694108186" + } } } @@ -467,7 +522,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 6000 2 GENERATING_RANDOM_DATASET Signal_6000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 21.228365659713745 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 37.62989258766174 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-07-18T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=5988 Min=1.0 Max=1.7541760852918893 Mean=1.3723845375005017 StdDev=0.09848046967483225 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3723845375005017 StdDev=0.09848046967483225 @@ -482,10 +537,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7089 MASE_Forecast=0.6845 MASE_Test=1.5373 INFO:pyaf.std:MODEL_L1 L1_Fit=0.078690461019712 L1_Forecast=0.07827153186670778 L1_Test=0.07023595487993717 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09852996328996433 L2_Forecast=0.09832847112639855 L2_Test=0.08533576778300482 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3719360972773678 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.3731276988983154 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.8248982429504395 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -519,31 +583,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-09-06 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-09-06 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 5988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 5988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07827153186670778", - "MAPE": "0.0574", - "MASE": "0.6845", - "RMSE": "0.09832847112639855" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07827153186670778", + "MAPE": "0.0574", + "MASE": "0.6845", + "RMSE": "0.09832847112639855" + } } } @@ -560,7 +626,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 7000 2 GENERATING_RANDOM_DATASET Signal_7000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 24.00507616996765 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 40.25805449485779 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-08-20T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=6988 Min=1.0 Max=1.7541760852918893 Mean=1.3728551878247004 StdDev=0.09882828054278137 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3728551878247004 StdDev=0.09882828054278137 @@ -575,10 +641,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7064 MASE_Forecast=0.6955 MASE_Test=0.6416 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07883967047199439 L1_Forecast=0.07886487265521876 L1_Test=0.05661375221403642 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09858685749855149 L2_Forecast=0.09988553152159205 L2_Test=0.06131663450443044 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3721798088375898 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.9541306495666504 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.8363556861877441 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -612,31 +687,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-10-18 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-10-18 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 6988 }, - "Training_Signal_Length": 6988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07886487265521876", - "MAPE": "0.0578", - "MASE": "0.6955", - "RMSE": "0.09988553152159205" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07886487265521876", + "MAPE": "0.0578", + "MASE": "0.6955", + "RMSE": "0.09988553152159205" + } } } @@ -653,7 +730,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 8000 2 GENERATING_RANDOM_DATASET Signal_8000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 23.77693819999695 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 42.24233651161194 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-09-23T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=7988 Min=1.0 Max=1.7541760852918893 Mean=1.3725821236358497 StdDev=0.09869628973192271 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3725821236358497 StdDev=0.09869628973192271 @@ -668,10 +745,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.703 MASE_Forecast=0.7123 MASE_Test=0.7811 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07876384769822455 L1_Forecast=0.0775976552597493 L1_Test=0.021268388336574295 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09874146087577965 L2_Forecast=0.09862837861411018 L2_Test=0.02927974991550791 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.372523863087832 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.6188271045684814 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.2124509811401367 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -705,31 +791,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-11-28 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-11-28 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 7988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 7988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.0775976552597493", - "MAPE": "0.0571", - "MASE": "0.7123", - "RMSE": "0.09862837861411018" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.0775976552597493", + "MAPE": "0.0571", + "MASE": "0.7123", + "RMSE": "0.09862837861411018" + } } } @@ -746,7 +834,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 9000 2 GENERATING_RANDOM_DATASET Signal_9000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 25.449299573898315 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 43.56703162193298 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-10-26T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=8988 Min=1.0 Max=1.7541760852918893 Mean=1.3721605758965485 StdDev=0.09846463873290134 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3721605758965485 StdDev=0.09846463873290134 @@ -761,10 +849,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7041 MASE_Forecast=0.7178 MASE_Test=0.7485 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07865597791811398 L1_Forecast=0.07672655075664514 L1_Test=0.11980479686063417 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09862402726588992 L2_Forecast=0.09773943973042094 L2_Test=0.1322637264751117 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3725370598741222 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0896904468536377 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.1742277145385742 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -798,31 +895,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-01-09 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-01-09 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 8988 }, - "Training_Signal_Length": 8988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07672655075664514", - "MAPE": "0.0566", - "MASE": "0.7178", - "RMSE": "0.09773943973042094" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07672655075664514", + "MAPE": "0.0566", + "MASE": "0.7178", + "RMSE": "0.09773943973042094" + } } } @@ -839,7 +938,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 10000 2 GENERATING_RANDOM_DATASET Signal_10000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 22.97901964187622 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 55.60714840888977 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-11-28T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=9988 Min=1.0 Max=1.7541760852918893 Mean=1.3720742755907434 StdDev=0.09871061738849869 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3720742755907434 StdDev=0.09871061738849869 @@ -854,10 +953,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7048 MASE_Forecast=0.714 MASE_Test=1.3064 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07850615460254619 L1_Forecast=0.07904931120472351 L1_Test=0.03739287955671844 INFO:pyaf.std:MODEL_L2 L2_Fit=0.0987010370556763 L2_Forecast=0.0988287268416534 L2_Test=0.05031118655416101 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.372588070451283 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.867051362991333 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.2183384895324707 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -891,31 +999,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-02-20 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-02-20 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 9988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 9988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07904931120472351", - "MAPE": "0.0584", - "MASE": "0.714", - "RMSE": "0.0988287268416534" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07904931120472351", + "MAPE": "0.0584", + "MASE": "0.714", + "RMSE": "0.0988287268416534" + } } } @@ -932,7 +1042,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 11000 2 GENERATING_RANDOM_DATASET Signal_11000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 30.58895754814148 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 61.83783221244812 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-01-01T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10988 Min=1.0 Max=1.7541760852918893 Mean=1.3727570138833984 StdDev=0.09889602357657945 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3727570138833984 StdDev=0.09889602357657945 @@ -947,10 +1057,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7078 MASE_Forecast=0.7124 MASE_Test=0.4606 INFO:pyaf.std:MODEL_L1 L1_Fit=0.0783416702699973 L1_Forecast=0.0802944082945034 L1_Test=0.07132912288903492 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09851368000810404 L2_Forecast=0.10041405617035813 L2_Test=0.09984812076939467 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3723043631128524 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.3290519714355469 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.7078030109405518 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -984,31 +1103,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-04-02 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-04-02 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 10988 }, - "Training_Signal_Length": 10988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.0802944082945034", - "MAPE": "0.0589", - "MASE": "0.7124", - "RMSE": "0.10041405617035813" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.0802944082945034", + "MAPE": "0.0589", + "MASE": "0.7124", + "RMSE": "0.10041405617035813" + } } } @@ -1025,7 +1146,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 12000 2 GENERATING_RANDOM_DATASET Signal_12000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 24.12173581123352 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 78.7697286605835 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-02-03T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=11988 Min=1.0 Max=1.7541760852918893 Mean=1.3724000573891202 StdDev=0.0990227532196697 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3724000573891202 StdDev=0.0990227532196697 @@ -1040,10 +1161,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7057 MASE_Forecast=0.7158 MASE_Test=0.8198 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07857813163230247 L1_Forecast=0.07963903258584329 L1_Test=0.07061341174470903 INFO:pyaf.std:MODEL_L2 L2_Fit=0.0987401476150457 L2_Forecast=0.10017654065470534 L2_Test=0.0820131237620789 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3719179013431293 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0996441841125488 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.7542004585266113 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1077,31 +1207,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-05-14 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-05-14 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 11988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 11988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07963903258584329", - "MAPE": "0.0584", - "MASE": "0.7158", - "RMSE": "0.10017654065470534" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07963903258584329", + "MAPE": "0.0584", + "MASE": "0.7158", + "RMSE": "0.10017654065470534" + } } } @@ -1118,7 +1250,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 13000 2 GENERATING_RANDOM_DATASET Signal_13000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 20.689087867736816 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 72.56379985809326 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-03-08T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=12988 Min=1.0 Max=1.7541760852918893 Mean=1.3723122380095796 StdDev=0.09924891305026087 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3723122380095796 StdDev=0.09924891305026087 @@ -1133,10 +1265,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7075 MASE_Forecast=0.7052 MASE_Test=0.6728 INFO:pyaf.std:MODEL_L1 L1_Fit=0.0786245768379836 L1_Forecast=0.08004108897773692 L1_Test=0.07582885539314255 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09872550233694065 L2_Forecast=0.10131663847786716 L2_Test=0.09837997235308765 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3722914090203804 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.4128057956695557 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.2633278369903564 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1170,31 +1311,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-06-25 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-06-25 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 12988 }, - "Training_Signal_Length": 12988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.08004108897773692", - "MAPE": "0.0588", - "MASE": "0.7052", - "RMSE": "0.10131663847786716" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.08004108897773692", + "MAPE": "0.0588", + "MASE": "0.7052", + "RMSE": "0.10131663847786716" + } } } @@ -1211,7 +1354,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 14000 2 GENERATING_RANDOM_DATASET Signal_14000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 22.79624056816101 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 68.80672025680542 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-04-11T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=13988 Min=1.0 Max=1.7541760852918893 Mean=1.37251295303514 StdDev=0.09915405995557493 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.37251295303514 StdDev=0.09915405995557493 @@ -1226,10 +1369,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7088 MASE_Forecast=0.6957 MASE_Test=0.8333 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07873530238661056 L1_Forecast=0.07914344575308124 L1_Test=0.07305906381355454 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09891789466406713 L2_Forecast=0.10011429846120641 L2_Test=0.08318522648581558 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3726447668888382 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.064413070678711 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 2.120952844619751 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1263,31 +1415,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-08-05 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-08-05 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 13988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 13988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07914344575308124", - "MAPE": "0.0583", - "MASE": "0.6957", - "RMSE": "0.10011429846120641" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07914344575308124", + "MAPE": "0.0583", + "MASE": "0.6957", + "RMSE": "0.10011429846120641" + } } } @@ -1304,7 +1458,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 15000 2 GENERATING_RANDOM_DATASET Signal_15000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 18.835888385772705 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 73.80287528038025 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-05-14T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=14988 Min=1.0 Max=1.7541760852918893 Mean=1.3729080342742703 StdDev=0.09889497643566447 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3729080342742703 StdDev=0.09889497643566447 @@ -1319,10 +1473,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7078 MASE_Forecast=0.7076 MASE_Test=2.9316 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07879031786285325 L1_Forecast=0.07810643813318749 L1_Test=0.1124902896609492 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09902499798499524 L2_Forecast=0.09834564957998833 L2_Test=0.121784338801257 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3723944760605704 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.28987717628479 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 3.1840343475341797 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1356,31 +1519,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-09-16 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-09-16 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 14988 }, - "Training_Signal_Length": 14988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07810643813318749", - "MAPE": "0.0572", - "MASE": "0.7076", - "RMSE": "0.09834564957998833" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07810643813318749", + "MAPE": "0.0572", + "MASE": "0.7076", + "RMSE": "0.09834564957998833" + } } } @@ -1397,7 +1562,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 16000 2 GENERATING_RANDOM_DATASET Signal_16000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 18.044116973876953 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 79.52713251113892 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-06-16T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=15988 Min=1.0 Max=1.7541760852918893 Mean=1.3730354927008195 StdDev=0.09893226904255123 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3730354927008195 StdDev=0.09893226904255123 @@ -1412,10 +1577,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7071 MASE_Forecast=0.7079 MASE_Test=0.5914 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07880633163069527 L1_Forecast=0.07834971194379974 L1_Test=0.11832436771392463 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09907442608079217 L2_Forecast=0.09831932322312618 L2_Test=0.13944552457315665 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3721681651288564 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0569872856140137 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.103804349899292 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1449,31 +1623,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-10-28 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-10-28 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 15988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 15988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07834971194379974", - "MAPE": "0.0573", - "MASE": "0.7079", - "RMSE": "0.09831932322312618" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07834971194379974", + "MAPE": "0.0573", + "MASE": "0.7079", + "RMSE": "0.09831932322312618" + } } } @@ -1490,7 +1666,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 17000 2 GENERATING_RANDOM_DATASET Signal_17000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 14.497398376464844 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 68.09179353713989 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-07-20T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=16988 Min=1.0 Max=1.7541760852918893 Mean=1.3736146146811243 StdDev=0.09886833866154503 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3736146146811243 StdDev=0.09886833866154503 @@ -1505,10 +1681,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7057 MASE_Forecast=0.7177 MASE_Test=0.5689 INFO:pyaf.std:MODEL_L1 L1_Fit=0.0788003762300406 L1_Forecast=0.07832046470566557 L1_Test=0.0692940108431106 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09909704747160511 L2_Forecast=0.09801459553543301 L2_Test=0.07718909603868106 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3723100749415975 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0637331008911133 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.7872989177703857 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1542,31 +1727,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2001-12-08 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-12-08 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 16988 }, - "Training_Signal_Length": 16988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07832046470566557", - "MAPE": "0.0571", - "MASE": "0.7177", - "RMSE": "0.09801459553543301" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07832046470566557", + "MAPE": "0.0571", + "MASE": "0.7177", + "RMSE": "0.09801459553543301" + } } } @@ -1583,7 +1770,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 18000 2 GENERATING_RANDOM_DATASET Signal_18000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 15.494671106338501 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 63.6625337600708 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-08-22T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=17988 Min=1.0 Max=1.7541760852918893 Mean=1.373439890044498 StdDev=0.09898218379134478 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.373439890044498 StdDev=0.09898218379134478 @@ -1598,10 +1785,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7071 MASE_Forecast=0.7116 MASE_Test=0.4882 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07876322141537849 L1_Forecast=0.07936137593484287 L1_Test=0.06079538865782341 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09907675940328484 L2_Forecast=0.0986288486962879 L2_Test=0.08205660432560209 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3728584625871438 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.2180514335632324 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.6491129398345947 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1635,31 +1831,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-01-19 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-01-19 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 17988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 17988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07936137593484287", - "MAPE": "0.0582", - "MASE": "0.7116", - "RMSE": "0.0986288486962879" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07936137593484287", + "MAPE": "0.0582", + "MASE": "0.7116", + "RMSE": "0.0986288486962879" + } } } @@ -1676,7 +1874,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 19000 2 GENERATING_RANDOM_DATASET Signal_19000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 17.442260026931763 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 64.8749828338623 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-09-24T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=18988 Min=1.0 Max=1.7541760852918893 Mean=1.3733133853431232 StdDev=0.09891692702493152 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.7541760852918893 Mean=1.3733133853431232 StdDev=0.09891692702493152 @@ -1691,10 +1889,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7087 MASE_Forecast=0.704 MASE_Test=0.9205 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07878542780391853 L1_Forecast=0.07886216370699059 L1_Test=0.057263474599044906 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09900594287273545 L2_Forecast=0.09859304821015864 L2_Test=0.06483150368367378 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3729614713201188 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.2256388664245605 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.6442656517028809 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1728,31 +1935,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-03-02 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-03-02 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 18988 }, - "Training_Signal_Length": 18988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07886216370699059", - "MAPE": "0.0579", - "MASE": "0.704", - "RMSE": "0.09859304821015864" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07886216370699059", + "MAPE": "0.0579", + "MASE": "0.704", + "RMSE": "0.09859304821015864" + } } } @@ -1769,7 +1978,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 20000 2 GENERATING_RANDOM_DATASET Signal_20000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 16.946390390396118 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 67.11559963226318 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-10-28T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=19988 Min=1.0 Max=1.8248292456326407 Mean=1.4442612260561516 StdDev=0.09902327366225308 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.8248292456326407 Mean=1.4442612260561516 StdDev=0.09902327366225308 @@ -1784,10 +1993,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7073 MASE_Forecast=0.7092 MASE_Test=1.1824 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07872916638445125 L1_Forecast=0.07924233589836825 L1_Test=0.17661568091626856 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09893366020486016 L2_Forecast=0.09926718253483441 L2_Test=0.1843634970301981 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4436932438702816 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.230530023574829 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.974985122680664 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1821,31 +2039,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-04-12 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-04-12 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 19988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 19988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07924233589836825", - "MAPE": "0.0552", - "MASE": "0.7092", - "RMSE": "0.09926718253483441" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07924233589836825", + "MAPE": "0.0552", + "MASE": "0.7092", + "RMSE": "0.09926718253483441" + } } } @@ -1862,7 +2082,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 21000 2 GENERATING_RANDOM_DATASET Signal_21000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 13.10918116569519 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 52.036991357803345 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-11-30T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=20988 Min=1.0 Max=1.8248292456326407 Mean=1.4444177546259254 StdDev=0.09904772644784893 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.8248292456326407 Mean=1.4444177546259254 StdDev=0.09904772644784893 @@ -1877,10 +2097,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7078 MASE_Forecast=0.7042 MASE_Test=0.6044 INFO:pyaf.std:MODEL_L1 L1_Fit=0.0786935088664862 L1_Forecast=0.07947481060474017 L1_Test=0.050232012173868856 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09888038284513427 L2_Forecast=0.09974148030925464 L2_Test=0.06589266930329672 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4442086098558904 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.178981065750122 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.4980690479278564 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -1914,31 +2143,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-05-24 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-05-24 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 20988 }, - "Training_Signal_Length": 20988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07947481060474017", - "MAPE": "0.0555", - "MASE": "0.7042", - "RMSE": "0.09974148030925464" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07947481060474017", + "MAPE": "0.0555", + "MASE": "0.7042", + "RMSE": "0.09974148030925464" + } } } @@ -1955,7 +2186,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 22000 2 GENERATING_RANDOM_DATASET Signal_22000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 13.872032165527344 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 42.44377684593201 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-01-02T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=21988 Min=1.0 Max=1.8248292456326407 Mean=1.4445523833778635 StdDev=0.09896124420297311 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.8248292456326407 Mean=1.4445523833778635 StdDev=0.09896124420297311 @@ -1970,10 +2201,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7083 MASE_Forecast=0.7052 MASE_Test=1.0793 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07888193358391257 L1_Forecast=0.07843482485507453 L1_Test=0.08001676942310643 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09899966609679865 L2_Forecast=0.09881838154869832 L2_Test=0.09073869359975172 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4441656031121766 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.3626151084899902 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.2356338500976562 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -2007,31 +2247,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-07-05 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-07-05 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 21988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 21988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07843482485507453", - "MAPE": "0.0548", - "MASE": "0.7052", - "RMSE": "0.09881838154869832" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07843482485507453", + "MAPE": "0.0548", + "MASE": "0.7052", + "RMSE": "0.09881838154869832" + } } } @@ -2048,7 +2290,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 23000 2 GENERATING_RANDOM_DATASET Signal_23000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 14.16695237159729 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 42.14847373962402 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-02-05T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=22988 Min=1.0 Max=1.827842240376035 Mean=1.444505672391023 StdDev=0.0991518412598356 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.827842240376035 Mean=1.444505672391023 StdDev=0.0991518412598356 @@ -2063,10 +2305,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7081 MASE_Forecast=0.7037 MASE_Test=0.8836 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07884730498139066 L1_Forecast=0.0789547554014068 L1_Test=0.1771693990089318 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09893711145156023 L2_Forecast=0.09984963641409 L2_Test=0.216811296157201 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4440415463689749 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.3180651664733887 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.3132445812225342 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -2100,31 +2351,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-08-15 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-08-15 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 22988 }, - "Training_Signal_Length": 22988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.0789547554014068", - "MAPE": "0.0551", - "MASE": "0.7037", - "RMSE": "0.09984963641409" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.0789547554014068", + "MAPE": "0.0551", + "MASE": "0.7037", + "RMSE": "0.09984963641409" + } } } @@ -2141,7 +2394,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 24000 2 GENERATING_RANDOM_DATASET Signal_24000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 13.955019474029541 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 45.62004065513611 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-03-10T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=23988 Min=1.0 Max=1.827842240376035 Mean=1.4444626525457447 StdDev=0.09915452537109536 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.827842240376035 Mean=1.4444626525457447 StdDev=0.09915452537109536 @@ -2156,10 +2409,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7077 MASE_Forecast=0.7029 MASE_Test=0.8886 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07877352488725974 L1_Forecast=0.07927639966097866 L1_Test=0.05274495242952981 INFO:pyaf.std:MODEL_L2 L2_Fit=0.0989253581801834 L2_Forecast=0.10009233641400067 L2_Test=0.06804838349832949 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4440256856487466 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.5043883323669434 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.6246912479400635 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -2193,31 +2455,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-09-26 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-09-26 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 23988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 23988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07927639966097866", - "MAPE": "0.0553", - "MASE": "0.7029", - "RMSE": "0.10009233641400067" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07927639966097866", + "MAPE": "0.0553", + "MASE": "0.7029", + "RMSE": "0.10009233641400067" + } } } @@ -2234,7 +2498,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 25000 2 GENERATING_RANDOM_DATASET Signal_25000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 16.478989362716675 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 42.930967807769775 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-04-12T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=24988 Min=1.0 Max=1.827842240376035 Mean=1.4444249509558438 StdDev=0.09907964085829919 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.827842240376035 Mean=1.4444249509558438 StdDev=0.09907964085829919 @@ -2249,10 +2513,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7078 MASE_Forecast=0.7006 MASE_Test=0.6235 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07884702883835266 L1_Forecast=0.07870084106400965 L1_Test=0.0954034663958675 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09901541943544223 L2_Forecast=0.09933203404105737 L2_Test=0.10494912768313834 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4442511085749927 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.5896415710449219 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.3714799880981445 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -2286,31 +2559,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-11-07 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-11-07 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 24988 }, - "Training_Signal_Length": 24988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07870084106400965", - "MAPE": "0.055", - "MASE": "0.7006", - "RMSE": "0.09933203404105737" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07870084106400965", + "MAPE": "0.055", + "MASE": "0.7006", + "RMSE": "0.09933203404105737" + } } } @@ -2327,7 +2602,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 26000 2 GENERATING_RANDOM_DATASET Signal_26000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 15.429480791091919 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 43.85330843925476 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-05-16T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=25988 Min=1.0 Max=1.827842240376035 Mean=1.4443972763721904 StdDev=0.09911037967856144 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.827842240376035 Mean=1.4443972763721904 StdDev=0.09911037967856144 @@ -2342,10 +2617,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7068 MASE_Forecast=0.7082 MASE_Test=1.4226 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07887449262005654 L1_Forecast=0.07880320007191213 L1_Test=0.06486414893816339 INFO:pyaf.std:MODEL_L2 L2_Fit=0.0990892294482006 L2_Forecast=0.09921497467989297 L2_Test=0.06827112121392744 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4444247589398462 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.6138272285461426 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.6961660385131836 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -2379,31 +2663,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-12-18 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-12-18 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 25988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 25988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07880320007191213", - "MAPE": "0.0551", - "MASE": "0.7082", - "RMSE": "0.09921497467989297" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07880320007191213", + "MAPE": "0.0551", + "MASE": "0.7082", + "RMSE": "0.09921497467989297" + } } } @@ -2420,7 +2706,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 27000 2 GENERATING_RANDOM_DATASET Signal_27000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 14.923411130905151 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 45.975135803222656 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-06-18T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=26988 Min=1.0 Max=1.827842240376035 Mean=1.4444109165841574 StdDev=0.09916567640014913 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.827842240376035 Mean=1.4444109165841574 StdDev=0.09916567640014913 @@ -2435,10 +2721,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7074 MASE_Forecast=0.7035 MASE_Test=1.0472 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07882877229621851 L1_Forecast=0.07926071851582213 L1_Test=0.09586926562496179 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09902902031559667 L2_Forecast=0.09970110683629041 L2_Test=0.11107294628742155 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4443925903600903 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.5148835182189941 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.6270530223846436 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -2472,31 +2767,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-01-29 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-01-29 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 26988 }, - "Training_Signal_Length": 26988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07926071851582213", - "MAPE": "0.0554", - "MASE": "0.7035", - "RMSE": "0.09970110683629041" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07926071851582213", + "MAPE": "0.0554", + "MASE": "0.7035", + "RMSE": "0.09970110683629041" + } } } @@ -2513,7 +2810,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 28000 2 GENERATING_RANDOM_DATASET Signal_28000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 16.170550107955933 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 42.5651695728302 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-07-21T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=27988 Min=1.0 Max=1.827842240376035 Mean=1.4443821584231487 StdDev=0.09919303637735678 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.827842240376035 Mean=1.4443821584231487 StdDev=0.09919303637735678 @@ -2528,10 +2825,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7075 MASE_Forecast=0.7012 MASE_Test=0.9128 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07884344226554545 L1_Forecast=0.07935941004584059 L1_Test=0.07691519830128513 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09903438709958272 L2_Forecast=0.09983590725963652 L2_Test=0.08319253850379515 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4445049463404567 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.7034952640533447 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.6846888065338135 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -2565,31 +2871,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-03-12 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-03-12 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 27988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 27988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07935941004584059", - "MAPE": "0.0555", - "MASE": "0.7012", - "RMSE": "0.09983590725963652" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07935941004584059", + "MAPE": "0.0555", + "MASE": "0.7012", + "RMSE": "0.09983590725963652" + } } } @@ -2606,7 +2914,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 29000 2 GENERATING_RANDOM_DATASET Signal_29000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 16.656190395355225 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 47.58840084075928 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-08-24T02:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=28988 Min=1.0 Max=1.8491743129800016 Mean=1.4656312357930716 StdDev=0.09924363935327768 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.8491743129800016 Mean=1.4656312357930716 StdDev=0.09924363935327768 @@ -2621,10 +2929,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7071 MASE_Forecast=0.7045 MASE_Test=1.1742 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07884899404149656 L1_Forecast=0.0794837978849208 L1_Test=0.10139503466730293 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09911179864741376 L2_Forecast=0.099762260361262 L2_Test=0.1112211764707545 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.465894768998823 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.6637470722198486 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.6707019805908203 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -2658,31 +2975,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-04-22 19:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-04-22 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 28988 }, - "Training_Signal_Length": 28988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.0794837978849208", - "MAPE": "0.0549", - "MASE": "0.7045", - "RMSE": "0.099762260361262" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.0794837978849208", + "MAPE": "0.0549", + "MASE": "0.7045", + "RMSE": "0.099762260361262" + } } } @@ -2699,7 +3018,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 30000 2 GENERATING_RANDOM_DATASET Signal_30000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 17.016258716583252 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 56.944111824035645 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-09-26T10:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=29988 Min=1.0 Max=1.8491743129800016 Mean=1.4655412291710663 StdDev=0.09926381673276578 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.8491743129800016 Mean=1.4655412291710663 StdDev=0.09926381673276578 @@ -2714,10 +3033,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7067 MASE_Forecast=0.7071 MASE_Test=1.0728 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07886812249701938 L1_Forecast=0.07961297318518705 L1_Test=0.11400055199410453 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09915332901236772 L2_Forecast=0.09967975988950091 L2_Test=0.13325892514933727 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4657999190720326 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.7791211605072021 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.655552625656128 INFO:pyaf.std:START_TRAINING 'Signal' Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', @@ -2751,31 +3079,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-06-03 11:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-06-03 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 29988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 29988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.07961297318518705", - "MAPE": "0.055", - "MASE": "0.7071", - "RMSE": "0.09967975988950091" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.07961297318518705", + "MAPE": "0.055", + "MASE": "0.7071", + "RMSE": "0.09967975988950091" + } } } @@ -2792,7 +3122,7 @@ TEST_CYCLES_END 2 TEST_CYCLES_START 31000 2 GENERATING_RANDOM_DATASET Signal_31000_H_0_constant_2_None_0.1_0 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 17.65751361846924 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 68.47109007835388 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-10-29T18:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=30988 Min=1.0 Max=1.8491743129800016 Mean=1.4655115960212783 StdDev=0.09944687104033499 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.8491743129800016 Mean=1.4655115960212783 StdDev=0.09944687104033499 @@ -2807,10 +3137,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7066 MASE_Forecast=0.7103 MASE_Test=0.6953 INFO:pyaf.std:MODEL_L1 L1_Fit=0.07883624281815105 L1_Forecast=0.08033214154023202 L1_Test=0.07104460504786325 INFO:pyaf.std:MODEL_L2 L2_Fit=0.09911739554984146 L2_Forecast=0.10074164845298081 L2_Test=0.11821649690194826 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4656898851607476 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.9284846782684326 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.7748610973358154 Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', '_Signal_ConstantTrend_residue_zeroCycle', @@ -2843,31 +3182,33 @@ Forecasts { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-07-15 03:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 4, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-07-15 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 30988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 30988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.08033214154023202", - "MAPE": "0.0555", - "MASE": "0.7103", - "RMSE": "0.10074164845298081" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "0.08033214154023202", + "MAPE": "0.0555", + "MASE": "0.7103", + "RMSE": "0.10074164845298081" + } } } diff --git a/tests/references/perf_test_ozone_ar_speed.log b/tests/references/perf_test_ozone_ar_speed.log index ba5897f38..8c118967e 100644 --- a/tests/references/perf_test_ozone_ar_speed.log +++ b/tests/references/perf_test_ozone_ar_speed.log @@ -29,461 +29,1230 @@ min 1.200000 75% 4.825000 max 8.700000 Name: Ozone, dtype: float64 -INFO:pyaf.std:TREND_TIME_IN_SECONDS Diff_Ozone 0.2839517593383789 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:TREND_TIME_IN_SECONDS _Ozone 0.3287222385406494 -INFO:pyaf.std:TREND_TIME_IN_SECONDS CumSum_Ozone 0.39771366119384766 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byL2' 0.31 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' 0.05 +INFO:pyaf.std:TREND_TIME_IN_SECONDS _Ozone 0.3789534568786621 +INFO:pyaf.std:TREND_TIME_IN_SECONDS RelDiff_Ozone 0.2544698715209961 +INFO:pyaf.std:TREND_TIME_IN_SECONDS CumSum_Ozone 0.20190143585205078 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle' 0.01 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear' 0.07 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_bestCycle_byL2' 0.18 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth' 0.08 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' 0.07 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek' 0.05 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear' 0.04 -INFO:pyaf.std:TREND_TIME_IN_SECONDS RelDiff_Ozone 0.24864673614501953 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2' 0.21 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_zeroCycle' 0.01 +INFO:pyaf.std:TREND_TIME_IN_SECONDS Diff_Ozone 0.3126535415649414 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_zeroCycle' 0.02 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE' 0.1 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE' 0.28 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_bestCycle_byMAPE' 0.19 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear' 0.06 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' 0.06 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth' 0.05 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE' 0.11 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth' 0.03 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth' 0.03 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear' 0.03 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear' 0.03 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth' 0.03 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek' 0.05 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_bestCycle_byL2' 0.18 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byL2' 0.27 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE' 0.13 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth' 0.05 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_bestCycle_byMAPE' 0.1 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE' 0.1 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear' 0.03 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2' 0.17 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear' 0.03 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear' 0.03 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear' 0.06 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear' 0.03 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth' 0.03 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear' 0.05 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE' 0.11 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INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.885572910308838 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1588110932.3061917 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 4.327857732772827 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.3043599128723145 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_ConstantTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.6637704372406006 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.1024794578552246 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1588110932.8073566 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.54 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1588110932.875121 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 4.463404417037964 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596040312.1677449 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596040312.1722329 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1588110933.1159673 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 4.661221265792847 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596040312.3808508 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596040312.3846316 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.7789273262023926 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1588110933.2219763 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.44 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1588110933.2460482 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.42 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1588110933.6461582 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.62 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1588110933.737614 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 30.63 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 29.85 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 31.2 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 32.03 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.068336248397827 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 0 1588110966.5942736 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.37 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 1000 1588110966.9666343 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 4.3744282722473145 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 0 1588110968.0022893 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.57 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 1000 1588110968.5741987 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 4.157671928405762 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 0 1588110969.0276594 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 4.118244647979736 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 0 1588110969.4223275 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.49 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 1000 1588110969.5175972 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.44 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue' 10200 1000 1588110969.8651996 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 25.0 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 23.66 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596040312.473332 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596040312.4769304 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596040312.7098076 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596040312.7136698 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 20.13 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 20.29 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 21.75 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.2885894775390625 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 22.68 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040335.1587017 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040335.1617963 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.7458584308624268 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040335.9964035 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040336.000491 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.9465482234954834 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.1802005767822266 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040337.2916653 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040337.295574 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040337.5292912 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040337.533136 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 19.1 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 22.77 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 22.77 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 19.82 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.433814525604248 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588110994.4103765 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.28 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588110994.6919677 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.558576822280884 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 21.12 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 22.65 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.323432445526123 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588110994.8102474 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.7810285091400146 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588110995.108348 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.43 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588110995.2398443 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.23 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588110995.3413317 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 3.030200719833374 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040359.2443485 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040359.2474694 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.722188949584961 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588110995.6929576 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.34 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588110996.0368547 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 22.64 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 24.27 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040360.101729 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040360.105901 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.229313850402832 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040360.722807 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040360.7325375 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.433290481567383 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040361.6466317 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040361.6542735 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 19.9 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 25.08 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.9635250568389893 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1588111019.9543622 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.2 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1588111020.1540778 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 24.37 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 19.53 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.4250271320343018 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.4020838737487793 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1588111022.94305 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.32 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1588111023.2650456 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.55509090423584 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1588111023.3419864 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.31 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1588111023.6568522 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 4.120205402374268 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596040381.776747 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596040381.7803204 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.294100522994995 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1588111024.5578558 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.31 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1588111024.867974 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 25.06 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 24.91 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.984760046005249 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 0 1588111048.2224112 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.26 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 1000 1588111048.4850943 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 26.88 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.1281185150146484 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 0 1588111051.3248322 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 26.51 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.32 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 1000 1588111051.6489632 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.795430898666382 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 0 1588111053.3553333 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.43 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 1000 1588111053.7898812 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 3.052802562713623 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 0 1588111054.4423718 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.3 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_bestCycle_byL2_residue' 10200 1000 1588111054.7411263 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 25.39 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 21.4 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596040382.2379346 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596040382.2420735 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 23.08 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 22.83 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.7192046642303467 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040386.8420188 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040386.8454149 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.59816575050354 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040387.3688486 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040387.373014 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 16.92 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.833404779434204 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596040400.8604894 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040400.863919 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.23 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.8914897441864014 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596040403.564928 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040403.567928 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 17.66 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 18.99 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.4259979724884033 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040408.3354173 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040408.3510942 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.3143746852874756 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040410.1957753 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040410.199317 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 25.04 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 23.24 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.466141700744629 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040428.625351 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040428.629379 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.5903165340423584 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040429.6267967 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040429.6319177 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 24.23 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 25.04 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.9661319255828857 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040435.8682761 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040435.8728013 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.8251826763153076 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040438.342883 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040438.3470232 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 20.83 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 21.92 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.659019947052002 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040453.3663104 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040453.3697581 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.7159457206726074 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040454.7880664 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040454.7927897 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 20.29 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 20.63 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.825521469116211 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040459.3897326 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040459.3963625 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.9296810626983643 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040462.179418 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040462.183291 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 19.23 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 19.31 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.840743064880371 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040474.6659305 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040474.669122 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.68318510055542 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040476.0088527 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040476.0111856 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 19.0 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 17.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.8594675064086914 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040481.5502768 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040481.5540502 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.7343246936798096 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040482.3478794 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040482.3539517 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 18.58 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 24.82 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 17.29 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.1291840076446533 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588111077.012737 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.3 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111077.3133147 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 24.44 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 3.298834800720215 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588111079.4681523 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.2543718814849854 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.396247386932373 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588111079.7451265 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.31 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111079.7781985 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.46 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111080.2008228 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.2362771034240723 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588111081.4805017 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.42 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111081.9050202 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 23.99 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.3974530696868896 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1588111103.7089715 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.28 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1588111103.991351 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 24.74 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040496.0200706 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040496.0272393 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.843369960784912 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040496.4096255 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040496.4137526 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 22.82 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 23.27 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.693547248840332 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040507.4080493 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040507.4136703 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.7857258319854736 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040508.746335 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040508.7505982 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 16.38 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.39 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.021821975708008 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) 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'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040516.2982614 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 24.01 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 17.49 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 24.34 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.769804000854492 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596040534.4588623 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596040534.4621058 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.4100515842437744 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040535.253199 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040535.2579687 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 19.33 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.2454147338867188 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596040536.6186178 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596040536.6221242 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.9149155616760254 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040538.884528 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040538.8930116 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 20.04 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.8 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.2281434535980225 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596040557.0623937 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040557.0667021 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 22.73 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.174940586090088 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596040558.8020675 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040558.8044875 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.074044942855835 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040560.2918088 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040560.295324 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 22.26 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.9153664112091064 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040563.2938092 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040563.2974803 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 17.04 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 17.57 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.687365770339966 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040577.1440742 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040577.1490767 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 18.21 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 24.97 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 23.85 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.2605555057525635 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.0162320137023926 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040579.703025 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040579.7090352 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.442737340927124 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1588111106.793313 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.22 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1588111107.0139282 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.5041515827178955 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596040581.2041771 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596040581.2087693 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 19.16 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.6437015533447266 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1588111107.7013328 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.32 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1588111108.0199263 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.6792399883270264 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1588111108.4564705 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.25 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1588111108.7026262 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.58 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.707838773727417 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue' 10200 0 1588111126.2950006 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.19 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue' 10200 1000 1588111126.4901233 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.86 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.14 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.2 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 3.084916353225708 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 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'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 22.42 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 22.39 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.906980037689209 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040629.185006 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 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'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040672.0283375 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040672.031736 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 16.3 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 23.77 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 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-INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111156.9175117 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.38 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111157.2177193 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.8080813884735107 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588111158.2176058 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.23 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111158.450144 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 18.54 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.682349443435669 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1588111171.6043026 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.17 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1588111171.7715712 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 16.38 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040694.4969664 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040694.5015469 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 21.87 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 22.32 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 22.19 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0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040710.7208352 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040710.7245712 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.276689052581787 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040711.3122249 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'_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040732.773061 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 25.91 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 27.48 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 21.6 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 4.232936382293701 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040741.1483927 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040741.1542845 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.9731791019439697 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 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'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040771.7202206 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040771.723706 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 33.61 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.429553270339966 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596040778.7519555 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596040778.7564569 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 36.91 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 20.94 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.09 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.2067999839782715 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.677426815032959 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596040784.7427392 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596040784.7473414 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.795532703399658 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1588111175.514307 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.28 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1588111175.7983284 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 18.28 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.6954596042633057 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596040785.3709395 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.01 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'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1588111177.3106835 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.8963027000427246 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1588111179.6500137 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.32 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1588111179.9720492 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 22.16 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.78 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 18.61 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.0904083251953125 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 0 1588111196.0421176 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.2 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 1000 1588111196.2385721 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.3650949001312256 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 0 1588111197.2969656 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.17 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 1000 1588111197.466969 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.9257948398590088 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 0 1588111197.519534 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.22 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 1000 1588111197.7438164 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.48 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.200941801071167 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 0 1588111201.6624596 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR' 10200 0 0.24 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue' 10200 1000 1588111201.9003391 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 17.17 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 16.21 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 16.61 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596040797.2678256 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596040797.2710187 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 22.8 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 30.04 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.861513376235962 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040811.3542807 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040811.3706386 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.4602811336517334 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040812.5749176 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040812.5787988 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 30.88 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 20.79 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.7001307010650635 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040819.7770214 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040819.7941647 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.0818076133728027 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040820.364117 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040820.3673913 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 17.52 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.744513988494873 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040832.1721344 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040832.18766 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 22.48 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 30.61 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 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'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040846.8178823 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040846.8221319 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 28.95 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.125420570373535 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 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'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 26.66 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.4786932468414307 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040880.2641156 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040880.2731636 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.2817046642303467 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'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040882.2493546 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040882.2537634 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 16.68 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.031751871109009 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040889.1688175 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040889.1726365 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 17.6 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.9525091648101807 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588111216.3858194 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.9884636402130127 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588111216.6899943 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.37 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111216.7521083 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.5355112552642822 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.9179909229278564 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588111216.9066005 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.37 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111217.0554335 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.26 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111217.1716979 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(1000)' 10200 1000 18.25 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040902.2394364 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040902.2436442 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 22.36 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 20.46 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.995532751083374 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040904.948477 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040904.9522152 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.4637351036071777 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040905.4216266 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040905.4257631 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 19.31 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.18043851852417 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040911.9663286 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040911.97046 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 20.05 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 18.88 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.0435101985931396 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 19.04 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040924.5762336 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040924.579343 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.1279730796813965 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040926.2350354 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040926.2391477 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.287842035293579 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040928.1993628 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040928.2107105 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 16.29 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.3814754486083984 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040930.893687 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040930.8972242 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 19.06 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.5813283920288086 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040946.4942644 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040946.500665 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 23.05 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 20.14 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 23.86 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.837641954421997 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040953.569611 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040953.5857925 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.6018590927124023 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040953.934697 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040953.9380696 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.7609992027282715 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040956.258666 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040956.263057 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 20.45 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.238525152206421 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040969.4459195 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040969.4490232 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 19.29 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.6358776092529297 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040976.2183275 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040976.2248363 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 25.57 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 23.26 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.430424928665161 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040981.9451048 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040981.9495811 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.704497814178467 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040982.564058 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040982.569457 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 19.71 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS _Ozone 679.7576198577881 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 18.29 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS RelDiff_Ozone 684.8745217323303 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 18.61 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 18.53 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.5434675216674805 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596041003.3825166 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596041003.3866487 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.7112767696380615 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596041004.1011724 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596041004.1050956 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 24.16 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 25.52 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.5684025287628174 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596041032.2124972 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596041032.2287815 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.8322489261627197 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596041033.2204745 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596041033.225415 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 26.58 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 26.18 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.4775025844573975 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596041062.669101 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596041062.6727583 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.6013638973236084 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596041063.6814108 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596041063.6853917 +INFO:pyaf.std:PERF_TIME_IN_SECONDS _Ozone 60 91.37157034873962 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS _Ozone 774.755464553833 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 22.08 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 22.38 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.5391790866851807 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:PERF_TIME_IN_SECONDS RelDiff_Ozone 60 92.82952737808228 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS RelDiff_Ozone 781.5373413562775 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596041087.6107178 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596041087.6144104 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.013439655303955 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596041089.515869 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596041089.5195007 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 20.75 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 20.25 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9652419090270996 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-INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1588111222.4648004 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.34 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1588111222.8014786 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.03 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS _Ozone 305.8962895870209 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.62 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS RelDiff_Ozone 306.18378710746765 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 19.16 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS CumSum_Ozone 307.4715328216553 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 16.31 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS Diff_Ozone 310.8037819862366 -INFO:pyaf.std:PERF_TIME_IN_SECONDS CumSum_Ozone 24 23.990859508514404 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS CumSum_Ozone 336.1457631587982 -INFO:pyaf.std:PERF_TIME_IN_SECONDS RelDiff_Ozone 24 27.840720891952515 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS RelDiff_Ozone 338.76278853416443 -INFO:pyaf.std:PERF_TIME_IN_SECONDS _Ozone 24 29.42005491256714 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS _Ozone 339.87585258483887 -INFO:pyaf.std:PERF_TIME_IN_SECONDS Diff_Ozone 24 25.215431690216064 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS Diff_Ozone 340.58902740478516 -INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS _Ozone 0.014414072036743164 -INFO:pyaf.std:PREDICTION_INTERVAL_TIME_IN_SECONDS Ozone 20.522395133972168 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 361.8585464954376 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'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.776552677154541 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596041178.9733977 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596041178.9788914 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596041179.1912034 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596041179.1950362 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 21.19 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 21.82 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.696073055267334 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596041203.3479264 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596041203.3519275 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.57761549949646 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596041203.7496612 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1596041229.138291 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596041229.1543927 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 21.97 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS CumSum_Ozone 937.655433177948 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 20.22 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS Diff_Ozone 939.9128792285919 +INFO:pyaf.std:PERF_TIME_IN_SECONDS Diff_Ozone 72 89.05299735069275 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS Diff_Ozone 1032.6335008144379 +INFO:pyaf.std:PERF_TIME_IN_SECONDS CumSum_Ozone 72 94.63697648048401 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS CumSum_Ozone 1035.6705675125122 +INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone 0.01689457893371582 +INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone 0.012613296508789062 +INFO:pyaf.std:PREDICTION_INTERVAL_TIME_IN_SECONDS Ozone 22.779833555221558 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 1062.8499641418457 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -495,18 +1264,27 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0071 MAPE_Forecast=0.0004 MAPE_Test=0.0004 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0073 SMAPE_Forecast=0.0004 SMAPE_Test=0.0004 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0289 MASE_Forecast=0.0014 MASE_Test=0.0022 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.024341607561003002 L1_Forecast=0.0012143771093255022 L1_Test=0.0010176601919480073 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.16533248972314277 L2_Forecast=0.0015159356036940383 L2_Test=0.0012241971783861797 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.024341607561002503 L1_Forecast=0.0012143771093251479 L1_Test=0.0010176601919387556 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.1653324897231422 L2_Forecast=0.001515935603693765 L2_Test=0.0012241971783758024 INFO:pyaf.std:MODEL_COMPLEXITY 1000 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9012013699597639 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3787201583811607 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3435086136424871 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.33553103277450225 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3283621215177107 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32759195472040936 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2924928063681673 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2895709952329606 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.2790233903071546 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.2742822539889601 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9012013699597679 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3787201583811708 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.34350861364249025 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.33553103277450397 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.32836212151770894 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3275919547204088 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.29249280636816494 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2895709952329676 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.2790233903071537 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.27428225398896344 INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_many.log b/tests/references/perf_test_ozone_ar_speed_many.log index d977f6475..40a561fee 100644 --- a/tests/references/perf_test_ozone_ar_speed_many.log +++ b/tests/references/perf_test_ozone_ar_speed_many.log @@ -29,25 +29,34 @@ min 1.200000 75% 4.825000 max 8.700000 Name: Ozone, dtype: float64 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 11.923637628555298 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 14.992173671722412 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [Lag1Trend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_Lag1Trend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2079 MAPE_Forecast=0.2079 MAPE_Test=0.1912 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2045 SMAPE_Forecast=0.2045 SMAPE_Test=0.2035 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8711 MASE_Forecast=0.8708 MASE_Test=0.9005 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7347547208118589 L1_Forecast=0.7348750468338849 L1_Test=0.42570795070605555 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.9493504051320051 L2_Forecast=0.949411259911352 L2_Test=0.47470650799474295 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=39125.00000000028 Mean=19605.363725490337 StdDev=11294.348459632758 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' +INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [Lag1Trend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL 'CumSum_Ozone_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2167 MAPE_Forecast=0.2166 MAPE_Test=0.1912 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2147 SMAPE_Forecast=0.2146 SMAPE_Test=0.2239 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9142 MASE_Forecast=0.9136 MASE_Test=0.899 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7711411042944856 L1_Forecast=0.7710009813544707 L1_Test=0.42500000000048516 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.017215011867223 L2_Forecast=1.0169394519200639 L2_Test=0.5074445782552324 +INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 2.7 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE 12 3.7999999999992724 {0: 2.099999999998545, 1: 2.400000000000091, 2: 2.7000000000007276, 3: 3.7999999999992724, 4: 3.7999999999992724, 5: 4.299999999999272, 6: 4.900000000001455, 7: 5.0, 8: 4.799999999999272, 9: 4.799999999999272, 10: 2.7999999999992724, 11: 2.2000000000007276} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 21.178611278533936 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 30.85416007041931 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -59,23 +68,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1652 MAPE_Forecast=0.1653 MAPE_Test=0.1903 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1595 SMAPE_Forecast=0.1596 SMAPE_Test=0.1768 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6901 MASE_Forecast=0.6898 MASE_Test=0.9253 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.5820898625007142 L1_Forecast=0.5821675672022341 L1_Test=0.4373917755728307 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.7784586364606513 L2_Forecast=0.7781974157435857 L2_Test=0.5140916849060597 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.5820898625007142 L1_Forecast=0.5821675672022341 L1_Test=0.4373917755728303 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.7784586364606513 L2_Forecast=0.7781974157435858 L2_Test=0.5140916849060585 INFO:pyaf.std:MODEL_COMPLEXITY 50 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.46716516523133866 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.1659120210828614 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.15982183881941514 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.14171716754711552 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag2 0.14091104017597741 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.14000940091746975 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag48 0.12963386728695672 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1211252731468438 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.12091149023600938 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag37 0.11935964969240712 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.46716516523134277 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.16591202108286163 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.15982183881941225 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.1417171675471157 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1409110401759769 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.14000940091747033 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag48 0.12963386728695633 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1211252731468459 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.12091149023601114 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag37 0.1193596496924073 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 36.07548213005066 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 49.27895402908325 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -87,79 +105,106 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1376 MAPE_Forecast=0.1374 MAPE_Test=0.2341 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1347 SMAPE_Forecast=0.1345 SMAPE_Test=0.2311 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5747 MASE_Forecast=0.5732 MASE_Test=1.2078 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.48477473744307 L1_Forecast=0.4837075610510262 L1_Test=0.5709826667757336 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.6465396535544337 L2_Forecast=0.6438252890953696 L2_Test=0.7128249778075042 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.4847747374430702 L1_Forecast=0.4837075610510263 L1_Test=0.5709826667757363 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.6465396535544338 L2_Forecast=0.6438252890953697 L2_Test=0.7128249778075041 INFO:pyaf.std:MODEL_COMPLEXITY 100 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4730850204107154 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.318617926646958 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.23866357449829492 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.22757561427267167 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag87 0.2065869380994289 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.19716488770967833 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag97 0.17935881914959176 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.17874157123013884 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag4 0.16990491275172734 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1671993718528206 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.47308502041071965 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.3186179266469543 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.23866357449829342 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.22757561427267176 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag87 0.20658693809942721 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.19716488770967844 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag97 0.17935881914959179 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.1787415712301379 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag4 0.16990491275172798 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.16719937185282224 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 39.84346675872803 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 70.77789783477783 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(150)' [ConstantTrend + Cycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(150)' [ConstantTrend + NoCycle + AR] INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(150)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1027 MAPE_Forecast=0.1013 MAPE_Test=0.2119 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.102 SMAPE_Forecast=0.1002 SMAPE_Test=0.2013 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4098 MASE_Forecast=0.4032 MASE_Test=1.0145 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.3456159915634797 L1_Forecast=0.34027356075488663 L1_Test=0.479601875090267 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.4521190389503564 L2_Forecast=0.43970827071284385 L2_Test=0.5647903200212232 -INFO:pyaf.std:MODEL_COMPLEXITY 158 +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(150)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1151 MAPE_Forecast=0.114 MAPE_Test=0.2296 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1131 SMAPE_Forecast=0.1122 SMAPE_Test=0.2202 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4806 MASE_Forecast=0.4759 MASE_Test=1.1891 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.40537524131974373 L1_Forecast=0.4015882302738338 L1_Test=0.5621372840947728 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.5311394579412342 L2_Forecast=0.5237706567242836 L2_Test=0.6545460221337277 +INFO:pyaf.std:MODEL_COMPLEXITY 150 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag96 -0.5447715446068946 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag1 0.4824828023739818 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag140 0.3538105691397554 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag44 0.3388603083044224 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag108 -0.3114535532497428 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag7 -0.3005605491798145 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag46 -0.2831437425829137 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag37 0.26729215147476365 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag36 -0.26711223427845293 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag85 -0.26611084675926366 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5947151721662776 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.3859792149061864 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag97 0.3470922572809153 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag44 0.32389308559970453 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag88 -0.3054561110151719 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.30297756491043626 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag140 0.27935923649387895 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag128 -0.2781261838662811 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.27636844903312185 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag110 -0.2738335556231832 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 49.773016691207886 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 98.1719024181366 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(200)' [ConstantTrend + Cycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(200)' [ConstantTrend + NoCycle + AR] INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(200)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0347 MAPE_Forecast=0.0294 MAPE_Test=0.0332 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0345 SMAPE_Forecast=0.0293 SMAPE_Test=0.0324 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1386 MASE_Forecast=0.117 MASE_Test=0.1706 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.11686694668446657 L1_Forecast=0.09874875313376535 L1_Test=0.08066498822916202 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.20770189324721952 L2_Forecast=0.12009071678130138 L2_Test=0.11743095609813094 -INFO:pyaf.std:MODEL_COMPLEXITY 208 +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(200)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0672 MAPE_Forecast=0.0632 MAPE_Test=0.0905 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0669 SMAPE_Forecast=0.0627 SMAPE_Test=0.0865 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2671 MASE_Forecast=0.2501 MASE_Test=0.4408 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.22529254188884426 L1_Forecast=0.21107599858154158 L1_Test=0.20836525169937734 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.29870968153860633 L2_Forecast=0.2541821407606399 L2_Test=0.258529536376885 +INFO:pyaf.std:MODEL_COMPLEXITY 200 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag96 -0.7929490455160725 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag108 -0.6992204423338815 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag72 -0.610147645471951 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag168 -0.6039388187567968 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag36 -0.5865287424073047 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag144 -0.5344942748659707 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag192 -0.5163717628018195 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag180 -0.5147321490085996 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag132 -0.5115339056562493 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_Lag60 -0.49298461427587476 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.4903068319692706 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag164 -0.48075663894218773 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag160 0.3970138078342637 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.39173250861564346 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag120 0.38862497639713045 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag56 -0.38251351087420776 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag108 -0.3661613162947641 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag44 0.3592112690532089 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.3548423678376884 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag127 0.3448081547009084 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 59.79639267921448 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 149.22827768325806 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -171,23 +216,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0088 MAPE_Forecast=0.0049 MAPE_Test=0.0058 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0087 SMAPE_Forecast=0.0049 SMAPE_Test=0.0058 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0359 MASE_Forecast=0.0188 MASE_Test=0.0273 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.03030735638072783 L1_Forecast=0.015873826024559896 L1_Test=0.012915144692528876 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.12108092799712493 L2_Forecast=0.01976056512053816 L2_Test=0.01557343835632003 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.030307356380726767 L1_Forecast=0.01587382602455877 L1_Test=0.01291514469252599 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.12108092799712486 L2_Forecast=0.019760565120536696 L2_Test=0.015573438356317332 INFO:pyaf.std:MODEL_COMPLEXITY 250 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9345105216381682 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.39088292268491054 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.39003842360011426 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.17928655561750495 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag219 0.16542904823177979 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag240 -0.16498572143334533 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.1621368674335601 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.14802749123571568 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.14548217659742854 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.1371533985630134 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9345105216381727 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3908829226849115 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3900384236001172 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.17928655561750645 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag219 0.16542904823177937 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag240 -0.1649857214333443 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.1621368674335584 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.14802749123571246 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.14548217659742887 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.13715339856301567 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 67.27544617652893 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 206.2936851978302 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -199,23 +253,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0074 MAPE_Forecast=0.0033 MAPE_Test=0.0035 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0073 SMAPE_Forecast=0.0033 SMAPE_Test=0.0035 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0303 MASE_Forecast=0.013 MASE_Test=0.0179 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.025546582629411403 L1_Forecast=0.010930580520687853 L1_Test=0.008474671244720714 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.11596559712577038 L2_Forecast=0.013414584061186152 L2_Test=0.009770670685922188 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.025546582629409287 L1_Forecast=0.010930580520685669 L1_Test=0.008474671244716939 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.11596559712577058 L2_Forecast=0.013414584061184339 L2_Test=0.009770670685919412 INFO:pyaf.std:MODEL_COMPLEXITY 300 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9520873812395041 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3821398619228905 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.37025985130811445 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.20323836693285008 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.1874688177448302 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.17425577728496705 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.17418265209610254 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.17232761249645248 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.16277834894133153 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag85 -0.16161573238693894 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9520873812395108 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3821398619228936 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3702598513081168 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.2032383669328519 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.18746881774482826 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.1742557772849714 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.1741826520960998 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.17232761249645182 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.16277834894133203 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag85 -0.16161573238694138 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 61.82954931259155 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 209.41051125526428 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -227,23 +290,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0063 MAPE_Forecast=0.0024 MAPE_Test=0.0032 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0062 SMAPE_Forecast=0.0024 SMAPE_Test=0.0032 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0264 MASE_Forecast=0.0092 MASE_Test=0.0145 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.022280038066612762 L1_Forecast=0.00778965200803237 L1_Test=0.0068548479539446055 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.11456268523471379 L2_Forecast=0.009854131082538716 L2_Test=0.009323682454965558 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.022280038066611958 L1_Forecast=0.00778965200803154 L1_Test=0.006854847953943606 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.11456268523471376 L2_Forecast=0.009854131082537214 L2_Test=0.009323682454962772 INFO:pyaf.std:MODEL_COMPLEXITY 350 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9618112957907552 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.34760036144941786 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3326234742086963 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.20343363394757613 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.19583272105950517 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.19373059335819134 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.192341246542318 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.18729254241253432 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.18445251122840076 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.16979708360688517 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9618112957907636 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.34760036144942064 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3326234742087 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.20343363394757877 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.19583272105950797 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.19373059335819276 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.1923412465423171 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.18729254241253507 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.18445251122839856 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.1697970836068921 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 46.60432267189026 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 222.9368076324463 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -255,23 +327,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0063 MAPE_Forecast=0.0023 MAPE_Test=0.0031 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0063 SMAPE_Forecast=0.0023 SMAPE_Test=0.0031 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0264 MASE_Forecast=0.0086 MASE_Test=0.0138 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02223434059140287 L1_Forecast=0.0072776334650358696 L1_Test=0.006544320395508267 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.11648718069403619 L2_Forecast=0.0089933811119953 L2_Test=0.007666934132865002 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.022234340591402002 L1_Forecast=0.007277633465034995 L1_Test=0.0065443203955065825 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.11648718069403617 L2_Forecast=0.008993381111994679 L2_Test=0.0076669341328632346 INFO:pyaf.std:MODEL_COMPLEXITY 400 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9658553378984656 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35211259058292155 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.33293096649585263 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag57 -0.21785955555605768 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.21281018203223673 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.20076957777878415 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.1991869421975909 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag261 0.19660518004308192 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.19198815948065745 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag250 0.18429111157929798 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.965855337898474 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35211259058292355 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3329309664958524 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag57 -0.21785955555605618 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.21281018203223517 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.20076957777878612 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.1991869421975928 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag261 0.19660518004308275 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.1919881594806609 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag250 0.18429111157929967 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 50.18085718154907 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 181.16642212867737 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -283,23 +364,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0061 MAPE_Forecast=0.0017 MAPE_Test=0.0021 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.006 SMAPE_Forecast=0.0017 SMAPE_Test=0.0021 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0252 MASE_Forecast=0.0066 MASE_Test=0.0092 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.021288154826919606 L1_Forecast=0.005562569033257532 L1_Test=0.004338372823266583 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.12002731921962415 L2_Forecast=0.00690727416035699 L2_Test=0.005147831054885108 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.021288154826919578 L1_Forecast=0.0055625690332575025 L1_Test=0.004338372823267582 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.12002731921962427 L2_Forecast=0.006907274160356605 L2_Test=0.005147831054884069 INFO:pyaf.std:MODEL_COMPLEXITY 450 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9164592102191733 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.39418365179957954 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.33257277957459064 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.22950271107694162 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2228308678239575 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag57 -0.2188285770955284 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.2106733893492511 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.20812093651070562 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.2043224360570044 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.20320566537770185 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9164592102191809 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.39418365179958204 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3325727795745933 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.2295027110769384 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.22283086782395337 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag57 -0.21882857709552916 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.2106733893492524 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.20812093651070762 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.20432243605700132 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.20320566537770263 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 50.8386812210083 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 193.2739281654358 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -311,23 +401,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0061 MAPE_Forecast=0.0016 MAPE_Test=0.0018 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0061 SMAPE_Forecast=0.0016 SMAPE_Test=0.0018 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0256 MASE_Forecast=0.0059 MASE_Test=0.0078 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.021557631279876307 L1_Forecast=0.00498967660285672 L1_Test=0.003670625651804279 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.12531406665001332 L2_Forecast=0.0061701822356419285 L2_Test=0.004159893727812361 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02155763127987575 L1_Forecast=0.0049896766028562824 L1_Test=0.0036706256518068505 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.1253140666500127 L2_Forecast=0.006170182235641204 L2_Test=0.004159893727816083 INFO:pyaf.std:MODEL_COMPLEXITY 500 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9266284416831075 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.4001125083231898 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3275235402295039 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.24663740706276505 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.2452412996923103 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.2375752591294843 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.23371277085315015 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.22381867648886383 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.22020120367742474 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.21916433330858198 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9266284416831129 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.4001125083231848 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3275235402294996 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.24663740706276047 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.24524129969230835 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.2375752591294788 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.23371277085314826 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.22381867648886494 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.2202012036774198 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2191643333085786 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 53.308194398880005 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 228.86425471305847 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -339,23 +438,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0063 MAPE_Forecast=0.0012 MAPE_Test=0.0012 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0063 SMAPE_Forecast=0.0012 SMAPE_Test=0.0012 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0259 MASE_Forecast=0.0045 MASE_Test=0.0047 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02185442063208972 L1_Forecast=0.003757172360608224 L1_Test=0.002232759234727414 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.1345800078797074 L2_Forecast=0.004680534596966051 L2_Test=0.003005153087773462 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02185442063208843 L1_Forecast=0.0037571723606069775 L1_Test=0.002232759234724454 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.13458000787970648 L2_Forecast=0.004680534596964971 L2_Test=0.0030051530877741803 INFO:pyaf.std:MODEL_COMPLEXITY 550 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9064305851810438 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.36383957432502273 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32520187070462725 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.2802079956986062 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.27969375016701475 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.27081992104350117 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.24829523925639108 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.246252312403277 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.23884710494446196 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.22978743762980125 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.906430585181047 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3638395743250211 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32520187070462697 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.28020799569860716 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.27969375016701026 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.27081992104349506 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.24829523925639527 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2462523124032715 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.23884710494446437 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.22978743762979542 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 63.129958629608154 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 197.63337421417236 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -367,23 +475,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0065 MAPE_Forecast=0.001 MAPE_Test=0.0011 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0066 SMAPE_Forecast=0.001 SMAPE_Test=0.0011 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0266 MASE_Forecast=0.004 MASE_Test=0.0046 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.022406498328261124 L1_Forecast=0.0033564447177335645 L1_Test=0.002162579899993892 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.1417726299549214 L2_Forecast=0.0041583104934155065 L2_Test=0.002621273834975918 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.022406498328260797 L1_Forecast=0.0033564447177333633 L1_Test=0.0021625798999914867 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.14177262995492115 L2_Forecast=0.004158310493415702 L2_Test=0.0026212738349729306 INFO:pyaf.std:MODEL_COMPLEXITY 600 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9039215279497244 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.346935812287575 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33345938867353486 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3291246099694738 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3173010454543119 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.30136370934928247 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.2634973813455049 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2608907423072475 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.24668098986606418 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag243 0.24568153191725076 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9039215279497292 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.34693581228757864 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33345938867353825 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.329124609969479 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3173010454543127 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.30136370934928164 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.2634973813455129 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.26089074230724096 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.246680989866069 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag243 0.24568153191725126 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 64.76956105232239 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 214.95140480995178 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -395,23 +512,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0065 MAPE_Forecast=0.0009 MAPE_Test=0.0009 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0068 SMAPE_Forecast=0.0009 SMAPE_Test=0.0009 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0267 MASE_Forecast=0.0033 MASE_Test=0.0044 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.022520033684122077 L1_Forecast=0.002781334340950632 L1_Test=0.002081669916476699 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.1486763411701144 L2_Forecast=0.0035120063615151163 L2_Test=0.0024409838064506383 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.022520033684121747 L1_Forecast=0.002781334340950565 L1_Test=0.0020816699164746266 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.1486763411701132 L2_Forecast=0.0035120063615152602 L2_Test=0.0024409838064478203 INFO:pyaf.std:MODEL_COMPLEXITY 650 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.8949071648193038 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.34317927218892924 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3353631518294794 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3319496294917873 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32774411792836833 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3216255905751154 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.26690681585698917 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.2667683975195432 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.24929073912225377 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.24012943262002284 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.8949071648193107 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3431792721889384 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3353631518294812 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33194962949178075 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32774411792837876 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3216255905751091 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2669068158569821 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.26676839751954806 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.24929073912225203 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.24012943262001818 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 71.55847573280334 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 213.43421149253845 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -423,23 +549,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0067 MAPE_Forecast=0.0007 MAPE_Test=0.0009 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.007 SMAPE_Forecast=0.0007 SMAPE_Test=0.0009 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0276 MASE_Forecast=0.0027 MASE_Test=0.0044 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.023242526493203665 L1_Forecast=0.0023138164537436228 L1_Test=0.0020893555199711553 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.1553913154214591 L2_Forecast=0.0029604294728704282 L2_Test=0.00261784965811242 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02324252649320325 L1_Forecast=0.0023138164537433612 L1_Test=0.0020893555199669733 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.15539131542145818 L2_Forecast=0.0029604294728701 L2_Test=0.00261784965810577 INFO:pyaf.std:MODEL_COMPLEXITY 700 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.8983630100597615 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.354236005632465 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33006638648797243 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3299298916852127 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3274687934640512 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3241175551903934 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2757337724604522 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2742074513567537 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.26013851937594823 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.2590590190969141 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.8983630100597664 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35423600563247226 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3300663864879737 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3299298916852147 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3274687934640446 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.324117555190398 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.27573377246044817 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2742074513567465 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.2601385193759526 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.25905901909691564 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 80.89142680168152 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 259.8100264072418 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -451,23 +586,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0067 MAPE_Forecast=0.0006 MAPE_Test=0.001 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0069 SMAPE_Forecast=0.0006 SMAPE_Test=0.001 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0275 MASE_Forecast=0.0025 MASE_Test=0.0047 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.023234315422084924 L1_Forecast=0.002097650557512542 L1_Test=0.0022256598624404247 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.15763413469038462 L2_Forecast=0.0026276824540225297 L2_Test=0.002677346090405251 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.023234315422083776 L1_Forecast=0.0020976505575114653 L1_Test=0.0022256598624392963 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.15763413469038345 L2_Forecast=0.0026276824540217834 L2_Test=0.0026773460904021378 INFO:pyaf.std:MODEL_COMPLEXITY 750 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.8919097112504131 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3583502281605203 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.33400931173461124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33383215225461915 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3285331834647462 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3181821977135474 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.28584850804928863 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.27720433713080894 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.891909711250419 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35835022816052753 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.33400931173461257 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3338321522546194 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.328533183464755 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.31818219771353967 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2858485080492868 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.27720433713079873 INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.26223580641096245 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.25883717031443115 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.25883717031443004 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 89.18339157104492 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 258.57784509658813 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -479,23 +623,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0068 MAPE_Forecast=0.0006 MAPE_Test=0.0007 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.007 SMAPE_Forecast=0.0006 SMAPE_Test=0.0007 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0279 MASE_Forecast=0.0023 MASE_Test=0.0034 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02354476837194359 L1_Forecast=0.0019245688102900455 L1_Test=0.001591321882645232 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.1600451061914437 L2_Forecast=0.0023560258644985035 L2_Test=0.002015419185001581 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.023544768371943227 L1_Forecast=0.0019245688102899543 L1_Test=0.001591321882642401 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.16004510619144224 L2_Forecast=0.0023560258644979132 L2_Test=0.002015419184996649 INFO:pyaf.std:MODEL_COMPLEXITY 800 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.898903863021336 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3635505490434892 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3392254799680925 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33443328703195885 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3312661033797837 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.327221062468973 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2913510370172105 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.28271641847858386 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.26848104175644727 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.26730268351685593 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.898903863021344 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3635505490434918 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.33922547996808844 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33443328703194897 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33126610337978474 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3272210624689798 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2913510370172112 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2827164184785814 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.2684810417564479 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.26730268351685416 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 97.39364719390869 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 296.2282602787018 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -507,23 +660,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0069 MAPE_Forecast=0.0005 MAPE_Test=0.0004 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0071 SMAPE_Forecast=0.0005 SMAPE_Test=0.0004 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0281 MASE_Forecast=0.0019 MASE_Test=0.0021 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.023736589398259962 L1_Forecast=0.0015908942172915559 L1_Test=0.0009859974623748398 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.16207926547708834 L2_Forecast=0.0019679376309597284 L2_Test=0.001190354849695607 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02373658939825961 L1_Forecast=0.0015908942172913791 L1_Test=0.000985997462375987 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.16207926547708698 L2_Forecast=0.001967937630960144 L2_Test=0.0011903548496953905 INFO:pyaf.std:MODEL_COMPLEXITY 850 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.900914860822866 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3677495819652491 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3425524707510082 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3365143514056379 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9009148608228692 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.36774958196525365 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.34255247075100065 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33651435140563 INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33195491963022317 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3278830743915203 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.29106698417855015 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2836097783587117 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.2751371966401971 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.27067376597729875 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3278830743915233 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.29106698417854926 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.28360977835871004 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.27513719664019465 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.270673765977303 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 106.57698512077332 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 322.00103282928467 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -535,23 +697,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.007 MAPE_Forecast=0.0005 MAPE_Test=0.0005 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0072 SMAPE_Forecast=0.0005 SMAPE_Test=0.0005 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0285 MASE_Forecast=0.0019 MASE_Test=0.0028 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.024078689227329367 L1_Forecast=0.0016180239625604876 L1_Test=0.0013333951655434273 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.16391619683045366 L2_Forecast=0.0019581052787013492 L2_Test=0.0016516630104228892 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.024078689227328517 L1_Forecast=0.0016180239625596987 L1_Test=0.0013333951655400227 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.16391619683045353 L2_Forecast=0.0019581052787011792 L2_Test=0.0016516630104200158 INFO:pyaf.std:MODEL_COMPLEXITY 900 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9006326337891313 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.37102682583843005 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.34307975198038787 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.34133508841005894 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3297120887933982 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3281039270206056 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2936446502975195 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2874657896981815 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.2743900772964252 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.2702172201928196 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9006326337891377 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3710268258384325 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.343079751980385 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.34133508841005783 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3297120887933968 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32810392702059626 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2936446502975182 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.28746578969817926 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.2743900772964196 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.2702172201928167 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 117.75360012054443 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 393.73567485809326 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -563,18 +734,27 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0071 MAPE_Forecast=0.0004 MAPE_Test=0.0005 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0073 SMAPE_Forecast=0.0004 SMAPE_Test=0.0005 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0288 MASE_Forecast=0.0016 MASE_Test=0.0025 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02432695647825162 L1_Forecast=0.0013787219802235667 L1_Test=0.001158398938491123 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.16507978841918 L2_Forecast=0.0016889156300595298 L2_Test=0.001455752771202116 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.024326956478251543 L1_Forecast=0.001378721980223524 L1_Test=0.0011583989384891986 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.16507978841917972 L2_Forecast=0.0016889156300594285 L2_Test=0.0014557527712000138 INFO:pyaf.std:MODEL_COMPLEXITY 950 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9012607657295444 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.37596645797584316 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.34144136050232543 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3397829973782298 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.328173616618929 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3276318296453248 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2913309000902433 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.29130265297277363 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.278750390278308 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.2732458486694653 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9012607657295476 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3759664579758385 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3414413605023231 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3397829973782326 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3281736166189236 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3276318296453165 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2913309000902372 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2913026529727761 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.27875039027830695 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.2732458486694681 INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_debug_perf.log b/tests/references/perf_test_ozone_debug_perf.log index 662ac54f2..9cf151409 100644 --- a/tests/references/perf_test_ozone_debug_perf.log +++ b/tests/references/perf_test_ozone_debug_perf.log @@ -5,103 +5,199 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1988 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.3646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1988 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.3646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.4619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.9761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 360.8514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 3289621.0774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.1949 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2164 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2172 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2164 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2172 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.2201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.1959 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.2137 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1796 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_AR 62 0.2137 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.1796 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.3191 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.1595 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1992 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_AR 62 0.1992 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_NoAR 24 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.1657 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.316 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.3799 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.316 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 0.3799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.2424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 6.2325 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 0.362 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.5176 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.6926 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_AR 110 0.2245 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.4322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.2778 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.18 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.5882 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.6726 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_AR 94 0.5882 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 0.6726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.7301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.3302 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 0.3766 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.4489 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.3068 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.4489 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 0.3068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.5794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.7377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 0.4357 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 5433.1627 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 40796628.6278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_AR 78 5433.1627 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_NoAR 40 40796628.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 16702.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 13195.3873 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 84073.9442 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.8931 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 84073.9442 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.8931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 58064.6971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 667.4806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5663 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 1338.131 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 19806.389 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 46569172.3098 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_AR 94 19806.389 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_NoAR 56 46569172.3098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 168397.0695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 113192.3779 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 1858329.0734 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 47541752.6548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 1858329.0734 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 47541752.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 16375730.725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 3613815.678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 24525015.6551 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3697 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3697 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 2.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 1.439 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.338 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.3178 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.3178 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 0.305 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3238 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.5055 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_AR 94 0.4118 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_NoAR 56 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 0.4065 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3193 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.9277 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_AR 94 0.3625 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_NoAR 56 0.9177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 12.0506911277771 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.1744019985198975 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -113,46 +209,50 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013038 L1_Test=0.42992050508902374 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507155 L2_Test=0.5519432695595545 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033716 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533314 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225448 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389378 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.1409304557420878 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046362 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481855 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102227 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045818 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947771 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 45.52981877326965 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.7763049602508545 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.174 0.9094 -1 None _Ozone ... 0.343 1.6728 - -[2 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.520286798477173 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.336667776107788 Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.1740 0.9094 -1 None _Ozone ... 0.3430 1.6728 -2 None _Ozone ... 0.2567 1.2245 -3 None _Ozone ... 0.2567 1.2245 -4 None Diff_Ozone ... 0.2262 1.0525 +1 None _Ozone ... 0.1740 0.9094 +2 None _Ozone ... 0.3430 1.6728 +3 None _Ozone ... 0.3430 1.6728 +4 None _Ozone ... 0.2209 1.0918 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -179,47 +279,49 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 5.2 KB None Forecasts - [[Timestamp('1972-01-01 00:00:00') nan 0.6111465641695248] - [Timestamp('1972-02-01 00:00:00') nan 1.6265294602869078] - [Timestamp('1972-03-01 00:00:00') nan 1.9422085585983488] - [Timestamp('1972-04-01 00:00:00') nan 2.3696733128899754] - [Timestamp('1972-05-01 00:00:00') nan 2.6630222017572542] - [Timestamp('1972-06-01 00:00:00') nan 3.248702087873624] - [Timestamp('1972-07-01 00:00:00') nan 3.220269503043295] - [Timestamp('1972-08-01 00:00:00') nan 3.3293872899828645] - [Timestamp('1972-09-01 00:00:00') nan 2.996846273026353] - [Timestamp('1972-10-01 00:00:00') nan 2.118734432843619] - [Timestamp('1972-11-01 00:00:00') nan 1.3326002972043849] - [Timestamp('1972-12-01 00:00:00') nan 0.8415747446287944]] + [[Timestamp('1972-01-01 00:00:00') nan 0.6111465641695266] + [Timestamp('1972-02-01 00:00:00') nan 1.6265294602869103] + [Timestamp('1972-03-01 00:00:00') nan 1.9422085585983508] + [Timestamp('1972-04-01 00:00:00') nan 2.369673312889975] + [Timestamp('1972-05-01 00:00:00') nan 2.6630222017572525] + [Timestamp('1972-06-01 00:00:00') nan 3.248702087873622] + [Timestamp('1972-07-01 00:00:00') nan 3.2202695030432924] + [Timestamp('1972-08-01 00:00:00') nan 3.329387289982864] + [Timestamp('1972-09-01 00:00:00') nan 2.9968462730263523] + [Timestamp('1972-10-01 00:00:00') nan 2.1187344328436204] + [Timestamp('1972-11-01 00:00:00') nan 1.3326002972043869] + [Timestamp('1972-12-01 00:00:00') nan 0.8415747446287978]] { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013038", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507155" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/perf_test_ozone_long_series.log b/tests/references/perf_test_ozone_long_series.log index e9dc67b06..510d19da9 100644 --- a/tests/references/perf_test_ozone_long_series.log +++ b/tests/references/perf_test_ozone_long_series.log @@ -29,7 +29,7 @@ min 1.200000 75% 4.825000 max 8.700000 Name: Ozone, dtype: float64 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 15.169864177703857 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 8.992746829986572 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2117-04-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=2448 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -41,41 +41,50 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1613 MAPE_Forecast=0.1634 MAPE_Test=0.1959 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1559 SMAPE_Forecast=0.1564 SMAPE_Test=0.175 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6705 MASE_Forecast=0.6543 MASE_Test=1.0188 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.5663817795433991 L1_Forecast=0.5532435140740595 L1_Test=0.4816176170673965 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.7585617196965734 L2_Forecast=0.7397864278645382 L2_Test=0.6194211130724255 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.5663817795433992 L1_Forecast=0.5532435140740597 L1_Test=0.4816176170673976 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.7585617196965734 L2_Forecast=0.7397864278645382 L2_Test=0.6194211130724266 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8589322381930184 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4278758093954964 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.16277211336618982 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.15347100398127994 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.15334909544570882 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag4 0.14446049559692486 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.14021763695463552 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag42 0.13126016208411284 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.12685178500565122 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.12444636198033902 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag3 0.1164778195591134 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4278758093954962 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.16277211336618966 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.15347100398127972 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.15334909544570885 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag4 0.14446049559692473 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.140217636954635 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag42 0.131260162084113 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.12685178500565086 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.12444636198033893 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag3 0.11647781955911417 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 49.076727628707886 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.511497974395752 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.422370195388794 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 2.6380558013916016 Split Transformation ... ForecastMAPE TestMAPE -0 None _Ozone ... 0.1608 0.2384 -1 None _Ozone ... 0.1608 0.2384 +0 None _Ozone ... 0.1590 0.2287 +1 None _Ozone ... 0.1616 0.1915 2 None _Ozone ... 0.1616 0.1915 -3 None _Ozone ... 0.1618 0.2256 -4 None _Ozone ... 0.1618 0.2256 +3 None _Ozone ... 0.1617 0.2281 +4 None _Ozone ... 0.1626 0.2319 [5 rows x 8 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -119,31 +128,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "2158-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "2158-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 2448 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(64)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 2448 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(64)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "64", - "MAE": "0.5532435140740595", - "MAPE": "0.1634", - "MASE": "0.6543", - "RMSE": "0.7397864278645382" + "Model_Performance": { + "COMPLEXITY": "64", + "MAE": "0.5532435140740597", + "MAPE": "0.1634", + "MASE": "0.6543", + "RMSE": "0.7397864278645382" + } } } diff --git a/tests/references/perf_test_ozone_long_series_2.log b/tests/references/perf_test_ozone_long_series_2.log index d6c32416b..8c90d297b 100644 --- a/tests/references/perf_test_ozone_long_series_2.log +++ b/tests/references/perf_test_ozone_long_series_2.log @@ -29,7 +29,7 @@ min 1.200000 75% 4.825000 max 8.700000 Name: Ozone, dtype: float64 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 25.833468675613403 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 31.664931058883667 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 @@ -41,41 +41,50 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1616 MAPE_Forecast=0.1616 MAPE_Test=0.1939 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1565 SMAPE_Forecast=0.1566 SMAPE_Test=0.1736 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6671 MASE_Forecast=0.6669 MASE_Test=1.0078 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.5626723620452728 L1_Forecast=0.562789933242084 L1_Test=0.4764079103256295 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.7529937583726243 L2_Forecast=0.7524714932067427 L2_Test=0.6133908884692815 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.5626723620452727 L1_Forecast=0.562789933242084 L1_Test=0.4764079103256286 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.7529937583726243 L2_Forecast=0.7524714932067427 L2_Test=0.6133908884692797 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227169247052462 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.16526299752943682 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.1545018314441784 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.14505277910734177 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.1409254837240059 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag4 0.13977010257344114 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.12947882372654812 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag42 0.12756105484000083 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag2 0.12307895237682727 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.12272734218456578 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.4227169247052488 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.16526299752943607 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.15450183144417576 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.14505277910734055 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.1409254837240055 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag4 0.13977010257344155 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.1294788237265478 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag42 0.12756105484000035 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1230789523768279 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.12272734218456714 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 44.02039456367493 -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 3.907472610473633 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 29.716951370239258 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 2.8414578437805176 Split Transformation ... ForecastMAPE TestMAPE -0 None _Ozone ... 0.1571 0.2307 -1 None _Ozone ... 0.1581 0.2344 -2 None _Ozone ... 0.1583 0.2212 -3 None _Ozone ... 0.1586 0.2352 -4 None _Ozone ... 0.1601 0.1892 +0 None _Ozone ... 0.1601 0.1892 +1 None _Ozone ... 0.1601 0.1892 +2 None _Ozone ... 0.1601 0.1892 +3 None _Ozone ... 0.1603 0.1925 +4 None _Ozone ... 0.1608 0.1947 [5 rows x 8 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -119,31 +128,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "2150-06-20 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "2150-06-20 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 10200 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(64)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 10200 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(64)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "64", - "MAE": "0.562789933242084", - "MAPE": "0.1616", - "MASE": "0.6669", - "RMSE": "0.7524714932067427" + "Model_Performance": { + "COMPLEXITY": "64", + "MAE": "0.562789933242084", + "MAPE": "0.1616", + "MASE": "0.6669", + "RMSE": "0.7524714932067427" + } } } diff --git a/tests/references/perf_test_perf1.log b/tests/references/perf_test_perf1.log index 8574c443e..87844fab3 100644 --- a/tests/references/perf_test_perf1.log +++ b/tests/references/perf_test_perf1.log @@ -2,31 +2,40 @@ INFO:pyaf.std:START_TRAINING 'Signal' TEST_CYCLES_START 64 GENERATING_RANDOM_DATASET Signal_4800_D_0_linear_64_None_0.0_100 TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal' 28.45394992828369 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 25.35572910308838 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2010-06-16T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=4788 Min=1.0 Max=10.84375 Mean=5.366025741436926 StdDev=2.7865646548175143 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=10.84375 Mean=5.366025741436926 StdDev=2.7865646548175143 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0 MAPE_Forecast=0.0 MAPE_Test=0.0 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0 SMAPE_Forecast=0.0 SMAPE_Test=0.0 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0 MASE_Forecast=0.0 MASE_Test=0.0 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.8664534722886795e-15 L1_Forecast=1.8692625318375023e-15 L1_Test=1.7393494052460785e-15 -INFO:pyaf.std:MODEL_L2 L2_Fit=2.2486445294400876e-15 L2_Forecast=2.2514459651216663e-15 L2_Test=2.022922624776835e-15 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.0 L1_Forecast=0.0 L1_Test=0.0 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.0 L2_Forecast=0.0 L2_Test=0.0 INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5.365142342931938 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 64 -0.6151423429319376 {0: -3.8963923429319376, 1: 1.7286076570680624, 2: -2.9588923429319376, 3: -1.3963923429319376, 4: -1.0838923429319376, 5: 3.4473576570680624, 6: 1.2598576570680624, 7: -0.7713923429319376, 8: -3.4276423429319376, 9: -0.6151423429319376, 10: -0.6151423429319376, 11: -2.4901423429319376, 12: 4.697357657068062, 13: -4.208892342931938, 14: 1.5723576570680624, 15: 1.7286076570680624, 16: -0.7713923429319376, 17: 2.8223576570680624, 18: -0.6151423429319376, 19: -1.7088923429319376, 20: 1.4161076570680624, 21: -0.4588923429319376, 22: -2.3338923429319376, 23: -3.1151423429319376, 24: -2.9588923429319376, 25: -1.2401423429319376, 26: 3.6036076570680624, 27: -1.8651423429319376, 28: 3.6036076570680624, 29: -3.5838923429319376, 30: 5.322357657068062, 31: -2.0213923429319376, 32: 2.9786076570680624, 33: -4.365142342931938, 34: -1.5526423429319376, 35: 1.1036076570680624, 36: -0.6151423429319376, 37: 3.2911076570680624, 38: 3.6036076570680624, 39: 0.1661076570680624, 40: -1.3963923429319376, 41: -1.3963923429319376, 42: -2.1776423429319376, 43: 1.7286076570680624, 44: 0.6348576570680624, 45: -4.208892342931938, 46: -2.9588923429319376, 47: 4.541107657068062, 48: 5.478607657068062, 49: 0.6348576570680624, 50: 0.4786076570680624, 51: -2.8026423429319376, 52: 3.7598576570680624, 53: -0.7713923429319376, 54: 1.1036076570680624, 55: 5.322357657068062, 56: -2.6463923429319376, 57: 3.4473576570680624, 58: 4.228607657068062, 59: 0.009857657068062409, 60: 0.9473576570680624, 61: -4.365142342931938, 62: -4.365142342931938, 63: 1.2598576570680624} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.7369163036346436 +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 1.0630786418914795 Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byL2', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -45,47 +54,49 @@ dtypes: datetime64[ns](1), float64(2) memory usage: 112.6 KB None Forecasts - [[Timestamp('2013-02-09 00:00:00') nan 9.125000000000004] - [Timestamp('2013-02-10 00:00:00') nan 4.593749999999999] - [Timestamp('2013-02-11 00:00:00') nan 6.468749999999999] + [[Timestamp('2013-02-09 00:00:00') nan 9.125] + [Timestamp('2013-02-10 00:00:00') nan 4.59375] + [Timestamp('2013-02-11 00:00:00') nan 6.46875] [Timestamp('2013-02-12 00:00:00') nan 10.6875] - [Timestamp('2013-02-13 00:00:00') nan 2.718750000000002] - [Timestamp('2013-02-14 00:00:00') nan 8.812500000000004] - [Timestamp('2013-02-15 00:00:00') nan 9.593750000000004] + [Timestamp('2013-02-13 00:00:00') nan 2.71875] + [Timestamp('2013-02-14 00:00:00') nan 8.8125] + [Timestamp('2013-02-15 00:00:00') nan 9.59375] [Timestamp('2013-02-16 00:00:00') nan 5.375] - [Timestamp('2013-02-17 00:00:00') nan 6.312499999999998] - [Timestamp('2013-02-18 00:00:00') nan 1.0000000000000027] - [Timestamp('2013-02-19 00:00:00') nan 1.0000000000000027] + [Timestamp('2013-02-17 00:00:00') nan 6.3125] + [Timestamp('2013-02-18 00:00:00') nan 1.0] + [Timestamp('2013-02-19 00:00:00') nan 1.0] [Timestamp('2013-02-20 00:00:00') nan 6.625]] { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2013-02-08 00:00:00" - ], - "TimeVariable": "Date" + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2013-02-08 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 4788 }, - "Training_Signal_Length": 4788 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byL2_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "1.8692625318375023e-15", - "MAPE": "0.0", - "MASE": "0.0", - "RMSE": "2.2514459651216663e-15" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.0", + "MAPE": "0.0", + "MASE": "0.0", + "RMSE": "0.0" + } } } diff --git a/tests/references/perf_test_web-traffic-time-series-forecasting.log b/tests/references/perf_test_web-traffic-time-series-forecasting.log index c558bcb8c..2e9efc6cc 100644 --- a/tests/references/perf_test_web-traffic-time-series-forecasting.log +++ b/tests/references/perf_test_web-traffic-time-series-forecasting.log @@ -1,47 +1,46 @@ INFO:pyaf.std:START_TRAINING '4360' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4360' 21.207984447479248 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4360']' 17.7079017162323 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4360' Length=550 Min=106.0 Max=27635.0 Mean=569.3618181818182 StdDev=1752.9610383048669 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4360' Min=106.0 Max=27635.0 Mean=569.3618181818182 StdDev=1752.9610383048669 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_4360_Lag1Trend_residue_zeroCycle_residue_AR(16)' [Lag1Trend + NoCycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' [Lag1Trend + Seasonal_WeekOfMonth + NoAR] INFO:pyaf.std:TREND_DETAIL '_4360_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_4360_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_4360_Lag1Trend_residue_zeroCycle_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3077 MAPE_Forecast=0.1855 MAPE_Test=0.1502 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2623 SMAPE_Forecast=0.1843 SMAPE_Test=0.1476 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9794 MASE_Forecast=0.9396 MASE_Test=0.9715 -INFO:pyaf.std:MODEL_L1 L1_Fit=303.1658026098537 L1_Forecast=52.73399916513117 L1_Test=43.37275154672089 -INFO:pyaf.std:MODEL_L2 L2_Fit=1508.2510027518376 L2_Forecast=103.64185168501656 L2_Test=70.67141997421632 -INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:CYCLE_DETAIL '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth' [Seasonal_WeekOfMonth] +INFO:pyaf.std:AUTOREG_DETAIL '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2162 MAPE_Forecast=0.1929 MAPE_Test=0.1503 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2174 SMAPE_Forecast=0.194 SMAPE_Test=0.1519 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9872 MASE_Forecast=0.9979 MASE_Test=1.0026 +INFO:pyaf.std:MODEL_L1 L1_Fit=305.57397959183675 L1_Forecast=56.005102040816325 L1_Test=44.75833333333333 +INFO:pyaf.std:MODEL_L2 L2_Fit=1700.4158067414942 L2_Forecast=113.89445301932588 L2_Test=74.99808330884197 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 185.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4360_Lag1Trend_residue_Seasonal_WeekOfMonth -7.5 {1: -2.5, 2: -10.0, 3: -1.5, 4: -14.0, 5: -12.0, 6: -11.0, -51: -13.0, -50: 9.0, -49: 35.0, -48: -934.0} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4360_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.45731285298336377 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4360_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.28829251210963935 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4360_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.286745958915988 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4360_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.26863568146137545 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4360_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.18783229802374482 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4360_Lag1Trend_residue_zeroCycle_residue_Lag8 -0.1789579179553144 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4360_Lag1Trend_residue_zeroCycle_residue_Lag7 -0.16624700835390757 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4360_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.14623639383827008 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4360_Lag1Trend_residue_zeroCycle_residue_Lag10 -0.14349080474490794 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4360_Lag1Trend_residue_zeroCycle_residue_Lag11 -0.1289656899559146 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 7.364354848861694 +INFO:pyaf.std:START_FORECASTING '['4360']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4360']' 2.444448947906494 Split Transformation ... ForecastMAPE TestMAPE 0 None _4360 ... 0.1855 0.1502 -1 None Anscombe_4360 ... 0.1869 0.1473 -2 None _4360 ... 0.1959 0.1523 -3 None Anscombe_4360 ... 0.1959 0.1523 -4 None Diff_4360 ... 0.1959 0.1523 +1 None _4360 ... 0.1855 0.1502 +2 None Anscombe_4360 ... 0.1869 0.1473 +3 None Anscombe_4360 ... 0.1869 0.1473 +4 None Anscombe_4360 ... 0.1928 0.1503 [5 rows x 8 columns] Forecast Columns Index(['Date', '4360', 'row_number', 'Date_Normalized', '_4360', '_4360_Lag1Trend', '_4360_Lag1Trend_residue', - '_4360_Lag1Trend_residue_zeroCycle', - '_4360_Lag1Trend_residue_zeroCycle_residue', - '_4360_Lag1Trend_residue_zeroCycle_residue_AR(16)', - '_4360_Lag1Trend_residue_zeroCycle_residue_AR(16)_residue', + '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth', + '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth_residue', + '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR', + '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR_residue', '_4360_Trend', '_4360_Trend_residue', '_4360_Cycle', '_4360_Cycle_residue', '_4360_AR', '_4360_AR_residue', '_4360_TransformedForecast', '4360_Forecast', @@ -60,95 +59,97 @@ memory usage: 14.4 KB None Forecasts Date 4360 4360_Forecast -550 2017-01-01 NaN 224.372617 -551 2017-01-02 NaN 226.931785 -552 2017-01-03 NaN 225.922915 -553 2017-01-04 NaN 234.128055 -554 2017-01-05 NaN 233.988713 -555 2017-01-06 NaN 230.468652 -556 2017-01-07 NaN 228.552268 -557 2017-01-08 NaN 226.907028 -558 2017-01-09 NaN 225.563676 -559 2017-01-10 NaN 224.909560 -560 2017-01-11 NaN 225.006071 -561 2017-01-12 NaN 227.698137 -562 2017-01-13 NaN 228.312960 -563 2017-01-14 NaN 231.053213 -564 2017-01-15 NaN 234.645652 -565 2017-01-16 NaN 233.503460 -566 2017-01-17 NaN 230.447675 -567 2017-01-18 NaN 229.709738 -568 2017-01-19 NaN 230.321494 -569 2017-01-20 NaN 230.606913 -570 2017-01-21 NaN 231.273695 -571 2017-01-22 NaN 231.772230 -572 2017-01-23 NaN 231.542604 -573 2017-01-24 NaN 231.166061 -574 2017-01-25 NaN 231.022603 -575 2017-01-26 NaN 231.036561 -576 2017-01-27 NaN 231.170393 -577 2017-01-28 NaN 231.377791 -578 2017-01-29 NaN 231.719351 -579 2017-01-30 NaN 232.010119 -580 2017-01-31 NaN 232.265061 -581 2017-02-01 NaN 232.592230 -582 2017-02-02 NaN 232.805941 -583 2017-02-03 NaN 232.795675 -584 2017-02-04 NaN 232.769735 -585 2017-02-05 NaN 232.873472 -586 2017-02-06 NaN 233.025591 -587 2017-02-07 NaN 233.190372 -588 2017-02-08 NaN 233.360576 -589 2017-02-09 NaN 233.486170 -590 2017-02-10 NaN 233.567060 -591 2017-02-11 NaN 233.652725 -592 2017-02-12 NaN 233.765429 -593 2017-02-13 NaN 233.900322 -594 2017-02-14 NaN 234.047957 -595 2017-02-15 NaN 234.204528 -596 2017-02-16 NaN 234.359701 -597 2017-02-17 NaN 234.506728 -598 2017-02-18 NaN 234.651381 -599 2017-02-19 NaN 234.794353 -600 2017-02-20 NaN 234.927028 -601 2017-02-21 NaN 235.051022 -602 2017-02-22 NaN 235.178013 -603 2017-02-23 NaN 235.310875 -604 2017-02-24 NaN 235.446065 -605 2017-02-25 NaN 235.581902 -606 2017-02-26 NaN 235.716057 -607 2017-02-27 NaN 235.846473 -608 2017-02-28 NaN 235.975261 -609 2017-03-01 NaN 236.105857 +550 2017-01-01 NaN 209.5 +551 2017-01-02 NaN 218.5 +552 2017-01-03 NaN 227.5 +553 2017-01-04 NaN 236.5 +554 2017-01-05 NaN 245.5 +555 2017-01-06 NaN 254.5 +556 2017-01-07 NaN 263.5 +557 2017-01-08 NaN 272.5 +558 2017-01-09 NaN 307.5 +559 2017-01-10 NaN 342.5 +560 2017-01-11 NaN 377.5 +561 2017-01-12 NaN 412.5 +562 2017-01-13 NaN 447.5 +563 2017-01-14 NaN 482.5 +564 2017-01-15 NaN 517.5 +565 2017-01-16 NaN -416.5 +566 2017-01-17 NaN -1350.5 +567 2017-01-18 NaN -2284.5 +568 2017-01-19 NaN -3218.5 +569 2017-01-20 NaN -4152.5 +570 2017-01-21 NaN -5086.5 +571 2017-01-22 NaN -6020.5 +572 2017-01-23 NaN -6028.0 +573 2017-01-24 NaN -6035.5 +574 2017-01-25 NaN -6043.0 +575 2017-01-26 NaN -6050.5 +576 2017-01-27 NaN -6058.0 +577 2017-01-28 NaN -6065.5 +578 2017-01-29 NaN -6073.0 +579 2017-01-30 NaN -6080.5 +580 2017-01-31 NaN -6088.0 +581 2017-02-01 NaN -6090.5 +582 2017-02-02 NaN -6093.0 +583 2017-02-03 NaN -6095.5 +584 2017-02-04 NaN -6098.0 +585 2017-02-05 NaN -6100.5 +586 2017-02-06 NaN -6110.5 +587 2017-02-07 NaN -6120.5 +588 2017-02-08 NaN -6130.5 +589 2017-02-09 NaN -6140.5 +590 2017-02-10 NaN -6150.5 +591 2017-02-11 NaN -6160.5 +592 2017-02-12 NaN -6170.5 +593 2017-02-13 NaN -6172.0 +594 2017-02-14 NaN -6173.5 +595 2017-02-15 NaN -6175.0 +596 2017-02-16 NaN -6176.5 +597 2017-02-17 NaN -6178.0 +598 2017-02-18 NaN -6179.5 +599 2017-02-19 NaN -6181.0 +600 2017-02-20 NaN -6195.0 +601 2017-02-21 NaN -6209.0 +602 2017-02-22 NaN -6223.0 +603 2017-02-23 NaN -6237.0 +604 2017-02-24 NaN -6251.0 +605 2017-02-25 NaN -6265.0 +606 2017-02-26 NaN -6279.0 +607 2017-02-27 NaN -6291.0 +608 2017-02-28 NaN -6303.0 +609 2017-03-01 NaN -6305.5 { - "Dataset": { - "Signal": "4360", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4360": { + "Dataset": { + "Signal": "4360", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4360_Lag1Trend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "52.73399916513117", - "MAPE": "0.1855", - "MASE": "0.9396", - "RMSE": "103.64185168501656" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4360_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR", + "Cycle": "Seasonal_WeekOfMonth", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "36", + "MAE": "56.005102040816325", + "MAPE": "0.1929", + "MASE": "0.9979", + "RMSE": "113.89445301932588" + } } } @@ -157,7 +158,7 @@ Forecasts 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diff --git a/tests/references/perf_test_web-traffic-time-series-forecasting_all.log b/tests/references/perf_test_web-traffic-time-series-forecasting_all.log index 703fe40ef..b6c81ecf1 100644 --- a/tests/references/perf_test_web-traffic-time-series-forecasting_all.log +++ b/tests/references/perf_test_web-traffic-time-series-forecasting_all.log @@ -1,40 +1,48 @@ INFO:pyaf.std:START_TRAINING '4310' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4310' 20.28469467163086 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4310']' 18.29341459274292 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4310' Length=550 Min=0.0 Max=3448.0 Mean=294.9472727272727 StdDev=448.1364675893617 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4310' Min=0.0 Max=3448.0 Mean=294.9472727272727 StdDev=448.1364675893617 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_4310_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [Lag1Trend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_4310_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] INFO:pyaf.std:TREND_DETAIL '_4310_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_4310_Lag1Trend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] -INFO:pyaf.std:AUTOREG_DETAIL '_4310_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=115461005830.9909 MAPE_Forecast=0.1706 MAPE_Test=0.1916 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.1011 SMAPE_Forecast=0.1645 SMAPE_Test=0.1867 -INFO:pyaf.std:MODEL_MASE MASE_Fit=1.1307 MASE_Forecast=0.9241 MASE_Test=0.9255 -INFO:pyaf.std:MODEL_L1 L1_Fit=75.84611880466473 L1_Forecast=39.06013119533527 L1_Test=31.342857142857138 -INFO:pyaf.std:MODEL_L2 L2_Fit=178.2631465216488 L2_Forecast=66.91371457791863 L2_Test=40.36826924689484 -INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:CYCLE_DETAIL '_4310_Lag1Trend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_4310_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0925 MAPE_Forecast=0.1892 MAPE_Test=0.2032 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0959 SMAPE_Forecast=0.1812 SMAPE_Test=0.1942 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.9915 MASE_Test=1.0045 +INFO:pyaf.std:MODEL_L1 L1_Fit=66.91071428571429 L1_Forecast=41.90816326530612 L1_Test=34.016666666666666 +INFO:pyaf.std:MODEL_L2 L2_Fit=180.0605552902372 L2_Forecast=66.11886328146193 L2_Test=44.312338387105385 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 0.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4310_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 4.661175489425659 +INFO:pyaf.std:START_FORECASTING '['4310']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4310']' 1.9189419746398926 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4310 ... 0.1695 0.1900 -1 None Anscombe_4310 ... 0.1699 0.1856 -2 None _4310 ... 0.1706 0.1916 -3 None Anscombe_4310 ... 0.1738 0.1862 -4 None Anscombe_4310 ... 0.1818 0.2099 +0 None Anscombe_4310 ... 0.1806 0.1912 +1 None _4310 ... 0.1809 0.1871 +2 None Anscombe_4310 ... 0.1838 0.2144 +3 None Anscombe_4310 ... 0.1838 0.2144 +4 None Anscombe_4310 ... 0.1838 0.2144 [5 rows x 8 columns] Forecast Columns Index(['Date', '4310', 'row_number', 'Date_Normalized', '_4310', '_4310_Lag1Trend', '_4310_Lag1Trend_residue', - '_4310_Lag1Trend_residue_Seasonal_DayOfWeek', - '_4310_Lag1Trend_residue_Seasonal_DayOfWeek_residue', - '_4310_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR', - '_4310_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', - '_4310_Trend', '_4310_Trend_residue', '_4310_Cycle', - '_4310_Cycle_residue', '_4310_AR', '_4310_AR_residue', - '_4310_TransformedForecast', '4310_Forecast', + '_4310_Lag1Trend_residue_zeroCycle', + '_4310_Lag1Trend_residue_zeroCycle_residue', + '_4310_Lag1Trend_residue_zeroCycle_residue_NoAR', + '_4310_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', '_4310_Trend', + '_4310_Trend_residue', '_4310_Cycle', '_4310_Cycle_residue', '_4310_AR', + '_4310_AR_residue', '_4310_TransformedForecast', '4310_Forecast', '_4310_TransformedResidue', '4310_Residue'], dtype='object') @@ -50,95 +58,97 @@ memory usage: 14.4 KB None Forecasts Date 4310 4310_Forecast -550 2017-01-01 NaN 209.910714 -551 2017-01-02 NaN 182.392857 -552 2017-01-03 NaN 171.696429 -553 2017-01-04 NaN 166.410714 -554 2017-01-05 NaN 133.125000 -555 2017-01-06 NaN 151.214286 -556 2017-01-07 NaN 179.696429 -557 2017-01-08 NaN 216.607143 -558 2017-01-09 NaN 189.089286 -559 2017-01-10 NaN 178.392857 -560 2017-01-11 NaN 173.107143 -561 2017-01-12 NaN 139.821429 -562 2017-01-13 NaN 157.910714 -563 2017-01-14 NaN 186.392857 -564 2017-01-15 NaN 223.303571 -565 2017-01-16 NaN 195.785714 -566 2017-01-17 NaN 185.089286 -567 2017-01-18 NaN 179.803571 -568 2017-01-19 NaN 146.517857 -569 2017-01-20 NaN 164.607143 -570 2017-01-21 NaN 193.089286 -571 2017-01-22 NaN 230.000000 -572 2017-01-23 NaN 202.482143 -573 2017-01-24 NaN 191.785714 -574 2017-01-25 NaN 186.500000 -575 2017-01-26 NaN 153.214286 -576 2017-01-27 NaN 171.303571 -577 2017-01-28 NaN 199.785714 -578 2017-01-29 NaN 236.696429 -579 2017-01-30 NaN 209.178571 -580 2017-01-31 NaN 198.482143 -581 2017-02-01 NaN 193.196429 -582 2017-02-02 NaN 159.910714 -583 2017-02-03 NaN 178.000000 -584 2017-02-04 NaN 206.482143 -585 2017-02-05 NaN 243.392857 -586 2017-02-06 NaN 215.875000 -587 2017-02-07 NaN 205.178571 -588 2017-02-08 NaN 199.892857 -589 2017-02-09 NaN 166.607143 -590 2017-02-10 NaN 184.696429 -591 2017-02-11 NaN 213.178571 -592 2017-02-12 NaN 250.089286 -593 2017-02-13 NaN 222.571429 -594 2017-02-14 NaN 211.875000 -595 2017-02-15 NaN 206.589286 -596 2017-02-16 NaN 173.303571 -597 2017-02-17 NaN 191.392857 -598 2017-02-18 NaN 219.875000 -599 2017-02-19 NaN 256.785714 -600 2017-02-20 NaN 229.267857 -601 2017-02-21 NaN 218.571429 -602 2017-02-22 NaN 213.285714 -603 2017-02-23 NaN 180.000000 -604 2017-02-24 NaN 198.089286 -605 2017-02-25 NaN 226.571429 -606 2017-02-26 NaN 263.482143 -607 2017-02-27 NaN 235.964286 -608 2017-02-28 NaN 225.267857 -609 2017-03-01 NaN 219.982143 +550 2017-01-01 NaN 173.0 +551 2017-01-02 NaN 173.0 +552 2017-01-03 NaN 173.0 +553 2017-01-04 NaN 173.0 +554 2017-01-05 NaN 173.0 +555 2017-01-06 NaN 173.0 +556 2017-01-07 NaN 173.0 +557 2017-01-08 NaN 173.0 +558 2017-01-09 NaN 173.0 +559 2017-01-10 NaN 173.0 +560 2017-01-11 NaN 173.0 +561 2017-01-12 NaN 173.0 +562 2017-01-13 NaN 173.0 +563 2017-01-14 NaN 173.0 +564 2017-01-15 NaN 173.0 +565 2017-01-16 NaN 173.0 +566 2017-01-17 NaN 173.0 +567 2017-01-18 NaN 173.0 +568 2017-01-19 NaN 173.0 +569 2017-01-20 NaN 173.0 +570 2017-01-21 NaN 173.0 +571 2017-01-22 NaN 173.0 +572 2017-01-23 NaN 173.0 +573 2017-01-24 NaN 173.0 +574 2017-01-25 NaN 173.0 +575 2017-01-26 NaN 173.0 +576 2017-01-27 NaN 173.0 +577 2017-01-28 NaN 173.0 +578 2017-01-29 NaN 173.0 +579 2017-01-30 NaN 173.0 +580 2017-01-31 NaN 173.0 +581 2017-02-01 NaN 173.0 +582 2017-02-02 NaN 173.0 +583 2017-02-03 NaN 173.0 +584 2017-02-04 NaN 173.0 +585 2017-02-05 NaN 173.0 +586 2017-02-06 NaN 173.0 +587 2017-02-07 NaN 173.0 +588 2017-02-08 NaN 173.0 +589 2017-02-09 NaN 173.0 +590 2017-02-10 NaN 173.0 +591 2017-02-11 NaN 173.0 +592 2017-02-12 NaN 173.0 +593 2017-02-13 NaN 173.0 +594 2017-02-14 NaN 173.0 +595 2017-02-15 NaN 173.0 +596 2017-02-16 NaN 173.0 +597 2017-02-17 NaN 173.0 +598 2017-02-18 NaN 173.0 +599 2017-02-19 NaN 173.0 +600 2017-02-20 NaN 173.0 +601 2017-02-21 NaN 173.0 +602 2017-02-22 NaN 173.0 +603 2017-02-23 NaN 173.0 +604 2017-02-24 NaN 173.0 +605 2017-02-25 NaN 173.0 +606 2017-02-26 NaN 173.0 +607 2017-02-27 NaN 173.0 +608 2017-02-28 NaN 173.0 +609 2017-03-01 NaN 173.0 { - "Dataset": { - "Signal": "4310", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4310": { + "Dataset": { + "Signal": "4310", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4310_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_4310_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "36", - "MAE": "39.06013119533527", - "MAPE": "0.1706", - "MASE": "0.9241", - "RMSE": "66.91371457791863" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "41.90816326530612", + "MAPE": "0.1892", + "MASE": "0.9915", + "RMSE": "66.11886328146193" + } } } @@ -147,58 +157,68 @@ Forecasts 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'4311' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4311']' 16.721888303756714 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4311' Length=550 Min=212.0 Max=1077.0 Mean=579.8418181818182 StdDev=184.94583599324335 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4311' Min=212.0 Max=1077.0 Mean=579.8418181818182 StdDev=184.94583599324335 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_4311_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(16)' [ConstantTrend + Seasonal_DayOfWeek + AR] -INFO:pyaf.std:TREND_DETAIL '_4311_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_4311_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] -INFO:pyaf.std:AUTOREG_DETAIL '_4311_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1041 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0.1018338878582292 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4311_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.09977786046329162 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4311_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.0870147207416548 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4311_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.06650279883924963 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 6.875437498092651 +INFO:pyaf.std:START_FORECASTING '['4311']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4311']' 3.504424571990967 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4311 ... 0.1056 0.1134 -1 None Anscombe_4311 ... 0.1056 0.1134 -2 None Anscombe_4311 ... 0.1067 0.1141 -3 None Anscombe_4311 ... 0.1067 0.1141 -4 None Anscombe_4311 ... 0.1085 0.1139 +0 None Anscombe_4311 ... 0.1073 0.1129 +1 None _4311 ... 0.1109 0.1184 +2 None Anscombe_4311 ... 0.1119 0.1155 +3 None Anscombe_4311 ... 0.1128 0.1105 +4 None Anscombe_4311 ... 0.1135 0.1168 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4311', 'row_number', 'Date_Normalized', '_4311', - '_4311_ConstantTrend', '_4311_ConstantTrend_residue', - '_4311_ConstantTrend_residue_Seasonal_DayOfWeek', - '_4311_ConstantTrend_residue_Seasonal_DayOfWeek_residue', - '_4311_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(16)', - '_4311_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(16)_residue', - '_4311_Trend', '_4311_Trend_residue', '_4311_Cycle', - '_4311_Cycle_residue', '_4311_AR', '_4311_AR_residue', - '_4311_TransformedForecast', '4311_Forecast', - '_4311_TransformedResidue', '4311_Residue'], +Forecast Columns Index(['Date', '4311', 'row_number', 'Date_Normalized', 'Anscombe_4311', + 'Anscombe_4311_ConstantTrend', 'Anscombe_4311_ConstantTrend_residue', + 'Anscombe_4311_ConstantTrend_residue_zeroCycle', + 'Anscombe_4311_ConstantTrend_residue_zeroCycle_residue', + 'Anscombe_4311_ConstantTrend_residue_zeroCycle_residue_AR(16)', + 'Anscombe_4311_ConstantTrend_residue_zeroCycle_residue_AR(16)_residue', + 'Anscombe_4311_Trend', 'Anscombe_4311_Trend_residue', + 'Anscombe_4311_Cycle', 'Anscombe_4311_Cycle_residue', + 'Anscombe_4311_AR', 'Anscombe_4311_AR_residue', + 'Anscombe_4311_TransformedForecast', '4311_Forecast', + 'Anscombe_4311_TransformedResidue', '4311_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -213,95 +233,97 @@ memory usage: 14.4 KB None Forecasts Date 4311 4311_Forecast -550 2017-01-01 NaN 535.775248 -551 2017-01-02 NaN 618.159027 -552 2017-01-03 NaN 584.557864 -553 2017-01-04 NaN 608.126297 -554 2017-01-05 NaN 585.371118 -555 2017-01-06 NaN 511.825052 -556 2017-01-07 NaN 413.609079 -557 2017-01-08 NaN 543.047649 -558 2017-01-09 NaN 600.499241 -559 2017-01-10 NaN 586.024670 -560 2017-01-11 NaN 609.387264 -561 2017-01-12 NaN 574.556951 -562 2017-01-13 NaN 491.193524 -563 2017-01-14 NaN 424.189774 -564 2017-01-15 NaN 549.688718 -565 2017-01-16 NaN 616.214559 -566 2017-01-17 NaN 592.756893 -567 2017-01-18 NaN 623.593532 -568 2017-01-19 NaN 582.926863 -569 2017-01-20 NaN 492.373199 -570 2017-01-21 NaN 428.221222 -571 2017-01-22 NaN 558.471412 -572 2017-01-23 NaN 623.590864 -573 2017-01-24 NaN 599.429069 -574 2017-01-25 NaN 632.371199 -575 2017-01-26 NaN 586.908719 -576 2017-01-27 NaN 494.217679 -577 2017-01-28 NaN 429.225828 -578 2017-01-29 NaN 565.121098 -579 2017-01-30 NaN 628.332236 -580 2017-01-31 NaN 605.260659 -581 2017-02-01 NaN 638.270228 -582 2017-02-02 NaN 592.336784 -583 2017-02-03 NaN 497.013210 -584 2017-02-04 NaN 432.044676 -585 2017-02-05 NaN 569.080633 -586 2017-02-06 NaN 633.077719 -587 2017-02-07 NaN 609.935678 -588 2017-02-08 NaN 643.132222 -589 2017-02-09 NaN 597.027284 -590 2017-02-10 NaN 499.969254 -591 2017-02-11 NaN 434.936070 -592 2017-02-12 NaN 572.235155 -593 2017-02-13 NaN 636.943061 -594 2017-02-14 NaN 613.604760 -595 2017-02-15 NaN 647.163111 -596 2017-02-16 NaN 600.805644 -597 2017-02-17 NaN 503.159507 -598 2017-02-18 NaN 437.541377 -599 2017-02-19 NaN 575.097910 -600 2017-02-20 NaN 640.007357 -601 2017-02-21 NaN 616.778625 -602 2017-02-22 NaN 650.450954 -603 2017-02-23 NaN 604.004718 -604 2017-02-24 NaN 506.047035 -605 2017-02-25 NaN 440.020448 -606 2017-02-26 NaN 577.614491 -607 2017-02-27 NaN 642.592755 -608 2017-02-28 NaN 619.462209 -609 2017-03-01 NaN 653.154042 +550 2017-01-01 NaN 480.498363 +551 2017-01-02 NaN 567.082784 +552 2017-01-03 NaN 554.203299 +553 2017-01-04 NaN 563.843863 +554 2017-01-05 NaN 581.989706 +555 2017-01-06 NaN 513.290582 +556 2017-01-07 NaN 420.818452 +557 2017-01-08 NaN 454.880926 +558 2017-01-09 NaN 548.694357 +559 2017-01-10 NaN 560.117768 +560 2017-01-11 NaN 555.488445 +561 2017-01-12 NaN 572.873774 +562 2017-01-13 NaN 519.495526 +563 2017-01-14 NaN 451.712819 +564 2017-01-15 NaN 479.246080 +565 2017-01-16 NaN 548.397792 +566 2017-01-17 NaN 564.267126 +567 2017-01-18 NaN 568.208237 +568 2017-01-19 NaN 575.758856 +569 2017-01-20 NaN 527.972823 +570 2017-01-21 NaN 469.453482 +571 2017-01-22 NaN 488.054997 +572 2017-01-23 NaN 547.864305 +573 2017-01-24 NaN 568.563134 +574 2017-01-25 NaN 571.084447 +575 2017-01-26 NaN 573.466119 +576 2017-01-27 NaN 534.453602 +577 2017-01-28 NaN 486.664170 +578 2017-01-29 NaN 499.525966 +579 2017-01-30 NaN 547.823290 +580 2017-01-31 NaN 570.102783 +581 2017-02-01 NaN 575.380283 +582 2017-02-02 NaN 574.393744 +583 2017-02-03 NaN 540.186062 +584 2017-02-04 NaN 500.017309 +585 2017-02-05 NaN 508.680535 +586 2017-02-06 NaN 549.181289 +587 2017-02-07 NaN 571.748501 +588 2017-02-08 NaN 577.493039 +589 2017-02-09 NaN 574.515893 +590 2017-02-10 NaN 545.303306 +591 2017-02-11 NaN 511.627650 +592 2017-02-12 NaN 517.048101 +593 2017-02-13 NaN 550.547645 +594 2017-02-14 NaN 572.556116 +595 2017-02-15 NaN 579.218117 +596 2017-02-16 NaN 575.201807 +597 2017-02-17 NaN 549.679519 +598 2017-02-18 NaN 521.139791 +599 2017-02-19 NaN 524.377863 +600 2017-02-20 NaN 552.341118 +601 2017-02-21 NaN 573.187032 +602 2017-02-22 NaN 580.148308 +603 2017-02-23 NaN 575.733974 +604 2017-02-24 NaN 553.556806 +605 2017-02-25 NaN 529.238982 +606 2017-02-26 NaN 530.822475 +607 2017-02-27 NaN 554.131848 +608 2017-02-28 NaN 573.559264 +609 2017-03-01 NaN 580.715241 { - "Dataset": { - "Signal": "4311", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4311": { + "Dataset": { + "Signal": "4311", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Anscombe_4311_ConstantTrend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Anscombe", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4311_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(16)", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "55.941788744593175", - "MAPE": "0.1146", - "MASE": "0.7853", - "RMSE": "84.57319828423483" + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "56.45452935503409", + "MAPE": "0.1144", + "MASE": "0.7925", + "RMSE": "89.2716739264236" + } } } @@ -310,44 +332,53 @@ Forecasts 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'4312' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4312']' 16.842002630233765 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4312' Length=550 Min=77.0 Max=6922.0 Mean=236.1890909090909 StdDev=404.07965536635436 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4312' Min=-2942.0 Max=2836.0 Mean=-0.09818181818181818 StdDev=269.55559621577083 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4312_LinearTrend_residue_zeroCycle_residue_NoAR' [LinearTrend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_4312_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_4312_LinearTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_4312_LinearTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.5016 MAPE_Forecast=0.317 MAPE_Test=0.8202 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+INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 5.417109251022339 +INFO:pyaf.std:START_FORECASTING '['4312']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4312']' 3.7149410247802734 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4312 ... 0.3170 0.8202 -1 None Diff_4312 ... 0.3413 0.6492 -2 None Diff_4312 ... 0.3623 0.2006 -3 None Diff_4312 ... 0.3667 0.7747 -4 None _4312 ... 0.3895 0.1973 +0 None Diff_4312 ... 0.2200 0.2332 +1 None Diff_4312 ... 0.2466 0.3224 +2 None Diff_4312 ... 0.3170 0.8202 +3 None Diff_4312 ... 0.3170 0.8202 +4 None _4312 ... 0.3416 0.1902 [5 rows x 8 columns] Forecast Columns Index(['Date', '4312', 'row_number', 'Date_Normalized', 'Diff_4312', - 'Diff_4312_LinearTrend', 'Diff_4312_LinearTrend_residue', - 'Diff_4312_LinearTrend_residue_zeroCycle', - 'Diff_4312_LinearTrend_residue_zeroCycle_residue', - 'Diff_4312_LinearTrend_residue_zeroCycle_residue_NoAR', - 'Diff_4312_LinearTrend_residue_zeroCycle_residue_NoAR_residue', + 'Diff_4312_ConstantTrend', 'Diff_4312_ConstantTrend_residue', + 'Diff_4312_ConstantTrend_residue_Seasonal_WeekOfMonth', + 'Diff_4312_ConstantTrend_residue_Seasonal_WeekOfMonth_residue', + 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'4313' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4313']' 21.91841459274292 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4313' Length=550 Min=7.0 Max=2938.0 Mean=73.16 StdDev=204.94721236100153 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4313' Min=7.0 Max=2938.0 Mean=73.16 StdDev=204.94721236100153 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_4313_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_4313_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_4313_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_4313_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3511 MAPE_Forecast=0.4097 MAPE_Test=0.2377 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3203 SMAPE_Forecast=0.3557 SMAPE_Test=0.2311 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.998 MASE_Test=0.9848 -INFO:pyaf.std:MODEL_L1 L1_Fit=29.316326530612244 L1_Forecast=19.8265306122449 L1_Test=10.966666666666667 -INFO:pyaf.std:MODEL_L2 L2_Fit=116.69229553288723 L2_Forecast=40.22373651952087 L2_Test=14.280522866244546 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:BEST_DECOMPOSITION '_4313_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [ConstantTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4313_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4313_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4313_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.6507 MAPE_Forecast=0.2974 MAPE_Test=0.3465 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.6594 SMAPE_Forecast=0.3713 SMAPE_Test=0.4324 +INFO:pyaf.std:MODEL_MASE MASE_Fit=2.2223 MASE_Forecast=0.9348 MASE_Test=1.567 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-INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 3.070676326751709 +INFO:pyaf.std:START_FORECASTING '['4313']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4313']' 3.543628692626953 Split Transformation ... ForecastMAPE TestMAPE -0 None _4313 ... 0.4097 0.2377 -1 None Anscombe_4313 ... 0.4097 0.2377 -2 None Diff_4313 ... 0.4097 0.2377 -3 None Anscombe_4313 ... 0.4357 0.2459 -4 None Anscombe_4313 ... 0.4393 0.2740 +0 None _4313 ... 0.2974 0.3465 +1 None Anscombe_4313 ... 0.2974 0.3465 +2 None Anscombe_4313 ... 0.3055 0.3146 +3 None _4313 ... 0.3058 0.3146 +4 None Anscombe_4313 ... 0.3107 0.2291 [5 rows x 8 columns] Forecast Columns Index(['Date', '4313', 'row_number', 'Date_Normalized', '_4313', - '_4313_Lag1Trend', '_4313_Lag1Trend_residue', - '_4313_Lag1Trend_residue_zeroCycle', - '_4313_Lag1Trend_residue_zeroCycle_residue', - '_4313_Lag1Trend_residue_zeroCycle_residue_NoAR', - '_4313_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', '_4313_Trend', - '_4313_Trend_residue', '_4313_Cycle', '_4313_Cycle_residue', '_4313_AR', - '_4313_AR_residue', '_4313_TransformedForecast', '4313_Forecast', + '_4313_ConstantTrend', '_4313_ConstantTrend_residue', + '_4313_ConstantTrend_residue_Seasonal_DayOfWeek', + '_4313_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + '_4313_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4313_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4313_Trend', '_4313_Trend_residue', '_4313_Cycle', + '_4313_Cycle_residue', '_4313_AR', '_4313_AR_residue', + '_4313_TransformedForecast', '4313_Forecast', '_4313_TransformedResidue', '4313_Residue'], dtype='object') @@ -518,95 +561,97 @@ memory usage: 14.4 KB None Forecasts Date 4313 4313_Forecast -550 2017-01-01 NaN 40.0 -551 2017-01-02 NaN 40.0 -552 2017-01-03 NaN 40.0 -553 2017-01-04 NaN 40.0 -554 2017-01-05 NaN 40.0 -555 2017-01-06 NaN 40.0 -556 2017-01-07 NaN 40.0 -557 2017-01-08 NaN 40.0 -558 2017-01-09 NaN 40.0 -559 2017-01-10 NaN 40.0 -560 2017-01-11 NaN 40.0 -561 2017-01-12 NaN 40.0 -562 2017-01-13 NaN 40.0 -563 2017-01-14 NaN 40.0 -564 2017-01-15 NaN 40.0 -565 2017-01-16 NaN 40.0 -566 2017-01-17 NaN 40.0 -567 2017-01-18 NaN 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'4314' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4314']' 25.1713604927063 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4314' Length=550 Min=140.0 Max=2409.0 Mean=450.7236363636364 StdDev=399.3568875368753 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4314' Min=1.224744871391589 Max=2.345207879911715 Mean=1.4150993745409572 StdDev=0.21275738803909477 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4314_PolyTrend_residue_zeroCycle_residue_AR(16)' [PolyTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL 'Anscombe_4314_PolyTrend' [PolyTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4314_PolyTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4314_PolyTrend_residue_zeroCycle_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1476 MAPE_Forecast=0.225 MAPE_Test=0.1849 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1437 SMAPE_Forecast=0.225 SMAPE_Test=0.2055 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4511 MASE_Forecast=0.3076 MASE_Test=0.337 -INFO:pyaf.std:MODEL_L1 L1_Fit=63.895602022291946 L1_Forecast=141.063055382824 L1_Test=114.67046577862621 -INFO:pyaf.std:MODEL_L2 L2_Fit=158.48776676259257 L2_Forecast=236.3018316747103 L2_Test=165.37146298402868 -INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(16)' [ConstantTrend + Seasonal_WeekOfYear + AR] +INFO:pyaf.std:TREND_DETAIL 'Anscombe_4314_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear' [Seasonal_WeekOfYear] +INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(16)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.148 MAPE_Forecast=0.2267 MAPE_Test=0.1844 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1453 SMAPE_Forecast=0.2347 SMAPE_Test=0.1902 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4717 MASE_Forecast=0.3178 MASE_Test=0.3257 +INFO:pyaf.std:MODEL_L1 L1_Fit=66.80953459534399 L1_Forecast=145.75116875847297 L1_Test=110.81708464643675 +INFO:pyaf.std:MODEL_L2 L2_Fit=165.30765126001342 L2_Forecast=247.52825420458714 L2_Test=160.00328473531656 +INFO:pyaf.std:MODEL_COMPLEXITY 52 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.3815286295198563 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear -0.07192078010096026 {27: -0.0783316397390229, 28: -0.07498430055005811, 29: -0.059150508521584544, 30: -0.11642594789124461, 31: -0.10947764529059789, 32: -0.12832647277942022, 33: -0.13043831613937762, 34: -0.09637936263814773, 35: -0.09295457504182458, 36: -0.12271221129571486, 37: -0.1297339724870279, 38: -0.12762331539109306, 39: -0.10878490134041763, 40: -0.08273439601093235, 41: -0.09090406470700207, 42: -0.0991257779943675, 43: -0.08205590870671609, 44: -0.06923129894820801, 45: -0.09775183588178415, 46: -0.006260716200132688, 47: -0.08545189705755907, 48: -0.06855978946044883, 49: -0.07596538999035984, 50: -0.0658771788944128, 51: -0.09295457504182458, 52: -0.10463633004079331, 53: -0.06855978946044883, 1: 0.27142657492124944, 2: 0.20781168782464254, 3: 0.175312710543843, 4: 0.15708842537204593, 5: 0.11055419334436722, 6: 0.1461648966549005, 7: 0.13399992380579784, 8: 0.13690518584804123, 9: 0.15651543696938397, 10: 0.1299231751881358, 11: 0.13516269643552548, 12: 0.03975701620173622, 13: -0.018492715936205162, 14: -0.02367599665167841, 15: -0.06520738047982588, 16: -0.06119572546757235, 17: -0.07934549466385854, 18: -0.09158720598276515, 19: -0.09295457504182458, 20: -0.07192078010096026, 21: -0.10878490134041763, 22: -0.10463633004079331, 23: -0.10670892813282773, 24: -0.11782019290128232, 25: -0.10394621119593417, 26: -0.06788862323639289} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag7 0.522923975988095 -INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag1 0.38862326518257956 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag8 -0.22397970569581743 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag14 0.22204347222095414 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag2 0.17230184386673036 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag15 -0.11852410474753171 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag9 -0.0863052847936708 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag5 0.0746677395240203 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag11 -0.054311651672889066 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4314_PolyTrend_residue_zeroCycle_residue_Lag6 0.05115516147964558 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag7 0.48404928071406644 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag1 0.3510893928011044 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag14 0.23161802894449152 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag8 -0.19977647217270103 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag3 -0.12854140722489338 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag2 0.1218286843855863 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag15 -0.11055359550574904 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag10 0.08968818154918401 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag13 0.06796504061042109 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag9 -0.06538939121625759 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 8.098921775817871 +INFO:pyaf.std:START_FORECASTING '['4314']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4314']' 5.065359830856323 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4314 ... 0.2197 0.1866 -1 None Anscombe_4314 ... 0.2197 0.1866 +0 None Anscombe_4314 ... 0.2230 0.1840 +1 None Anscombe_4314 ... 0.2250 0.1849 2 None Anscombe_4314 ... 0.2250 0.1849 -3 None Anscombe_4314 ... 0.2351 0.1652 -4 None Anscombe_4314 ... 0.2376 0.1690 +3 None Anscombe_4314 ... 0.2267 0.1844 +4 None Anscombe_4314 ... 0.2267 0.2109 [5 rows x 8 columns] Forecast Columns Index(['Date', '4314', 'row_number', 'Date_Normalized', 'Anscombe_4314', - 'Date_Normalized_^2', 'Date_Normalized_^3', 'Anscombe_4314_PolyTrend', - 'Anscombe_4314_PolyTrend_residue', - 'Anscombe_4314_PolyTrend_residue_zeroCycle', - 'Anscombe_4314_PolyTrend_residue_zeroCycle_residue', - 'Anscombe_4314_PolyTrend_residue_zeroCycle_residue_AR(16)', - 'Anscombe_4314_PolyTrend_residue_zeroCycle_residue_AR(16)_residue', + 'Anscombe_4314_ConstantTrend', 'Anscombe_4314_ConstantTrend_residue', + 'Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear', + 'Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue', + 'Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(16)', + 'Anscombe_4314_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(16)_residue', 'Anscombe_4314_Trend', 'Anscombe_4314_Trend_residue', 'Anscombe_4314_Cycle', 'Anscombe_4314_Cycle_residue', 'Anscombe_4314_AR', 'Anscombe_4314_AR_residue', @@ -683,95 +736,97 @@ memory usage: 14.4 KB None Forecasts Date 4314 4314_Forecast -550 2017-01-01 NaN 843.951793 -551 2017-01-02 NaN 333.203675 -552 2017-01-03 NaN 301.937414 -553 2017-01-04 NaN 252.409229 -554 2017-01-05 NaN 191.740038 -555 2017-01-06 NaN 169.983174 -556 2017-01-07 NaN 148.883004 -557 2017-01-08 NaN 548.879981 -558 2017-01-09 NaN 164.923552 -559 2017-01-10 NaN 152.226445 -560 2017-01-11 NaN 101.344371 -561 2017-01-12 NaN 42.834408 -562 2017-01-13 NaN 49.336695 -563 2017-01-14 NaN 55.213888 -564 2017-01-15 NaN 340.507470 -565 2017-01-16 NaN 37.495716 -566 2017-01-17 NaN 36.839501 -567 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'4315' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4315']' 17.811147212982178 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4315' Length=550 Min=44.0 Max=15208.0 Mean=148.4181818181818 StdDev=711.9606243174635 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4315' Min=-8310.0 Max=14152.0 Mean=0.005454545454545455 StdDev=740.2405149478432 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4315_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_4315_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_4315_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_4315_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2489 MAPE_Forecast=0.1946 MAPE_Test=0.2001 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3123 SMAPE_Forecast=0.2357 SMAPE_Test=0.2205 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9687 MASE_Forecast=0.9649 MASE_Test=0.791 -INFO:pyaf.std:MODEL_L1 L1_Fit=96.48272855060394 L1_Forecast=30.17156913785931 L1_Test=23.207993197279 -INFO:pyaf.std:MODEL_L2 L2_Fit=846.8729227667936 L2_Forecast=68.34301570255491 L2_Test=36.54836046131836 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4315' Min=44.0 Max=15208.0 Mean=148.4181818181818 StdDev=711.9606243174635 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4315_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [ConstantTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4315_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4315_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4315_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2251 MAPE_Forecast=0.1615 MAPE_Test=0.1793 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.25 SMAPE_Forecast=0.1944 SMAPE_Test=0.2006 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9176 MASE_Forecast=0.8465 MASE_Test=0.7303 +INFO:pyaf.std:MODEL_L1 L1_Fit=91.39030612244898 L1_Forecast=26.46938775510204 L1_Test=21.425 +INFO:pyaf.std:MODEL_L2 L2_Fit=845.4120040741396 L2_Forecast=66.07590718411728 L2_Test=35.52938924327296 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 164.7576530612245 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4315_ConstantTrend_residue_Seasonal_DayOfWeek -76.75765306122449 {2: -73.25765306122449, 3: -75.75765306122449, 4: -79.75765306122449, 5: -87.75765306122449, 6: -77.25765306122449, 0: -74.75765306122449, 1: -74.25765306122449} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.4795186519622803 +INFO:pyaf.std:START_FORECASTING '['4315']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4315']' 3.2365496158599854 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4315 ... 0.1946 0.2001 -1 None _4315 ... 0.2251 0.2291 -2 None Anscombe_4315 ... 0.2395 0.2749 -3 None _4315 ... 0.2514 0.2921 -4 None Anscombe_4315 ... 0.2514 0.2921 +0 None _4315 ... 0.1615 0.1793 +1 None Anscombe_4315 ... 0.1615 0.1793 +2 None _4315 ... 0.1657 0.1880 +3 None Anscombe_4315 ... 0.1657 0.1880 +4 None Diff_4315 ... 0.1946 0.2001 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4315', 'row_number', 'Date_Normalized', 'Diff_4315', - 'Diff_4315_ConstantTrend', 'Diff_4315_ConstantTrend_residue', - 'Diff_4315_ConstantTrend_residue_zeroCycle', - 'Diff_4315_ConstantTrend_residue_zeroCycle_residue', - 'Diff_4315_ConstantTrend_residue_zeroCycle_residue_NoAR', - 'Diff_4315_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', - 'Diff_4315_Trend', 'Diff_4315_Trend_residue', 'Diff_4315_Cycle', - 'Diff_4315_Cycle_residue', 'Diff_4315_AR', 'Diff_4315_AR_residue', - 'Diff_4315_TransformedForecast', '4315_Forecast', - 'Diff_4315_TransformedResidue', '4315_Residue'], +Forecast Columns Index(['Date', '4315', 'row_number', 'Date_Normalized', '_4315', + '_4315_ConstantTrend', '_4315_ConstantTrend_residue', + '_4315_ConstantTrend_residue_Seasonal_DayOfWeek', + '_4315_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + '_4315_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4315_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4315_Trend', '_4315_Trend_residue', '_4315_Cycle', + '_4315_Cycle_residue', '_4315_AR', '_4315_AR_residue', + '_4315_TransformedForecast', '4315_Forecast', + '_4315_TransformedResidue', '4315_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -836,95 +900,97 @@ memory usage: 14.4 KB None Forecasts Date 4315 4315_Forecast -550 2017-01-01 NaN 87.706633 -551 2017-01-02 NaN 87.755102 -552 2017-01-03 NaN 87.803571 -553 2017-01-04 NaN 87.852041 -554 2017-01-05 NaN 87.900510 -555 2017-01-06 NaN 87.948980 -556 2017-01-07 NaN 87.997449 -557 2017-01-08 NaN 88.045918 -558 2017-01-09 NaN 88.094388 -559 2017-01-10 NaN 88.142857 -560 2017-01-11 NaN 88.191327 -561 2017-01-12 NaN 88.239796 -562 2017-01-13 NaN 88.288265 -563 2017-01-14 NaN 88.336735 -564 2017-01-15 NaN 88.385204 -565 2017-01-16 NaN 88.433673 -566 2017-01-17 NaN 88.482143 -567 2017-01-18 NaN 88.530612 -568 2017-01-19 NaN 88.579082 -569 2017-01-20 NaN 88.627551 -570 2017-01-21 NaN 88.676020 -571 2017-01-22 NaN 88.724490 -572 2017-01-23 NaN 88.772959 -573 2017-01-24 NaN 88.821429 -574 2017-01-25 NaN 88.869898 -575 2017-01-26 NaN 88.918367 -576 2017-01-27 NaN 88.966837 -577 2017-01-28 NaN 89.015306 -578 2017-01-29 NaN 89.063776 -579 2017-01-30 NaN 89.112245 -580 2017-01-31 NaN 89.160714 -581 2017-02-01 NaN 89.209184 -582 2017-02-02 NaN 89.257653 -583 2017-02-03 NaN 89.306122 -584 2017-02-04 NaN 89.354592 -585 2017-02-05 NaN 89.403061 -586 2017-02-06 NaN 89.451531 -587 2017-02-07 NaN 89.500000 -588 2017-02-08 NaN 89.548469 -589 2017-02-09 NaN 89.596939 -590 2017-02-10 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NaN 87.5 +607 2017-02-27 NaN 90.0 +608 2017-02-28 NaN 90.5 +609 2017-03-01 NaN 91.5 { - "Dataset": { - "Signal": "4315", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4315": { + "Dataset": { + "Signal": "4315", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "Diff_4315_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "Difference", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "30.17156913785931", - "MAPE": "0.1946", - "MASE": "0.9649", - "RMSE": "68.34301570255491" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4315_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "26.46938775510204", + "MAPE": "0.1615", + "MASE": "0.8465", + "RMSE": "66.07590718411728" + } } } @@ -933,44 +999,53 @@ Forecasts 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'4316' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4316']' 24.982738971710205 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4316' Length=550 Min=9.0 Max=17770.0 Mean=66.37272727272727 StdDev=756.3512466751856 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4316' Min=-17714.0 Max=17079.0 Mean=-0.007272727272727273 StdDev=1049.8429336818213 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4316_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4316_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [ConstantTrend + Seasonal_DayOfWeek + NoAR] INFO:pyaf.std:TREND_DETAIL 'Diff_4316_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_4316_ConstantTrend_residue_zeroCycle' [NoCycle] 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MASE_Forecast=0.7366 MASE_Test=0.8608 +INFO:pyaf.std:MODEL_L1 L1_Fit=58.275510204081634 L1_Forecast=10.153061224489797 L1_Test=8.375 +INFO:pyaf.std:MODEL_L2 L2_Fit=896.8639921460574 L2_Forecast=31.97033063351592 L2_Test=12.182808926242474 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 24.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend -0.025510204081632654 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Diff_4316_ConstantTrend_residue_Seasonal_DayOfWeek 0.025510204081632654 {2: 0.025510204081632654, 3: 1.0255102040816326, 4: -1.9744897959183674, 5: -0.4744897959183674, 6: 7.025510204081633, 0: -3.4744897959183674, 1: -1.9744897959183674} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 4.32230544090271 +INFO:pyaf.std:START_FORECASTING '['4316']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4316']' 3.7694993019104004 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4316 ... 0.4377 0.5914 -1 None _4316 ... 0.4393 0.3061 -2 None Anscombe_4316 ... 0.4606 0.3140 -3 None _4316 ... 0.4864 0.3335 -4 None Anscombe_4316 ... 0.4864 0.3335 +0 None Diff_4316 ... 0.2815 0.2588 +1 None Diff_4316 ... 0.4377 0.5914 +2 None Diff_4316 ... 0.4377 0.5914 +3 None _4316 ... 0.4393 0.3061 +4 None _4316 ... 0.4393 0.3061 [5 rows x 8 columns] Forecast Columns Index(['Date', '4316', 'row_number', 'Date_Normalized', 'Diff_4316', 'Diff_4316_ConstantTrend', 'Diff_4316_ConstantTrend_residue', - 'Diff_4316_ConstantTrend_residue_zeroCycle', - 'Diff_4316_ConstantTrend_residue_zeroCycle_residue', - 'Diff_4316_ConstantTrend_residue_zeroCycle_residue_NoAR', - 'Diff_4316_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', + 'Diff_4316_ConstantTrend_residue_Seasonal_DayOfWeek', + 'Diff_4316_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + 'Diff_4316_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + 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'4317' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4317']' 19.423946380615234 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4317' Length=550 Min=39.0 Max=19064.0 Mean=269.9509090909091 StdDev=874.85451224194 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4317' Min=39.0 Max=19064.0 Mean=269.9509090909091 StdDev=874.85451224194 @@ -1099,33 +1176,42 @@ INFO:pyaf.std:AUTOREG_DETAIL '_4317_Lag1Trend_residue_zeroCycle_residue_AR(16)' INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3676 MAPE_Forecast=0.1973 MAPE_Test=0.2979 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2665 SMAPE_Forecast=0.1974 SMAPE_Test=0.2631 INFO:pyaf.std:MODEL_MASE MASE_Fit=1.2147 MASE_Forecast=0.9588 MASE_Test=1.1877 -INFO:pyaf.std:MODEL_L1 L1_Fit=168.63627410026078 L1_Forecast=32.2234672320385 L1_Test=76.77942214730872 +INFO:pyaf.std:MODEL_L1 L1_Fit=168.63627410026078 L1_Forecast=32.22346723203851 L1_Test=76.77942214730871 INFO:pyaf.std:MODEL_L2 L2_Fit=924.3445045927557 L2_Forecast=61.33976676576624 L2_Test=162.45090415309318 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 115.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4317_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4317_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.5264848932581623 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4317_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.49550602854915266 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4317_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.4217555159262829 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4317_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.3426695634314831 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4317_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.30324926308213385 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4317_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.2584651753391076 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4317_Lag1Trend_residue_zeroCycle_residue_Lag7 -0.22953736968552294 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4317_Lag1Trend_residue_zeroCycle_residue_Lag8 -0.21841206473307614 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4317_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.199721737469635 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4317_Lag1Trend_residue_zeroCycle_residue_Lag10 -0.17289342107795852 +INFO:pyaf.std:AR_MODEL_COEFF 1 _4317_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.5264848932581626 +INFO:pyaf.std:AR_MODEL_COEFF 2 _4317_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.49550602854915304 +INFO:pyaf.std:AR_MODEL_COEFF 3 _4317_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.4217555159262826 +INFO:pyaf.std:AR_MODEL_COEFF 4 _4317_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.34266956343148325 +INFO:pyaf.std:AR_MODEL_COEFF 5 _4317_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.30324926308213374 +INFO:pyaf.std:AR_MODEL_COEFF 6 _4317_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.25846517533910757 +INFO:pyaf.std:AR_MODEL_COEFF 7 _4317_Lag1Trend_residue_zeroCycle_residue_Lag7 -0.22953736968552277 +INFO:pyaf.std:AR_MODEL_COEFF 8 _4317_Lag1Trend_residue_zeroCycle_residue_Lag8 -0.21841206473307617 +INFO:pyaf.std:AR_MODEL_COEFF 9 _4317_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.1997217374696348 +INFO:pyaf.std:AR_MODEL_COEFF 10 _4317_Lag1Trend_residue_zeroCycle_residue_Lag10 -0.17289342107795838 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 6.03921365737915 +INFO:pyaf.std:START_FORECASTING '['4317']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4317']' 3.837716579437256 Split Transformation ... ForecastMAPE TestMAPE -0 None _4317 ... 0.1973 0.2979 -1 None Anscombe_4317 ... 0.2086 0.2259 -2 None _4317 ... 0.2221 0.2183 -3 None Anscombe_4317 ... 0.2221 0.2183 -4 None Diff_4317 ... 0.2221 0.2183 +0 None _4317 ... 0.1883 0.3029 +1 None _4317 ... 0.1890 0.2792 +2 None _4317 ... 0.1973 0.2979 +3 None Anscombe_4317 ... 0.1984 0.2152 +4 None _4317 ... 0.1994 0.2979 [5 rows x 8 columns] Forecast Columns Index(['Date', '4317', 'row_number', 'Date_Normalized', '_4317', @@ -1216,31 +1302,33 @@ Forecasts { - "Dataset": { - "Signal": "4317", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4317": { + "Dataset": { + "Signal": "4317", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_4317_Lag1Trend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4317_Lag1Trend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "32.2234672320385", - "MAPE": "0.1973", - "MASE": "0.9588", - "RMSE": "61.33976676576624" + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "32.22346723203851", + "MAPE": "0.1973", + "MASE": "0.9588", + "RMSE": "61.33976676576624" + } } } @@ -1250,7 +1338,7 @@ Forecasts {"Date":{"490":"2016-11-02T00:00:00.000Z","491":"2016-11-03T00:00:00.000Z","492":"2016-11-04T00:00:00.000Z","493":"2016-11-05T00:00:00.000Z","494":"2016-11-06T00:00:00.000Z","495":"2016-11-07T00:00:00.000Z","496":"2016-11-08T00:00:00.000Z","497":"2016-11-09T00:00:00.000Z","498":"2016-11-10T00:00:00.000Z","499":"2016-11-11T00:00:00.000Z","500":"2016-11-12T00:00:00.000Z","501":"2016-11-13T00:00:00.000Z","502":"2016-11-14T00:00:00.000Z","503":"2016-11-15T00:00:00.000Z","504":"2016-11-16T00:00:00.000Z","505":"2016-11-17T00:00:00.000Z","506":"2016-11-18T00:00:00.000Z","507":"2016-11-19T00:00:00.000Z","508":"2016-11-20T00:00:00.000Z","509":"2016-11-21T00:00:00.000Z","510":"2016-11-22T00:00:00.000Z","511":"2016-11-23T00:00:00.000Z","512":"2016-11-24T00:00:00.000Z","513":"2016-11-25T00:00:00.000Z","514":"2016-11-26T00:00:00.000Z","515":"2016-11-27T00:00:00.000Z","516":"2016-11-28T00:00:00.000Z","517":"2016-11-29T00:00:00.000Z","518":"2016-11-30T00:00:00.000Z","519":"2016-12-01T00:00:00.000Z","520":"2016-12-02T00:00:00.000Z","521":"2016-12-03T00:00:00.000Z","522":"2016-12-04T00:00:00.000Z","523":"2016-12-05T00:00:00.000Z","524":"2016-12-06T00:00:00.000Z","525":"2016-12-07T00:00:00.000Z","526":"2016-12-08T00:00:00.000Z","527":"2016-12-09T00:00:00.000Z","528":"2016-12-10T00:00:00.000Z","529":"2016-12-11T00:00:00.000Z","530":"2016-12-12T00:00:00.000Z","531":"2016-12-13T00:00:00.000Z","532":"2016-12-14T00:00:00.000Z","533":"2016-12-15T00:00:00.000Z","534":"2016-12-16T00:00:00.000Z","535":"2016-12-17T00:00:00.000Z","536":"2016-12-18T00:00:00.000Z","537":"2016-12-19T00:00:00.000Z","538":"2016-12-20T00:00:00.000Z","539":"2016-12-21T00:00:00.000Z","540":"2016-12-22T00:00:00.000Z","541":"2016-12-23T00:00:00.000Z","542":"2016-12-24T00:00:00.000Z","543":"2016-12-25T00:00:00.000Z","544":"2016-12-26T00:00:00.000Z","545":"2016-12-27T00:00:00.000Z","546":"2016-12-28T00:00:00.000Z","547":"2016-12-29T00:00:00.000Z","548":"2016-12-30T00:00:00.000Z","549":"2016-12-31T00:00:00.000Z","550":"2017-01-01T00:00:00.000Z","551":"2017-01-02T00:00:00.000Z","552":"2017-01-03T00:00:00.000Z","553":"2017-01-04T00:00:00.000Z","554":"2017-01-05T00:00:00.000Z","555":"2017-01-06T00:00:00.000Z","556":"2017-01-07T00:00:00.000Z","557":"2017-01-08T00:00:00.000Z","558":"2017-01-09T00:00:00.000Z","559":"2017-01-10T00:00:00.000Z","560":"2017-01-11T00:00:00.000Z","561":"2017-01-12T00:00:00.000Z","562":"2017-01-13T00:00:00.000Z","563":"2017-01-14T00:00:00.000Z","564":"2017-01-15T00:00:00.000Z","565":"2017-01-16T00:00:00.000Z","566":"2017-01-17T00:00:00.000Z","567":"2017-01-18T00:00:00.000Z","568":"2017-01-19T00:00:00.000Z","569":"2017-01-20T00:00:00.000Z","570":"2017-01-21T00:00:00.000Z","571":"2017-01-22T00:00:00.000Z","572":"2017-01-23T00:00:00.000Z","573":"2017-01-24T00:00:00.000Z","574":"2017-01-25T00:00:00.000Z","575":"2017-01-26T00:00:00.000Z","576":"2017-01-27T00:00:00.000Z","577":"2017-01-28T00:00:00.000Z","578":"2017-01-29T00:00:00.000Z","579":"2017-01-30T00:00:00.000Z","580":"2017-01-31T00:00:00.000Z","581":"2017-02-01T00:00:00.000Z","582":"2017-02-02T00:00:00.000Z","583":"2017-02-03T00:00:00.000Z","584":"2017-02-04T00:00:00.000Z","585":"2017-02-05T00:00:00.000Z","586":"2017-02-06T00:00:00.000Z","587":"2017-02-07T00:00:00.000Z","588":"2017-02-08T00:00:00.000Z","589":"2017-02-09T00:00:00.000Z","590":"2017-02-10T00:00:00.000Z","591":"2017-02-11T00:00:00.000Z","592":"2017-02-12T00:00:00.000Z","593":"2017-02-13T00:00:00.000Z","594":"2017-02-14T00:00:00.000Z","595":"2017-02-15T00:00:00.000Z","596":"2017-02-16T00:00:00.000Z","597":"2017-02-17T00:00:00.000Z","598":"2017-02-18T00:00:00.000Z","599":"2017-02-19T00:00:00.000Z","600":"2017-02-20T00:00:00.000Z","601":"2017-02-21T00:00:00.000Z","602":"2017-02-22T00:00:00.000Z","603":"2017-02-23T00:00:00.000Z","604":"2017-02-24T00:00:00.000Z","605":"2017-02-25T00:00:00.000Z","606":"2017-02-26T00:00:00.000Z","607":"2017-02-27T00:00:00.000Z","608":"2017-02-28T00:00:00.000Z","609":"2017-03-01T00:00:00.000Z"},"4317":{"490":322.0,"491":203.0,"492":253.0,"493":240.0,"494":305.0,"495":357.0,"496":373.0,"497":359.0,"498":698.0,"499":1464.0,"500":349.0,"501":311.0,"502":281.0,"503":274.0,"504":247.0,"505":200.0,"506":167.0,"507":170.0,"508":213.0,"509":210.0,"510":195.0,"511":241.0,"512":217.0,"513":153.0,"514":155.0,"515":193.0,"516":273.0,"517":214.0,"518":237.0,"519":173.0,"520":149.0,"521":151.0,"522":178.0,"523":223.0,"524":243.0,"525":171.0,"526":172.0,"527":158.0,"528":166.0,"529":179.0,"530":243.0,"531":227.0,"532":232.0,"533":150.0,"534":118.0,"535":117.0,"536":127.0,"537":126.0,"538":129.0,"539":125.0,"540":110.0,"541":103.0,"542":66.0,"543":63.0,"544":109.0,"545":114.0,"546":107.0,"547":81.0,"548":109.0,"549":108.0,"550":null,"551":null,"552":null,"553":null,"554":null,"555":null,"556":null,"557":null,"558":null,"559":null,"560":null,"561":null,"562":null,"563":null,"564":null,"565":null,"566":null,"567":null,"568":null,"569":null,"570":null,"571":null,"572":null,"573":null,"574":null,"575":null,"576":null,"577":n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'4318' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4318' 10.586178541183472 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4318']' 19.08194136619568 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4318' Length=550 Min=293.0 Max=3804.0 Mean=1486.861818181818 StdDev=533.0103930390131 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4318' Min=293.0 Max=3804.0 Mean=1486.861818181818 StdDev=533.0103930390131 @@ -1262,33 +1350,42 @@ INFO:pyaf.std:AUTOREG_DETAIL '_4318_ConstantTrend_residue_zeroCycle_residue_AR(1 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0542 MAPE_Forecast=0.0706 MAPE_Test=0.0445 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.054 SMAPE_Forecast=0.0703 SMAPE_Test=0.0448 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7536 MASE_Forecast=0.7535 MASE_Test=0.6289 -INFO:pyaf.std:MODEL_L1 L1_Fit=66.6774910214519 L1_Forecast=143.84553044233235 L1_Test=85.61571323832341 -INFO:pyaf.std:MODEL_L2 L2_Fit=89.52070758089452 L2_Forecast=252.7802066432823 L2_Test=112.85805488152494 +INFO:pyaf.std:MODEL_L1 L1_Fit=66.67749102145189 L1_Forecast=143.84553044233235 L1_Test=85.61571323832338 +INFO:pyaf.std:MODEL_L2 L2_Fit=89.52070758089451 L2_Forecast=252.7802066432823 L2_Test=112.858054881525 INFO:pyaf.std:MODEL_COMPLEXITY 16 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1312.9285714285713 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4318_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4318_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5527862107734842 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4318_ConstantTrend_residue_zeroCycle_residue_Lag7 0.4892666027980434 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4318_ConstantTrend_residue_zeroCycle_residue_Lag14 0.192654738589051 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4318_ConstantTrend_residue_zeroCycle_residue_Lag2 0.17655103753180718 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4318_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.17386300656458692 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4318_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.1468278304309661 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4318_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.13519932037226112 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4318_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.11157019338031077 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4318_ConstantTrend_residue_zeroCycle_residue_Lag5 0.1005871365401626 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4318_ConstantTrend_residue_zeroCycle_residue_Lag3 0.09937609804755068 +INFO:pyaf.std:AR_MODEL_COEFF 1 _4318_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5527862107734841 +INFO:pyaf.std:AR_MODEL_COEFF 2 _4318_ConstantTrend_residue_zeroCycle_residue_Lag7 0.4892666027980439 +INFO:pyaf.std:AR_MODEL_COEFF 3 _4318_ConstantTrend_residue_zeroCycle_residue_Lag14 0.19265473858905108 +INFO:pyaf.std:AR_MODEL_COEFF 4 _4318_ConstantTrend_residue_zeroCycle_residue_Lag2 0.17655103753180748 +INFO:pyaf.std:AR_MODEL_COEFF 5 _4318_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.17386300656458717 +INFO:pyaf.std:AR_MODEL_COEFF 6 _4318_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.14682783043096648 +INFO:pyaf.std:AR_MODEL_COEFF 7 _4318_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.13519932037226123 +INFO:pyaf.std:AR_MODEL_COEFF 8 _4318_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.11157019338031063 +INFO:pyaf.std:AR_MODEL_COEFF 9 _4318_ConstantTrend_residue_zeroCycle_residue_Lag5 0.10058713654016255 +INFO:pyaf.std:AR_MODEL_COEFF 10 _4318_ConstantTrend_residue_zeroCycle_residue_Lag3 0.09937609804755046 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 4.8212502002716064 +INFO:pyaf.std:START_FORECASTING '['4318']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4318']' 4.773563861846924 Split Transformation ... ForecastMAPE TestMAPE -0 None _4318 ... 0.0675 0.0413 -1 None _4318 ... 0.0675 0.0413 -2 None _4318 ... 0.0681 0.0414 -3 None _4318 ... 0.0681 0.0414 -4 None _4318 ... 0.0683 0.0416 +0 None Anscombe_4318 ... 0.0660 0.0440 +1 None _4318 ... 0.0668 0.0432 +2 None _4318 ... 0.0678 0.0411 +3 None _4318 ... 0.0678 0.0411 +4 None _4318 ... 0.0682 0.0466 [5 rows x 8 columns] Forecast Columns Index(['Date', '4318', 'row_number', 'Date_Normalized', '_4318', @@ -1379,31 +1476,33 @@ Forecasts { - "Dataset": { - "Signal": "4318", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4318": { + "Dataset": { + "Signal": "4318", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_4318_ConstantTrend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4318_ConstantTrend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "16", - "MAE": "143.84553044233235", - "MAPE": "0.0706", - "MASE": "0.7535", - "RMSE": "252.7802066432823" + "Model_Performance": { + "COMPLEXITY": "16", + "MAE": "143.84553044233235", + "MAPE": "0.0706", + "MASE": "0.7535", + "RMSE": "252.7802066432823" + } } } @@ -1412,8 +1511,8 @@ Forecasts 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'4319' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4319']' 23.991790294647217 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4319' Length=550 Min=392.0 Max=3513.0 Mean=786.5490909090909 StdDev=317.1470326478799 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4319' Min=392.0 Max=3513.0 Mean=786.5490909090909 StdDev=317.1470326478799 @@ -1425,33 +1524,42 @@ INFO:pyaf.std:AUTOREG_DETAIL '_4319_Lag1Trend_residue_zeroCycle_residue_AR(16)' INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1277 MAPE_Forecast=0.1306 MAPE_Test=0.0711 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1267 SMAPE_Forecast=0.1273 SMAPE_Test=0.0713 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8997 MASE_Forecast=0.8877 MASE_Test=0.8757 -INFO:pyaf.std:MODEL_L1 L1_Fit=121.620910748658 L1_Forecast=94.68931913239857 L1_Test=52.60324794311696 -INFO:pyaf.std:MODEL_L2 L2_Fit=255.70393512065937 L2_Forecast=217.62056139257993 L2_Test=66.77802291712565 +INFO:pyaf.std:MODEL_L1 L1_Fit=121.62091074865798 L1_Forecast=94.68931913239855 L1_Test=52.60324794311695 +INFO:pyaf.std:MODEL_L2 L2_Fit=255.70393512065937 L2_Forecast=217.6205613925799 L2_Test=66.77802291712564 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 861.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4319_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4319_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.40026630827682663 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4319_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.329360283846814 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4319_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.26690244630534693 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4319_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.2575159634380123 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4319_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.2422588954947471 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4319_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.23738769331444695 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4319_Lag1Trend_residue_zeroCycle_residue_Lag7 -0.22456206882360089 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4319_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.18183902958842607 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4319_Lag1Trend_residue_zeroCycle_residue_Lag11 -0.1680400437484835 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4319_Lag1Trend_residue_zeroCycle_residue_Lag8 -0.16426115266095265 +INFO:pyaf.std:AR_MODEL_COEFF 1 _4319_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.4002663082768269 +INFO:pyaf.std:AR_MODEL_COEFF 2 _4319_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.32936028384681365 +INFO:pyaf.std:AR_MODEL_COEFF 3 _4319_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.26690244630534676 +INFO:pyaf.std:AR_MODEL_COEFF 4 _4319_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.25751596343801264 +INFO:pyaf.std:AR_MODEL_COEFF 5 _4319_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.24225889549474688 +INFO:pyaf.std:AR_MODEL_COEFF 6 _4319_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.2373876933144469 +INFO:pyaf.std:AR_MODEL_COEFF 7 _4319_Lag1Trend_residue_zeroCycle_residue_Lag7 -0.2245620688236009 +INFO:pyaf.std:AR_MODEL_COEFF 8 _4319_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.18183902958842627 +INFO:pyaf.std:AR_MODEL_COEFF 9 _4319_Lag1Trend_residue_zeroCycle_residue_Lag11 -0.16804004374848341 +INFO:pyaf.std:AR_MODEL_COEFF 10 _4319_Lag1Trend_residue_zeroCycle_residue_Lag8 -0.16426115266095276 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 6.081468820571899 +INFO:pyaf.std:START_FORECASTING '['4319']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4319']' 5.701129198074341 Split Transformation ... ForecastMAPE TestMAPE -0 None _4319 ... 0.1306 0.0711 -1 None Anscombe_4319 ... 0.1347 0.1680 -2 None Anscombe_4319 ... 0.1378 0.1670 -3 None Anscombe_4319 ... 0.1378 0.1670 -4 None Anscombe_4319 ... 0.1395 0.0736 +0 None _4319 ... 0.1301 0.0772 +1 None _4319 ... 0.1306 0.0711 +2 None _4319 ... 0.1306 0.0711 +3 None _4319 ... 0.1322 0.0806 +4 None Anscombe_4319 ... 0.1347 0.1680 [5 rows x 8 columns] Forecast Columns Index(['Date', '4319', 'row_number', 'Date_Normalized', '_4319', @@ -1542,31 +1650,33 @@ Forecasts { - "Dataset": { - "Signal": "4319", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4319": { + "Dataset": { + "Signal": "4319", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4319_Lag1Trend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "94.68931913239857", - "MAPE": "0.1306", - "MASE": "0.8877", - "RMSE": "217.62056139257993" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_4319_Lag1Trend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "94.68931913239855", + "MAPE": "0.1306", + "MASE": "0.8877", + "RMSE": "217.6205613925799" + } } } @@ -1576,47 +1686,56 @@ Forecasts {"Date":{"490":"2016-11-02T00:00:00.000Z","491":"2016-11-03T00:00:00.000Z","492":"2016-11-04T00:00:00.000Z","493":"2016-11-05T00:00:00.000Z","494":"2016-11-06T00:00:00.000Z","495":"2016-11-07T00:00:00.000Z","496":"2016-11-08T00:00:00.000Z","497":"2016-11-09T00:00:00.000Z","498":"2016-11-10T00:00:00.000Z","499":"2016-11-11T00:00:00.000Z","500":"2016-11-12T00:00:00.000Z","501":"2016-11-13T00:00:00.000Z","502":"2016-11-14T00:00:00.000Z","503":"2016-11-15T00:00:00.000Z","504":"2016-11-16T00:00:00.000Z","505":"2016-11-17T00:00:00.000Z","506":"2016-11-18T00:00:00.000Z","507":"2016-11-19T00:00:00.000Z","508":"2016-11-20T00:00:00.000Z","509":"2016-11-21T00:00:00.000Z","510":"2016-11-22T00:00:00.000Z","511":"2016-11-23T00:00:00.000Z","512":"2016-11-24T00:00:00.000Z","513":"2016-11-25T00:00:00.000Z","514":"2016-11-26T00:00:00.000Z","515":"2016-11-27T00:00:00.000Z","516":"2016-11-28T00:00:00.000Z","517":"2016-11-29T00:00:00.000Z","518":"2016-11-30T00:00:00.000Z","519":"2016-12-01T00:00:00.000Z","520":"2016-12-02T00:00:00.000Z","521":"2016-12-03T00:00:00.000Z","522":"2016-12-04T00:00:00.000Z","523":"2016-12-05T00:00:00.000Z","524":"2016-12-06T00:00:00.000Z","525":"2016-12-07T00:00:00.000Z","526":"2016-12-08T00:00:00.000Z","527":"2016-12-09T00:00:00.000Z","528":"2016-12-10T00:00:00.000Z","529":"2016-12-11T00:00:00.000Z","530":"2016-12-12T00:00:00.000Z","531":"2016-12-13T00:00:00.000Z","532":"2016-12-14T00:00:00.000Z","533":"2016-12-15T00:00:00.000Z","534":"2016-12-16T00:00:00.000Z","535":"2016-12-17T00:00:00.000Z","536":"2016-12-18T00:00:00.000Z","537":"2016-12-19T00:00:00.000Z","538":"2016-12-20T00:00:00.000Z","539":"2016-12-21T00:00:00.000Z","540":"2016-12-22T00:00:00.000Z","541":"2016-12-23T00:00:00.000Z","542":"2016-12-24T00:00:00.000Z","543":"2016-12-25T00:00:00.000Z","544":"2016-12-26T00:00:00.000Z","545":"2016-12-27T00:00:00.000Z","546":"2016-12-28T00:00:00.000Z","547":"2016-12-29T00:00:00.000Z","548":"2016-12-30T00:00:00.000Z","549":"2016-12-31T00:00:00.000Z","550":"2017-01-01T00:00:00.000Z","551":"2017-01-02T00:00:00.000Z","552":"2017-01-03T00:00:00.000Z","553":"2017-01-04T00:00:00.000Z","554":"2017-01-05T00:00:00.000Z","555":"2017-01-06T00:00:00.000Z","556":"2017-01-07T00:00:00.000Z","557":"2017-01-08T00:00:00.000Z","558":"2017-01-09T00:00:00.000Z","559":"2017-01-10T00:00:00.000Z","560":"2017-01-11T00:00:00.000Z","561":"2017-01-12T00:00:00.000Z","562":"2017-01-13T00:00:00.000Z","563":"2017-01-14T00:00:00.000Z","564":"2017-01-15T00:00:00.000Z","565":"2017-01-16T00:00:00.000Z","566":"2017-01-17T00:00:00.000Z","567":"2017-01-18T00:00:00.000Z","568":"2017-01-19T00:00:00.000Z","569":"2017-01-20T00:00:00.000Z","570":"2017-01-21T00:00:00.000Z","571":"2017-01-22T00:00:00.000Z","572":"2017-01-23T00:00:00.000Z","573":"2017-01-24T00:00:00.000Z","574":"2017-01-25T00:00:00.000Z","575":"2017-01-26T00:00:00.000Z","576":"2017-01-27T00:00:00.000Z","577":"2017-01-28T00:00:00.000Z","578":"2017-01-29T00:00:00.000Z","579":"2017-01-30T00:00:00.000Z","580":"2017-01-31T00:00:00.000Z","581":"2017-02-01T00:00:00.000Z","582":"2017-02-02T00:00:00.000Z","583":"2017-02-03T00:00:00.000Z","584":"2017-02-04T00:00:00.000Z","585":"2017-02-05T00:00:00.000Z","586":"2017-02-06T00:00:00.000Z","587":"2017-02-07T00:00:00.000Z","588":"2017-02-08T00:00:00.000Z","589":"2017-02-09T00:00:00.000Z","590":"2017-02-10T00:00:00.000Z","591":"2017-02-11T00:00:00.000Z","592":"2017-02-12T00:00:00.000Z","593":"2017-02-13T00:00:00.000Z","594":"2017-02-14T00:00:00.000Z","595":"2017-02-15T00:00:00.000Z","596":"2017-02-16T00:00:00.000Z","597":"2017-02-17T00:00:00.000Z","598":"2017-02-18T00:00:00.000Z","599":"2017-02-19T00:00:00.000Z","600":"2017-02-20T00:00:00.000Z","601":"2017-02-21T00:00:00.000Z","602":"2017-02-22T00:00:00.000Z","603":"2017-02-23T00:00:00.000Z","604":"2017-02-24T00:00:00.000Z","605":"2017-02-25T00:00:00.000Z","606":"2017-02-26T00:00:00.000Z","607":"2017-02-27T00:00:00.000Z","608":"2017-02-28T00:00:00.000Z","609":"2017-03-01T00:00:00.000Z"},"4319":{"490":663.0,"491":682.0,"492":697.0,"493":879.0,"494":705.0,"495":613.0,"496":796.0,"497":678.0,"498":699.0,"499":818.0,"500":821.0,"501":746.0,"502":726.0,"503":763.0,"504":722.0,"505":708.0,"506":689.0,"507":809.0,"508":770.0,"509":773.0,"510":707.0,"511":801.0,"512":862.0,"513":713.0,"514":761.0,"515":748.0,"516":805.0,"517":776.0,"518":804.0,"519":708.0,"520":748.0,"521":829.0,"522":859.0,"523":813.0,"524":760.0,"525":775.0,"526":848.0,"527":782.0,"528":859.0,"529":743.0,"530":746.0,"531":812.0,"532":733.0,"533":691.0,"534":684.0,"535":688.0,"536":628.0,"537":702.0,"538":716.0,"539":687.0,"540":691.0,"541":657.0,"542":734.0,"543":576.0,"544":698.0,"545":662.0,"546":720.0,"547":654.0,"548":724.0,"549":715.0,"550":null,"551":null,"552":null,"553":null,"554":null,"555":null,"556":null,"557":null,"558":null,"559":null,"560":null,"561":null,"562":null,"563":null,"564":null,"565":null,"566":null,"567":null,"568":null,"569":null,"570":null,"571":null,"572":null,"573":null,"574":null,"575":null,"576":null,"577"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'4320' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4320' 14.485020160675049 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4320']' 23.661595821380615 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4320' Length=550 Min=39.0 Max=9138.0 Mean=122.10363636363637 StdDev=437.39192970217806 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4320' Min=-6064.0 Max=5870.0 Mean=0.04 StdDev=401.18169794201276 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4320_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_4320_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_4320_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_4320_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3395 MAPE_Forecast=0.1938 MAPE_Test=0.1788 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.4548 SMAPE_Forecast=0.2337 SMAPE_Test=0.204 -INFO:pyaf.std:MODEL_MASE MASE_Fit=1.1408 MASE_Forecast=1.0122 MASE_Test=1.2116 -INFO:pyaf.std:MODEL_L1 L1_Fit=86.36156809662639 L1_Forecast=23.020720533111202 L1_Test=16.71598639455787 -INFO:pyaf.std:MODEL_L2 L2_Fit=523.0825625489877 L2_Forecast=54.30451233913719 L2_Test=22.468788183934954 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4320' Min=39.0 Max=9138.0 Mean=122.10363636363637 StdDev=437.39192970217806 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4320_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [ConstantTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4320_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4320_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4320_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2593 MAPE_Forecast=0.182 MAPE_Test=0.1512 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2939 SMAPE_Forecast=0.1999 SMAPE_Test=0.1636 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.0017 MASE_Forecast=0.9045 MASE_Test=0.9966 +INFO:pyaf.std:MODEL_L1 L1_Fit=75.83418367346938 L1_Forecast=20.571428571428573 L1_Test=13.75 +INFO:pyaf.std:MODEL_L2 L2_Fit=520.1478596691207 L2_Forecast=51.590370482587275 L2_Test=18.757442967170835 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 137.15051020408163 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4320_ConstantTrend_residue_Seasonal_DayOfWeek -62.15051020408163 {2: -60.15051020408163, 3: -65.65051020408163, 4: -66.65051020408163, 5: -67.15051020408163, 6: -60.65051020408163, 0: -56.15051020408163, 1: -61.15051020408163} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 3.5782246589660645 +INFO:pyaf.std:START_FORECASTING '['4320']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4320']' 2.991671085357666 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4320 ... 0.1938 0.1788 -1 None _4320 ... 0.2559 0.1546 -2 None _4320 ... 0.2686 0.1628 -3 None Anscombe_4320 ... 0.2686 0.1628 -4 None Diff_4320 ... 0.2686 0.1628 +0 None _4320 ... 0.1820 0.1512 +1 None Anscombe_4320 ... 0.1820 0.1512 +2 None _4320 ... 0.1836 0.1526 +3 None Anscombe_4320 ... 0.1836 0.1526 +4 None Diff_4320 ... 0.1938 0.1788 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4320', 'row_number', 'Date_Normalized', 'Diff_4320', - 'Diff_4320_ConstantTrend', 'Diff_4320_ConstantTrend_residue', - 'Diff_4320_ConstantTrend_residue_zeroCycle', - 'Diff_4320_ConstantTrend_residue_zeroCycle_residue', - 'Diff_4320_ConstantTrend_residue_zeroCycle_residue_NoAR', - 'Diff_4320_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', - 'Diff_4320_Trend', 'Diff_4320_Trend_residue', 'Diff_4320_Cycle', - 'Diff_4320_Cycle_residue', 'Diff_4320_AR', 'Diff_4320_AR_residue', - 'Diff_4320_TransformedForecast', '4320_Forecast', - 'Diff_4320_TransformedResidue', '4320_Residue'], +Forecast Columns Index(['Date', '4320', 'row_number', 'Date_Normalized', '_4320', + '_4320_ConstantTrend', '_4320_ConstantTrend_residue', + '_4320_ConstantTrend_residue_Seasonal_DayOfWeek', + '_4320_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + '_4320_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4320_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4320_Trend', '_4320_Trend_residue', '_4320_Cycle', + '_4320_Cycle_residue', '_4320_AR', '_4320_AR_residue', + '_4320_TransformedForecast', '4320_Forecast', + '_4320_TransformedResidue', '4320_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -1631,95 +1750,97 @@ memory usage: 14.4 KB None Forecasts Date 4320 4320_Forecast -550 2017-01-01 NaN 70.112245 -551 2017-01-02 NaN 70.163265 -552 2017-01-03 NaN 70.214286 -553 2017-01-04 NaN 70.265306 -554 2017-01-05 NaN 70.316327 -555 2017-01-06 NaN 70.367347 -556 2017-01-07 NaN 70.418367 -557 2017-01-08 NaN 70.469388 -558 2017-01-09 NaN 70.520408 -559 2017-01-10 NaN 70.571429 -560 2017-01-11 NaN 70.622449 -561 2017-01-12 NaN 70.673469 -562 2017-01-13 NaN 70.724490 -563 2017-01-14 NaN 70.775510 -564 2017-01-15 NaN 70.826531 -565 2017-01-16 NaN 70.877551 -566 2017-01-17 NaN 70.928571 -567 2017-01-18 NaN 70.979592 -568 2017-01-19 NaN 71.030612 -569 2017-01-20 NaN 71.081633 -570 2017-01-21 NaN 71.132653 -571 2017-01-22 NaN 71.183673 -572 2017-01-23 NaN 71.234694 -573 2017-01-24 NaN 71.285714 -574 2017-01-25 NaN 71.336735 -575 2017-01-26 NaN 71.387755 -576 2017-01-27 NaN 71.438776 -577 2017-01-28 NaN 71.489796 -578 2017-01-29 NaN 71.540816 -579 2017-01-30 NaN 71.591837 -580 2017-01-31 NaN 71.642857 -581 2017-02-01 NaN 71.693878 -582 2017-02-02 NaN 71.744898 -583 2017-02-03 NaN 71.795918 -584 2017-02-04 NaN 71.846939 -585 2017-02-05 NaN 71.897959 -586 2017-02-06 NaN 71.948980 -587 2017-02-07 NaN 72.000000 -588 2017-02-08 NaN 72.051020 -589 2017-02-09 NaN 72.102041 -590 2017-02-10 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'4322' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4322' 8.114256858825684 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4322']' 16.254558324813843 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4322' Length=550 Min=32.0 Max=82910.0 Mean=226.75454545454545 StdDev=3528.9784975422704 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4322' Min=32.0 Max=82910.0 Mean=226.75454545454545 StdDev=3528.9784975422704 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_4322_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_4322_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [Lag1Trend + Seasonal_DayOfWeek + NoAR] INFO:pyaf.std:TREND_DETAIL '_4322_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_4322_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_4322_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.7127 MAPE_Forecast=0.2934 MAPE_Test=0.2483 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2214 SMAPE_Forecast=0.2631 SMAPE_Test=0.2324 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=1.0006 MASE_Test=0.9904 -INFO:pyaf.std:MODEL_L1 L1_Fit=438.0204081632653 L1_Forecast=22.693877551020407 L1_Test=21.166666666666668 -INFO:pyaf.std:MODEL_L2 L2_Fit=5912.862397315858 L2_Forecast=43.322473783174914 L2_Test=38.19031290785662 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:CYCLE_DETAIL '_4322_Lag1Trend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4322_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.6828 MAPE_Forecast=0.2617 MAPE_Test=0.2291 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1952 SMAPE_Forecast=0.2402 SMAPE_Test=0.2161 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9929 MASE_Forecast=0.9219 MASE_Test=0.9194 +INFO:pyaf.std:MODEL_L1 L1_Fit=436.0255102040816 L1_Forecast=20.908163265306122 L1_Test=19.65 +INFO:pyaf.std:MODEL_L2 L2_Fit=5912.852984458207 L2_Forecast=41.471160375098535 L2_Test=37.172906800518035 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 56.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4322_Lag1Trend_residue_Seasonal_DayOfWeek 1.0 {2: 2.0, 3: 2.0, 4: -11.5, 5: -14.5, 6: 7.0, 0: 18.0, 1: 2.0} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.0767717361450195 +INFO:pyaf.std:START_FORECASTING '['4322']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4322']' 2.587041139602661 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4322 ... 0.2856 0.2323 -1 None _4322 ... 0.2934 0.2483 -2 None Anscombe_4322 ... 0.2934 0.2483 -3 None Diff_4322 ... 0.2934 0.2483 -4 None _4322 ... 0.3023 0.2421 +0 None Anscombe_4322 ... 0.2579 0.1938 +1 None _4322 ... 0.2617 0.2291 +2 None Anscombe_4322 ... 0.2617 0.2290 +3 None _4322 ... 0.2765 0.1986 +4 None Anscombe_4322 ... 0.2843 0.2294 [5 rows x 8 columns] Forecast Columns Index(['Date', '4322', 'row_number', 'Date_Normalized', '_4322', '_4322_Lag1Trend', '_4322_Lag1Trend_residue', - '_4322_Lag1Trend_residue_zeroCycle', - '_4322_Lag1Trend_residue_zeroCycle_residue', - '_4322_Lag1Trend_residue_zeroCycle_residue_NoAR', - '_4322_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', '_4322_Trend', - '_4322_Trend_residue', '_4322_Cycle', '_4322_Cycle_residue', '_4322_AR', - '_4322_AR_residue', '_4322_TransformedForecast', '4322_Forecast', + '_4322_Lag1Trend_residue_Seasonal_DayOfWeek', + '_4322_Lag1Trend_residue_Seasonal_DayOfWeek_residue', + '_4322_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4322_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4322_Trend', '_4322_Trend_residue', '_4322_Cycle', + '_4322_Cycle_residue', '_4322_AR', '_4322_AR_residue', + '_4322_TransformedForecast', '4322_Forecast', '_4322_TransformedResidue', '4322_Residue'], dtype='object') @@ -1936,95 +2078,97 @@ memory usage: 14.4 KB None Forecasts Date 4322 4322_Forecast -550 2017-01-01 NaN 46.0 -551 2017-01-02 NaN 46.0 -552 2017-01-03 NaN 46.0 -553 2017-01-04 NaN 46.0 -554 2017-01-05 NaN 46.0 -555 2017-01-06 NaN 46.0 -556 2017-01-07 NaN 46.0 -557 2017-01-08 NaN 46.0 -558 2017-01-09 NaN 46.0 -559 2017-01-10 NaN 46.0 -560 2017-01-11 NaN 46.0 -561 2017-01-12 NaN 46.0 -562 2017-01-13 NaN 46.0 -563 2017-01-14 NaN 46.0 -564 2017-01-15 NaN 46.0 -565 2017-01-16 NaN 46.0 -566 2017-01-17 NaN 46.0 -567 2017-01-18 NaN 46.0 -568 2017-01-19 NaN 46.0 -569 2017-01-20 NaN 46.0 -570 2017-01-21 NaN 46.0 -571 2017-01-22 NaN 46.0 -572 2017-01-23 NaN 46.0 -573 2017-01-24 NaN 46.0 -574 2017-01-25 NaN 46.0 -575 2017-01-26 NaN 46.0 -576 2017-01-27 NaN 46.0 -577 2017-01-28 NaN 46.0 -578 2017-01-29 NaN 46.0 -579 2017-01-30 NaN 46.0 -580 2017-01-31 NaN 46.0 -581 2017-02-01 NaN 46.0 -582 2017-02-02 NaN 46.0 -583 2017-02-03 NaN 46.0 -584 2017-02-04 NaN 46.0 -585 2017-02-05 NaN 46.0 -586 2017-02-06 NaN 46.0 -587 2017-02-07 NaN 46.0 -588 2017-02-08 NaN 46.0 -589 2017-02-09 NaN 46.0 -590 2017-02-10 NaN 46.0 -591 2017-02-11 NaN 46.0 -592 2017-02-12 NaN 46.0 -593 2017-02-13 NaN 46.0 -594 2017-02-14 NaN 46.0 -595 2017-02-15 NaN 46.0 -596 2017-02-16 NaN 46.0 -597 2017-02-17 NaN 46.0 -598 2017-02-18 NaN 46.0 -599 2017-02-19 NaN 46.0 -600 2017-02-20 NaN 46.0 -601 2017-02-21 NaN 46.0 -602 2017-02-22 NaN 46.0 -603 2017-02-23 NaN 46.0 -604 2017-02-24 NaN 46.0 -605 2017-02-25 NaN 46.0 -606 2017-02-26 NaN 46.0 -607 2017-02-27 NaN 46.0 -608 2017-02-28 NaN 46.0 -609 2017-03-01 NaN 46.0 +550 2017-01-01 NaN 53.0 +551 2017-01-02 NaN 71.0 +552 2017-01-03 NaN 73.0 +553 2017-01-04 NaN 75.0 +554 2017-01-05 NaN 77.0 +555 2017-01-06 NaN 65.5 +556 2017-01-07 NaN 51.0 +557 2017-01-08 NaN 58.0 +558 2017-01-09 NaN 76.0 +559 2017-01-10 NaN 78.0 +560 2017-01-11 NaN 80.0 +561 2017-01-12 NaN 82.0 +562 2017-01-13 NaN 70.5 +563 2017-01-14 NaN 56.0 +564 2017-01-15 NaN 63.0 +565 2017-01-16 NaN 81.0 +566 2017-01-17 NaN 83.0 +567 2017-01-18 NaN 85.0 +568 2017-01-19 NaN 87.0 +569 2017-01-20 NaN 75.5 +570 2017-01-21 NaN 61.0 +571 2017-01-22 NaN 68.0 +572 2017-01-23 NaN 86.0 +573 2017-01-24 NaN 88.0 +574 2017-01-25 NaN 90.0 +575 2017-01-26 NaN 92.0 +576 2017-01-27 NaN 80.5 +577 2017-01-28 NaN 66.0 +578 2017-01-29 NaN 73.0 +579 2017-01-30 NaN 91.0 +580 2017-01-31 NaN 93.0 +581 2017-02-01 NaN 95.0 +582 2017-02-02 NaN 97.0 +583 2017-02-03 NaN 85.5 +584 2017-02-04 NaN 71.0 +585 2017-02-05 NaN 78.0 +586 2017-02-06 NaN 96.0 +587 2017-02-07 NaN 98.0 +588 2017-02-08 NaN 100.0 +589 2017-02-09 NaN 102.0 +590 2017-02-10 NaN 90.5 +591 2017-02-11 NaN 76.0 +592 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'4323' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4323']' 21.94598150253296 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4323' Length=550 Min=0.0 Max=36269.0 Mean=72.67818181818181 StdDev=1544.8341564642178 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4323' Min=-36262.0 Max=36265.0 Mean=0.0036363636363636364 StdDev=2186.7862829411347 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4323_Lag1Trend_residue_zeroCycle_residue_AR(16)' [Lag1Trend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL 'Diff_4323_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_4323_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_4323_Lag1Trend_residue_zeroCycle_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=342545991.5602 MAPE_Forecast=192704422.7035 MAPE_Test=915050351.0582 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.181 SMAPE_Forecast=1.3372 SMAPE_Test=1.3985 -INFO:pyaf.std:MODEL_MASE MASE_Fit=3.0188 MASE_Forecast=2.403 MASE_Test=2.3441 -INFO:pyaf.std:MODEL_L1 L1_Fit=571.6553276390644 L1_Forecast=16.053045037336343 L1_Test=10.607878902974072 -INFO:pyaf.std:MODEL_L2 L2_Fit=3205.557672676037 L2_Forecast=25.10042681296373 L2_Test=15.199163025210659 -INFO:pyaf.std:MODEL_COMPLEXITY 80 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4323' Min=0.0 Max=36269.0 Mean=72.67818181818181 StdDev=1544.8341564642178 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4323_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [PolyTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4323_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4323_PolyTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4323_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=4824603683.8688 MAPE_Forecast=70029748.8173 MAPE_Test=53973747283.0138 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.6797 SMAPE_Forecast=1.8196 SMAPE_Test=2.0 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8447 MASE_Forecast=10.1787 MASE_Test=56.917 +INFO:pyaf.std:MODEL_L1 L1_Fit=159.95477539912807 L1_Forecast=67.99790970810568 L1_Test=257.57373643861445 +INFO:pyaf.std:MODEL_L2 L2_Fit=1830.773066727284 L2_Forecast=85.07730667450954 L2_Test=263.7700539484819 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (-70.75925456658227, array([ 357.87867974, 324.33452126, -469.23356946])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4323_PolyTrend_residue_Seasonal_DayOfWeek -122.17647095652956 {2: -124.52366604964635, 3: -125.38036695902238, 4: -126.23219721321169, 5: -125.07910971339149, 6: -119.42105736073904, 0: -126.05112978723994, 1: -125.58986970164577} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag5 -3.059187144107126 -INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag6 -3.0198621575449907 -INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag4 -2.974103876894342 -INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag7 -2.8761584310557504 -INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag3 -2.7453400129832124 -INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag8 -2.647149678575592 -INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag2 -2.3530746052131972 -INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag9 -2.3527115630256663 -INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag10 -2.0124497963612127 -INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_4323_Lag1Trend_residue_zeroCycle_residue_Lag1 -1.7778572755825817 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 5.564227104187012 +INFO:pyaf.std:START_FORECASTING '['4323']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4323']' 4.037328243255615 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4323 ... 1.927044e+08 9.150504e+08 -1 None _4323 ... 2.879009e+08 1.065476e+09 -2 None Anscombe_4323 ... 2.879177e+08 1.065470e+09 -3 None _4323 ... 3.061225e+08 1.166667e+09 -4 None Diff_4323 ... 3.061225e+08 1.166667e+09 +0 None _4323 ... 7.002975e+07 5.397375e+10 +1 None Diff_4323 ... 1.927044e+08 9.150504e+08 +2 None Diff_4323 ... 1.927044e+08 9.150504e+08 +3 None Anscombe_4323 ... 3.055651e+08 3.657405e+10 +4 None _4323 ... 3.061225e+08 1.166667e+09 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4323', 'row_number', 'Date_Normalized', 'Diff_4323', - 'Diff_4323_Lag1Trend', 'Diff_4323_Lag1Trend_residue', - 'Diff_4323_Lag1Trend_residue_zeroCycle', - 'Diff_4323_Lag1Trend_residue_zeroCycle_residue', - 'Diff_4323_Lag1Trend_residue_zeroCycle_residue_AR(16)', - 'Diff_4323_Lag1Trend_residue_zeroCycle_residue_AR(16)_residue', - 'Diff_4323_Trend', 'Diff_4323_Trend_residue', 'Diff_4323_Cycle', - 'Diff_4323_Cycle_residue', 'Diff_4323_AR', 'Diff_4323_AR_residue', - 'Diff_4323_TransformedForecast', '4323_Forecast', - 'Diff_4323_TransformedResidue', '4323_Residue'], +Forecast Columns Index(['Date', '4323', 'row_number', 'Date_Normalized', '_4323', + 'Date_Normalized_^2', 'Date_Normalized_^3', '_4323_PolyTrend', + '_4323_PolyTrend_residue', '_4323_PolyTrend_residue_Seasonal_DayOfWeek', + '_4323_PolyTrend_residue_Seasonal_DayOfWeek_residue', + '_4323_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4323_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4323_Trend', '_4323_Trend_residue', '_4323_Cycle', + '_4323_Cycle_residue', '_4323_AR', '_4323_AR_residue', + '_4323_TransformedForecast', '4323_Forecast', + '_4323_TransformedResidue', '4323_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -2099,95 +2242,97 @@ memory usage: 14.4 KB None Forecasts Date 4323 4323_Forecast -550 2017-01-01 NaN -5.214678 -551 2017-01-02 NaN 4.351484 -552 2017-01-03 NaN 3.932052 -553 2017-01-04 NaN 3.524570 -554 2017-01-05 NaN 2.891700 -555 2017-01-06 NaN 2.373156 -556 2017-01-07 NaN 2.038709 -557 2017-01-08 NaN 1.986700 -558 2017-01-09 NaN 1.661281 -559 2017-01-10 NaN 1.478122 -560 2017-01-11 NaN 0.445052 -561 2017-01-12 NaN -0.191371 -562 2017-01-13 NaN -0.885617 -563 2017-01-14 NaN -0.975148 -564 2017-01-15 NaN -0.875224 -565 2017-01-16 NaN -1.318056 -566 2017-01-17 NaN -1.245499 -567 2017-01-18 NaN -1.230087 -568 2017-01-19 NaN -1.335813 -569 2017-01-20 NaN -2.562592 -570 2017-01-21 NaN -2.889379 -571 2017-01-22 NaN -3.207884 -572 2017-01-23 NaN -3.516345 -573 2017-01-24 NaN -3.844310 -574 2017-01-25 NaN -4.187165 -575 2017-01-26 NaN -4.528286 -576 2017-01-27 NaN -4.829730 -577 2017-01-28 NaN -5.113466 -578 2017-01-29 NaN -5.356963 -579 2017-01-30 NaN -5.660237 -580 2017-01-31 NaN -5.999923 -581 2017-02-01 NaN -6.398777 -582 2017-02-02 NaN -6.797463 -583 2017-02-03 NaN -7.165562 -584 2017-02-04 NaN -7.557015 -585 2017-02-05 NaN -7.911633 -586 2017-02-06 NaN -8.219025 -587 2017-02-07 NaN -8.477845 -588 2017-02-08 NaN -8.819435 -589 2017-02-09 NaN -9.163693 -590 2017-02-10 NaN -9.509826 -591 2017-02-11 NaN -9.856390 -592 2017-02-12 NaN -10.205310 -593 2017-02-13 NaN -10.558670 -594 2017-02-14 NaN -10.917132 -595 2017-02-15 NaN -11.276378 -596 2017-02-16 NaN -11.633202 -597 2017-02-17 NaN -11.980345 -598 2017-02-18 NaN -12.321245 -599 2017-02-19 NaN -12.658375 -600 2017-02-20 NaN -12.998624 -601 2017-02-21 NaN -13.343845 -602 2017-02-22 NaN -13.692311 -603 2017-02-23 NaN -14.048219 -604 2017-02-24 NaN -14.409281 -605 2017-02-25 NaN -14.770415 -606 2017-02-26 NaN -15.124267 -607 2017-02-27 NaN -15.478020 -608 2017-02-28 NaN -15.831765 -609 2017-03-01 NaN -16.185560 +550 2017-01-01 NaN -351.032749 +551 2017-01-02 NaN -361.548434 +552 2017-01-03 NaN -364.994496 +553 2017-01-04 NaN -367.857369 +554 2017-01-05 NaN -372.664949 +555 2017-01-06 NaN -377.489509 +556 2017-01-07 NaN -380.331048 +557 2017-01-08 NaN -378.689566 +558 2017-01-09 NaN -389.358200 +559 2017-01-10 NaN -392.957539 +560 2017-01-11 NaN -395.974020 +561 2017-01-12 NaN -400.935538 +562 2017-01-13 NaN -405.914365 +563 2017-01-14 NaN -408.910501 +564 2017-01-15 NaN -407.423945 +565 2017-01-16 NaN -418.247836 +566 2017-01-17 NaN -422.002761 +567 2017-01-18 NaN -425.175158 +568 2017-01-19 NaN -430.292921 +569 2017-01-20 NaN -435.428323 +570 2017-01-21 NaN -438.581364 +571 2017-01-22 NaN -437.252043 +572 2017-01-23 NaN -448.233497 +573 2017-01-24 NaN -452.146316 +574 2017-01-25 NaN -455.476937 +575 2017-01-26 NaN -460.753253 +576 2017-01-27 NaN -466.047538 +577 2017-01-28 NaN -469.359791 +578 2017-01-29 NaN -468.190013 +579 2017-01-30 NaN -479.331339 +580 2017-01-31 NaN -483.404360 +581 2017-02-01 NaN -486.895512 +582 2017-02-02 NaN -492.332689 +583 2017-02-03 NaN -497.788165 +584 2017-02-04 NaN -501.261939 +585 2017-02-05 NaN -500.254010 +586 2017-02-06 NaN -511.557516 +587 2017-02-07 NaN -515.793047 +588 2017-02-08 NaN -519.447038 +589 2017-02-09 NaN -525.047384 +590 2017-02-10 NaN 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"Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Diff_4323_Lag1Trend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "Difference", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "80", - "MAE": "16.053045037336343", - "MAPE": "192704422.7035", - "MASE": "2.403", - "RMSE": "25.10042681296373" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4323_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "67.99790970810568", + "MAPE": "70029748.8173", + "MASE": "10.1787", + "RMSE": "85.07730667450954" + } } } @@ -2196,8 +2341,8 @@ Forecasts 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'4324' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4324']' 20.663556814193726 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4324' Length=550 Min=0.0 Max=41015.0 Mean=86.44909090909091 StdDev=1747.3432405041483 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4324' Min=0.0 Max=41015.0 Mean=86.44909090909091 StdDev=1747.3432405041483 @@ -2212,20 +2357,29 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7925 MASE_Forecast=0.837 MASE_Test=0.5168 INFO:pyaf.std:MODEL_L1 L1_Fit=3.082830070803832 L1_Forecast=6.246980424822991 L1_Test=723.0534013605441 INFO:pyaf.std:MODEL_L2 L2_Fit=4.038612571606763 L2_Forecast=16.33326710512516 L2_Test=5295.7997176510025 INFO:pyaf.std:MODEL_COMPLEXITY 0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.704081632653061 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4324_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 4.471227407455444 +INFO:pyaf.std:START_FORECASTING '['4324']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4324']' 3.230164051055908 Split Transformation ... ForecastMAPE TestMAPE -0 None _4324 ... 0.5032 0.5587 -1 None Anscombe_4324 ... 0.5032 0.5587 -2 None Anscombe_4324 ... 0.5032 0.5586 -3 None _4324 ... 0.5089 0.5488 -4 None Anscombe_4324 ... 0.5089 0.5488 +0 None _4324 ... 0.5089 0.5488 +1 None _4324 ... 0.5089 0.5488 +2 None Anscombe_4324 ... 0.5089 0.5488 +3 None Anscombe_4324 ... 0.5089 0.5488 +4 None Anscombe_4324 ... 0.5089 0.5487 [5 rows x 8 columns] Forecast Columns Index(['Date', '4324', 'row_number', 'Date_Normalized', '_4324', @@ -2316,31 +2470,33 @@ Forecasts { - "Dataset": { - "Signal": "4324", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4324": { + "Dataset": { + "Signal": "4324", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4324_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_4324_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "6.246980424822991", - "MAPE": "0.5089", - "MASE": "0.837", - "RMSE": "16.33326710512516" + "Model_Performance": { + "COMPLEXITY": "0", + "MAE": "6.246980424822991", + "MAPE": "0.5089", + "MASE": "0.837", + "RMSE": "16.33326710512516" + } } } @@ -2350,58 +2506,56 @@ Forecasts 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'4325' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4325' 9.59380054473877 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4325']' 22.452453136444092 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4325' Length=550 Min=13.0 Max=1866.0 Mean=128.07818181818183 StdDev=192.53724852459456 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4325' Min=1.224744871391589 Max=2.345207879911715 Mean=1.3157538234432982 StdDev=0.13117443200256315 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_AR(16)' [Lag1Trend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL 'Anscombe_4325_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4325_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2878 MAPE_Forecast=0.3667 MAPE_Test=0.2815 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2653 SMAPE_Forecast=0.338 SMAPE_Test=0.2542 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8673 MASE_Forecast=0.9722 MASE_Test=0.8081 -INFO:pyaf.std:MODEL_L1 L1_Fit=43.74003147639059 L1_Forecast=21.07850730021617 L1_Test=23.544788790636062 -INFO:pyaf.std:MODEL_L2 L2_Fit=104.98099822279244 L2_Forecast=42.58592057367912 L2_Test=30.170460815707223 -INFO:pyaf.std:MODEL_COMPLEXITY 80 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4325' Min=13.0 Max=1866.0 Mean=128.07818181818183 StdDev=192.53724852459456 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4325_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [ConstantTrend + Seasonal_WeekOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4325_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4325_ConstantTrend_residue_Seasonal_WeekOfYear' [Seasonal_WeekOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_4325_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2527 MAPE_Forecast=0.3517 MAPE_Test=0.4939 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2427 SMAPE_Forecast=0.3111 SMAPE_Test=0.3679 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8532 MASE_Forecast=0.858 MASE_Test=1.5857 +INFO:pyaf.std:MODEL_L1 L1_Fit=43.025510204081634 L1_Forecast=18.602040816326532 L1_Test=46.2 +INFO:pyaf.std:MODEL_L2 L2_Fit=98.78972334598124 L2_Forecast=37.512310904391846 L2_Test=60.31307210436778 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 150.99489795918367 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4325_ConstantTrend_residue_Seasonal_WeekOfYear -76.49489795918367 {27: -118.99489795918367, 28: -122.49489795918367, 29: -118.99489795918367, 30: -116.99489795918367, 31: -116.99489795918367, 32: -117.99489795918367, 33: -115.99489795918367, 34: -123.99489795918367, 35: -115.99489795918367, 36: -118.99489795918367, 37: -109.99489795918367, 38: -101.99489795918367, 39: -111.99489795918367, 40: -89.99489795918367, 41: -55.994897959183675, 42: -55.994897959183675, 43: -63.994897959183675, 44: -59.994897959183675, 45: 39.005102040816325, 46: 79.00510204081633, 47: 51.005102040816325, 48: -61.994897959183675, 49: -62.994897959183675, 50: 2.0051020408163254, 51: -84.99489795918367, 52: -89.99489795918367, 53: -31.994897959183675, 1: 56.005102040816325, 2: 146.00510204081633, 3: 175.00510204081633, 4: 187.00510204081633, 5: 139.00510204081633, 6: 136.00510204081633, 7: 193.00510204081633, 8: 22.005102040816325, 9: -0.9948979591836746, 10: 165.00510204081633, 11: -56.994897959183675, 12: -43.994897959183675, 13: 56.005102040816325, 14: 586.0051020408164, 15: 1172.0051020408164, 16: 76.00510204081633, 17: -30.994897959183675, 18: -64.99489795918367, 19: -92.99489795918367, 20: -99.99489795918367, 21: -110.99489795918367, 22: -113.99489795918367, 23: -117.99489795918367, 24: -124.99489795918367, 25: -119.99489795918367, 26: -119.99489795918367} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.2142326272052857 -INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.14613783369568648 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag7 0.1379891356724363 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.09660985760102406 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag6 0.08322936514319582 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag12 -0.07750196761592694 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag10 -0.06236681663123521 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.051281265191979925 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag3 0.04829281108977931 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_Lag14 0.04394650984878658 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 5.626774311065674 +INFO:pyaf.std:START_FORECASTING '['4325']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4325']' 3.6102890968322754 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4325 ... 0.3667 0.2815 -1 None Anscombe_4325 ... 0.3730 0.2968 -2 None _4325 ... 0.3856 0.2703 -3 None _4325 ... 0.3873 0.3301 -4 None Anscombe_4325 ... 0.3873 0.3301 +0 None _4325 ... 0.3517 0.4939 +1 None Anscombe_4325 ... 0.3517 0.4939 +2 None Anscombe_4325 ... 0.3624 0.2766 +3 None Anscombe_4325 ... 0.3667 0.2815 +4 None Anscombe_4325 ... 0.3667 0.2815 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4325', 'row_number', 'Date_Normalized', 'Anscombe_4325', - 'Anscombe_4325_Lag1Trend', 'Anscombe_4325_Lag1Trend_residue', - 'Anscombe_4325_Lag1Trend_residue_zeroCycle', - 'Anscombe_4325_Lag1Trend_residue_zeroCycle_residue', - 'Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_AR(16)', - 'Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_AR(16)_residue', - 'Anscombe_4325_Trend', 'Anscombe_4325_Trend_residue', - 'Anscombe_4325_Cycle', 'Anscombe_4325_Cycle_residue', - 'Anscombe_4325_AR', 'Anscombe_4325_AR_residue', - 'Anscombe_4325_TransformedForecast', '4325_Forecast', - 'Anscombe_4325_TransformedResidue', '4325_Residue'], +Forecast Columns Index(['Date', '4325', 'row_number', 'Date_Normalized', '_4325', + '_4325_ConstantTrend', '_4325_ConstantTrend_residue', + '_4325_ConstantTrend_residue_Seasonal_WeekOfYear', + '_4325_ConstantTrend_residue_Seasonal_WeekOfYear_residue', + '_4325_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR', + '_4325_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR_residue', + '_4325_Trend', '_4325_Trend_residue', '_4325_Cycle', + '_4325_Cycle_residue', '_4325_AR', '_4325_AR_residue', + '_4325_TransformedForecast', '4325_Forecast', + '_4325_TransformedResidue', '4325_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -2416,95 +2570,97 @@ memory usage: 14.4 KB None Forecasts Date 4325 4325_Forecast -550 2017-01-01 NaN 56.183089 -551 2017-01-02 NaN 58.888168 -552 2017-01-03 NaN 57.520689 -553 2017-01-04 NaN 58.030406 -554 2017-01-05 NaN 61.470145 -555 2017-01-06 NaN 59.423784 -556 2017-01-07 NaN 57.404692 -557 2017-01-08 NaN 57.578625 -558 2017-01-09 NaN 57.286560 -559 2017-01-10 NaN 56.940934 -560 2017-01-11 NaN 56.381203 -561 2017-01-12 NaN 58.513903 -562 2017-01-13 NaN 58.354384 -563 2017-01-14 NaN 56.131903 -564 2017-01-15 NaN 56.684318 -565 2017-01-16 NaN 57.398011 -566 2017-01-17 NaN 56.658087 -567 2017-01-18 NaN 56.793210 -568 2017-01-19 NaN 57.756729 -569 2017-01-20 NaN 57.418970 -570 2017-01-21 NaN 56.686309 -571 2017-01-22 NaN 56.946697 -572 2017-01-23 NaN 57.288920 -573 2017-01-24 NaN 56.918404 -574 2017-01-25 NaN 56.844977 -575 2017-01-26 NaN 57.357108 -576 2017-01-27 NaN 57.201443 -577 2017-01-28 NaN 56.717449 -578 2017-01-29 NaN 56.905066 -579 2017-01-30 NaN 57.092743 -580 2017-01-31 NaN 56.814786 -581 2017-02-01 NaN 56.786919 -582 2017-02-02 NaN 57.033455 -583 2017-02-03 NaN 56.918205 -584 2017-02-04 NaN 56.679538 -585 2017-02-05 NaN 56.778572 -586 2017-02-06 NaN 56.870264 -587 2017-02-07 NaN 56.708017 -588 2017-02-08 NaN 56.674551 -589 2017-02-09 NaN 56.803244 -590 2017-02-10 NaN 56.732717 -591 2017-02-11 NaN 56.593417 -592 2017-02-12 NaN 56.649080 -593 2017-02-13 NaN 56.688120 -594 2017-02-14 NaN 56.582939 -595 2017-02-15 NaN 56.557923 -596 2017-02-16 NaN 56.617286 -597 2017-02-17 NaN 56.566661 -598 2017-02-18 NaN 56.484436 -599 2017-02-19 NaN 56.507394 -600 2017-02-20 NaN 56.517693 -601 2017-02-21 NaN 56.448649 -602 2017-02-22 NaN 56.425246 -603 2017-02-23 NaN 56.448523 -604 2017-02-24 NaN 56.410370 -605 2017-02-25 NaN 56.356511 -606 2017-02-26 NaN 56.361326 -607 2017-02-27 NaN 56.356503 -608 2017-02-28 NaN 56.308009 -609 2017-03-01 NaN 56.286430 +550 2017-01-01 NaN 61.0 +551 2017-01-02 NaN 207.0 +552 2017-01-03 NaN 207.0 +553 2017-01-04 NaN 207.0 +554 2017-01-05 NaN 207.0 +555 2017-01-06 NaN 207.0 +556 2017-01-07 NaN 207.0 +557 2017-01-08 NaN 207.0 +558 2017-01-09 NaN 297.0 +559 2017-01-10 NaN 297.0 +560 2017-01-11 NaN 297.0 +561 2017-01-12 NaN 297.0 +562 2017-01-13 NaN 297.0 +563 2017-01-14 NaN 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2017-02-22 NaN 173.0 +603 2017-02-23 NaN 173.0 +604 2017-02-24 NaN 173.0 +605 2017-02-25 NaN 173.0 +606 2017-02-26 NaN 173.0 +607 2017-02-27 NaN 150.0 +608 2017-02-28 NaN 150.0 +609 2017-03-01 NaN 150.0 { - "Dataset": { - "Signal": "4325", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4325": { + "Dataset": { + "Signal": "4325", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Anscombe_4325_Lag1Trend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "Anscombe", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "80", - "MAE": "21.07850730021617", - "MAPE": "0.3667", - "MASE": "0.9722", - "RMSE": "42.58592057367912" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4325_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR", + "Cycle": "Seasonal_WeekOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "18.602040816326532", + "MAPE": "0.3517", + "MASE": "0.858", + "RMSE": "37.512310904391846" + } } } @@ -2513,46 +2669,56 @@ Forecasts 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'4326' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4326']' 27.308464288711548 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4326' Length=550 Min=132.0 Max=12924.0 Mean=544.3909090909091 StdDev=1117.4970872075485 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4326' Min=132.0 Max=12924.0 Mean=544.3909090909091 StdDev=1117.4970872075485 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_4326_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_4326_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [Lag1Trend + Seasonal_DayOfWeek + NoAR] INFO:pyaf.std:TREND_DETAIL '_4326_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_4326_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_4326_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2434 MAPE_Forecast=0.2646 MAPE_Test=0.2182 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.239 SMAPE_Forecast=0.2451 SMAPE_Test=0.2103 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.999 MASE_Test=1.0431 -INFO:pyaf.std:MODEL_L1 L1_Fit=192.1887755102041 L1_Forecast=98.60204081632654 L1_Test=115.4 -INFO:pyaf.std:MODEL_L2 L2_Fit=655.4222077471615 L2_Forecast=183.72064530205657 L2_Test=357.52477769612926 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:CYCLE_DETAIL '_4326_Lag1Trend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4326_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2252 MAPE_Forecast=0.2407 MAPE_Test=0.2218 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.23 SMAPE_Forecast=0.236 SMAPE_Test=0.222 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.973 MASE_Forecast=0.9448 MASE_Test=1.0511 +INFO:pyaf.std:MODEL_L1 L1_Fit=187.4795918367347 L1_Forecast=93.25510204081633 L1_Test=116.275 +INFO:pyaf.std:MODEL_L2 L2_Fit=656.1501410904834 L2_Forecast=178.35093530017997 L2_Test=355.9488902918508 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 837.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4326_Lag1Trend_residue_Seasonal_DayOfWeek -10.5 {2: -17.0, 3: -18.5, 4: -35.5, 5: -48.5, 6: 21.0, 0: 24.5, 1: -13.5} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 3.4813833236694336 +INFO:pyaf.std:START_FORECASTING '['4326']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4326']' 3.355929136276245 Split Transformation ... ForecastMAPE TestMAPE -0 None _4326 ... 0.2646 0.2182 -1 None Anscombe_4326 ... 0.2646 0.2182 -2 None Diff_4326 ... 0.2646 0.2182 -3 None Anscombe_4326 ... 0.2678 0.2316 -4 None Anscombe_4326 ... 0.2708 0.2213 +0 None Anscombe_4326 ... 0.2401 0.2212 +1 None _4326 ... 0.2407 0.2218 +2 None Anscombe_4326 ... 0.2462 0.2360 +3 None Anscombe_4326 ... 0.2553 0.2183 +4 None Anscombe_4326 ... 0.2601 0.2372 [5 rows x 8 columns] Forecast Columns Index(['Date', '4326', 'row_number', 'Date_Normalized', '_4326', '_4326_Lag1Trend', '_4326_Lag1Trend_residue', - '_4326_Lag1Trend_residue_zeroCycle', - '_4326_Lag1Trend_residue_zeroCycle_residue', - '_4326_Lag1Trend_residue_zeroCycle_residue_NoAR', - '_4326_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', '_4326_Trend', - '_4326_Trend_residue', '_4326_Cycle', '_4326_Cycle_residue', '_4326_AR', - '_4326_AR_residue', '_4326_TransformedForecast', '4326_Forecast', + '_4326_Lag1Trend_residue_Seasonal_DayOfWeek', + 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'4327' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4327']' 19.766943216323853 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4327' Length=550 Min=136.0 Max=5389.0 Mean=530.2345454545455 StdDev=382.3364837503891 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4327' Min=1.224744871391589 Max=2.345207879911715 Mean=1.3384996145720027 StdDev=0.09282466145104593 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_AR(16)' [Lag1Trend + NoCycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(16)' [Lag1Trend + Seasonal_DayOfWeek + AR] INFO:pyaf.std:TREND_DETAIL 'Anscombe_4327_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4327_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1921 MAPE_Forecast=0.1638 MAPE_Test=0.2379 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.176 SMAPE_Forecast=0.16 SMAPE_Test=0.2098 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8374 MASE_Forecast=0.8514 MASE_Test=0.8945 -INFO:pyaf.std:MODEL_L1 L1_Fit=123.38946957700662 L1_Forecast=72.84534080422553 L1_Test=172.0559751200885 -INFO:pyaf.std:MODEL_L2 L2_Fit=392.83489599639967 L2_Forecast=114.27591149874597 L2_Test=390.38782719652613 -INFO:pyaf.std:MODEL_COMPLEXITY 80 +INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(16)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1824 MAPE_Forecast=0.1468 MAPE_Test=0.2119 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1697 SMAPE_Forecast=0.1458 SMAPE_Test=0.1871 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.78 MASE_Forecast=0.7427 MASE_Test=0.813 +INFO:pyaf.std:MODEL_L1 L1_Fit=114.92649032373882 L1_Forecast=63.54533477414762 L1_Test=156.36931890000963 +INFO:pyaf.std:MODEL_L2 L2_Fit=388.1423653484417 L2_Forecast=100.50680422817258 L2_Test=382.39374714188534 +INFO:pyaf.std:MODEL_COMPLEXITY 84 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1.2596854919182805 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek -0.0006120302696945856 {2: 0.004922305947575367, 3: -0.007464118707572354, 4: -0.024916158263206145, 5: -0.015299844929991924, 6: 0.029457722697907918, 0: 0.014609812131028632, 1: -0.002136570462283327} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.308821628213499 -INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.28201902273914925 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.15609631668680252 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.12893529034028525 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag7 0.12505409621005814 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.10386589619121268 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.07421156475549148 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag11 -0.023457172091264765 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.021008693430229194 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_Lag10 -0.017484225387941848 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag1 -0.3189818515625725 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag2 -0.2735244031546866 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag3 -0.14588646542718542 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag4 -0.11435701969845227 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag7 0.10164517399518941 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag5 -0.08496757969816028 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag6 -0.07634126311486128 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag14 -0.035358850784395685 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag15 -0.022450781100021466 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag8 -0.018250310337601657 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 3.757120370864868 +INFO:pyaf.std:START_FORECASTING '['4327']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4327']' 5.381695032119751 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4327 ... 0.1638 0.2379 -1 None _4327 ... 0.1858 0.2474 -2 None Anscombe_4327 ... 0.1858 0.2474 -3 None Diff_4327 ... 0.1858 0.2474 -4 None _4327 ... 0.1867 0.2541 +0 None Anscombe_4327 ... 0.1468 0.2119 +1 None Anscombe_4327 ... 0.1618 0.2114 +2 None Anscombe_4327 ... 0.1635 0.2081 +3 None Anscombe_4327 ... 0.1638 0.2379 +4 None Anscombe_4327 ... 0.1638 0.2379 [5 rows x 8 columns] Forecast Columns Index(['Date', '4327', 'row_number', 'Date_Normalized', 'Anscombe_4327', 'Anscombe_4327_Lag1Trend', 'Anscombe_4327_Lag1Trend_residue', - 'Anscombe_4327_Lag1Trend_residue_zeroCycle', - 'Anscombe_4327_Lag1Trend_residue_zeroCycle_residue', - 'Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_AR(16)', - 'Anscombe_4327_Lag1Trend_residue_zeroCycle_residue_AR(16)_residue', + 'Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek', + 'Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue', + 'Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(16)', + 'Anscombe_4327_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(16)_residue', 'Anscombe_4327_Trend', 'Anscombe_4327_Trend_residue', 'Anscombe_4327_Cycle', 'Anscombe_4327_Cycle_residue', 'Anscombe_4327_AR', 'Anscombe_4327_AR_residue', @@ -2732,95 +2909,97 @@ memory usage: 14.4 KB None Forecasts Date 4327 4327_Forecast -550 2017-01-01 NaN 386.398964 -551 2017-01-02 NaN 457.082111 -552 2017-01-03 NaN 486.684588 -553 2017-01-04 NaN 500.247712 -554 2017-01-05 NaN 479.138299 -555 2017-01-06 NaN 488.067174 -556 2017-01-07 NaN 456.738198 -557 2017-01-08 NaN 458.841225 -558 2017-01-09 NaN 480.427024 -559 2017-01-10 NaN 484.123378 -560 2017-01-11 NaN 483.610232 -561 2017-01-12 NaN 478.351354 -562 2017-01-13 NaN 478.809837 -563 2017-01-14 NaN 472.315639 -564 2017-01-15 NaN 471.721573 -565 2017-01-16 NaN 477.933542 -566 2017-01-17 NaN 478.260893 -567 2017-01-18 NaN 477.308301 -568 2017-01-19 NaN 476.368669 -569 2017-01-20 NaN 476.218734 -570 2017-01-21 NaN 475.156962 -571 2017-01-22 NaN 474.864107 -572 2017-01-23 NaN 476.525994 -573 2017-01-24 NaN 476.613351 -574 2017-01-25 NaN 476.139401 -575 2017-01-26 NaN 475.990986 -576 2017-01-27 NaN 475.926499 -577 2017-01-28 NaN 475.719470 -578 2017-01-29 NaN 475.622353 -579 2017-01-30 NaN 476.006234 -580 2017-01-31 NaN 476.020509 -581 2017-02-01 NaN 475.819249 -582 2017-02-02 NaN 475.779251 -583 2017-02-03 NaN 475.751113 -584 2017-02-04 NaN 475.683633 -585 2017-02-05 NaN 475.642912 -586 2017-02-06 NaN 475.709640 -587 2017-02-07 NaN 475.696524 -588 2017-02-08 NaN 475.612631 -589 2017-02-09 NaN 475.584171 -590 2017-02-10 NaN 475.561985 -591 2017-02-11 NaN 475.523791 -592 2017-02-12 NaN 475.494272 -593 2017-02-13 NaN 475.488171 -594 2017-02-14 NaN 475.464718 -595 2017-02-15 NaN 475.420739 -596 2017-02-16 NaN 475.392800 -597 2017-02-17 NaN 475.368402 -598 2017-02-18 NaN 475.337640 -599 2017-02-19 NaN 475.309556 -600 2017-02-20 NaN 475.286866 -601 2017-02-21 NaN 475.260120 -602 2017-02-22 NaN 475.227942 -603 2017-02-23 NaN 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'Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] -INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1673 MAPE_Forecast=0.2607 MAPE_Test=0.2045 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1536 SMAPE_Forecast=0.227 SMAPE_Test=0.1801 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7516 MASE_Forecast=0.7178 MASE_Test=0.9782 -INFO:pyaf.std:MODEL_L1 L1_Fit=58.91098991233605 L1_Forecast=166.97175138891998 L1_Test=73.61151528901921 -INFO:pyaf.std:MODEL_L2 L2_Fit=85.20380554596552 L2_Forecast=830.7590973586679 L2_Test=85.0288437678615 +INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear' [Seasonal_WeekOfYear] +INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(16)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1651 MAPE_Forecast=0.2176 MAPE_Test=0.3027 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1541 SMAPE_Forecast=0.1956 SMAPE_Test=0.2449 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7542 MASE_Forecast=0.6392 MASE_Test=1.3736 +INFO:pyaf.std:MODEL_L1 L1_Fit=59.11098959041995 L1_Forecast=148.6920586670083 L1_Test=103.37191778671165 +INFO:pyaf.std:MODEL_L2 L2_Fit=88.42002768205658 L2_Forecast=821.810574085627 L2_Test=132.09375726675188 INFO:pyaf.std:MODEL_COMPLEXITY 52 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.2797929565859967 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear -0.0005420274745318032 {27: -0.028451572709597417, 28: -0.03179288988892304, 29: -0.03217541869218132, 30: -0.030741777678414017, 31: -0.025404335126006705, 32: -0.026546159185554696, 33: -0.028070211490519137, 34: -0.022554311863814958, 35: 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0.004669929507026049, 22: 0.0016942640058457137, 23: -0.006149934635419685, 24: -0.00802473218069455, 25: -0.019900106839716214, 26: -0.022554311863814958} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag1 0.08399426462675277 -INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag2 0.059251664621044295 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag3 0.05468667878049506 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag4 0.050890516131101515 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag5 0.047225821098099974 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag7 0.047054573421857405 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag6 0.04642443180401457 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag8 0.04190628571522049 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag9 0.03432321104264001 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag10 0.03340236796931139 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag7 0.05488439111138001 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag14 0.05262074357659578 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag1 0.04690117402476945 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag3 -0.032511489690764395 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag4 -0.03199579122843625 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag11 -0.02925109893562619 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag10 -0.029208190291884213 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag8 0.025341100779806915 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag13 0.023788275470451235 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_Lag6 0.023078923987081036 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 4.426442623138428 +INFO:pyaf.std:START_FORECASTING '['4328']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4328']' 7.5962677001953125 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4328 ... 0.2584 0.2025 -1 None Anscombe_4328 ... 0.2607 0.2045 -2 None Anscombe_4328 ... 0.3131 0.2732 -3 None Anscombe_4328 ... 0.3141 0.2763 -4 None Anscombe_4328 ... 0.3165 0.2767 +0 None Anscombe_4328 ... 0.2089 0.2932 +1 None Anscombe_4328 ... 0.2176 0.3027 +2 None _4328 ... 0.2335 0.3225 +3 None Anscombe_4328 ... 0.2350 0.3232 +4 None _4328 ... 0.2531 0.3347 [5 rows x 8 columns] Forecast Columns Index(['Date', '4328', 'row_number', 'Date_Normalized', 'Anscombe_4328', 'Anscombe_4328_ConstantTrend', 'Anscombe_4328_ConstantTrend_residue', - 'Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek', - 'Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue', - 'Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(16)', - 'Anscombe_4328_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(16)_residue', + 'Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear', + 'Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue', + 'Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(16)', + 'Anscombe_4328_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(16)_residue', 'Anscombe_4328_Trend', 'Anscombe_4328_Trend_residue', 'Anscombe_4328_Cycle', 'Anscombe_4328_Cycle_residue', 'Anscombe_4328_AR', 'Anscombe_4328_AR_residue', @@ -2896,95 +3084,97 @@ memory usage: 14.4 KB None Forecasts Date 4328 4328_Forecast -550 2017-01-01 NaN 259.968262 -551 2017-01-02 NaN 392.992945 -552 2017-01-03 NaN 385.669499 -553 2017-01-04 NaN 397.497327 -554 2017-01-05 NaN 376.195550 -555 2017-01-06 NaN 287.803777 -556 2017-01-07 NaN 206.768857 -557 2017-01-08 NaN 276.042942 -558 2017-01-09 NaN 411.453775 -559 2017-01-10 NaN 405.794540 -560 2017-01-11 NaN 420.040877 -561 2017-01-12 NaN 401.440451 -562 2017-01-13 NaN 314.260609 -563 2017-01-14 NaN 233.901479 -564 2017-01-15 NaN 303.460009 -565 2017-01-16 NaN 437.801749 -566 2017-01-17 NaN 430.736239 -567 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"Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "Anscombe", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "52", - "MAE": "166.97175138891998", - "MAPE": "0.2607", - "MASE": "0.7178", - "RMSE": "830.7590973586679" + "Model_Performance": { + "COMPLEXITY": "52", + "MAE": "148.6920586670083", + "MAPE": "0.2176", + "MASE": "0.6392", + "RMSE": "821.810574085627" + } } } @@ -2993,48 +3183,57 @@ Forecasts 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'4329' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4329']' 32.6474335193634 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4329' Length=550 Min=3.0 Max=36650.0 Mean=85.32363636363637 StdDev=1560.5859414700776 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4329' Min=-36603.0 Max=36631.0 Mean=-0.012727272727272728 StdDev=2208.1209786154504 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4329_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_4329_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_4329_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_4329_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3984 MAPE_Forecast=0.4463 MAPE_Test=0.5301 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.4925 SMAPE_Forecast=0.6131 SMAPE_Test=0.7529 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5255 MASE_Forecast=1.225 MASE_Test=1.6524 -INFO:pyaf.std:MODEL_L1 L1_Fit=101.67825905872556 L1_Forecast=11.239561640982924 L1_Test=10.698384353741481 -INFO:pyaf.std:MODEL_L2 L2_Fit=1850.5494320744863 L2_Forecast=24.809127468171603 L2_Test=13.029989399145467 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4329' Min=3.0 Max=36650.0 Mean=85.32363636363637 StdDev=1560.5859414700776 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4329_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [ConstantTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4329_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4329_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4329_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3109 MAPE_Forecast=0.3704 MAPE_Test=0.3813 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2898 SMAPE_Forecast=0.3216 SMAPE_Test=0.3229 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5103 MASE_Forecast=0.8374 MASE_Test=0.8675 +INFO:pyaf.std:MODEL_L1 L1_Fit=98.7295918367347 L1_Forecast=7.683673469387755 L1_Test=5.616666666666666 +INFO:pyaf.std:MODEL_L2 L2_Fit=1850.4117481671753 L2_Forecast=21.881172598508385 L2_Test=7.479973261984297 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 112.12244897959184 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4329_ConstantTrend_residue_Seasonal_DayOfWeek -94.12244897959184 {2: -93.12244897959184, 3: -93.12244897959184, 4: -93.12244897959184, 5: -99.12244897959184, 6: -98.12244897959184, 0: -93.12244897959184, 1: -93.12244897959184} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.2752699851989746 +INFO:pyaf.std:START_FORECASTING '['4329']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4329']' 4.080682277679443 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4329 ... 0.4463 0.5301 -1 None _4329 ... 0.4760 0.4312 -2 None Anscombe_4329 ... 0.4760 0.4312 -3 None Diff_4329 ... 0.4760 0.4312 -4 None Anscombe_4329 ... 0.4979 0.3945 +0 None _4329 ... 0.3704 0.3813 +1 None Anscombe_4329 ... 0.3704 0.3813 +2 None Anscombe_4329 ... 0.3805 0.5154 +3 None Anscombe_4329 ... 0.3837 0.5460 +4 None Anscombe_4329 ... 0.3877 0.5203 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4329', 'row_number', 'Date_Normalized', 'Diff_4329', - 'Diff_4329_ConstantTrend', 'Diff_4329_ConstantTrend_residue', - 'Diff_4329_ConstantTrend_residue_zeroCycle', - 'Diff_4329_ConstantTrend_residue_zeroCycle_residue', - 'Diff_4329_ConstantTrend_residue_zeroCycle_residue_NoAR', - 'Diff_4329_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', - 'Diff_4329_Trend', 'Diff_4329_Trend_residue', 'Diff_4329_Cycle', - 'Diff_4329_Cycle_residue', 'Diff_4329_AR', 'Diff_4329_AR_residue', - 'Diff_4329_TransformedForecast', '4329_Forecast', - 'Diff_4329_TransformedResidue', '4329_Residue'], +Forecast Columns Index(['Date', '4329', 'row_number', 'Date_Normalized', '_4329', + '_4329_ConstantTrend', '_4329_ConstantTrend_residue', + '_4329_ConstantTrend_residue_Seasonal_DayOfWeek', + '_4329_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + '_4329_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4329_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4329_Trend', '_4329_Trend_residue', '_4329_Cycle', + '_4329_Cycle_residue', '_4329_AR', '_4329_AR_residue', + '_4329_TransformedForecast', '4329_Forecast', + '_4329_TransformedResidue', '4329_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -3049,95 +3248,97 @@ memory usage: 14.4 KB None Forecasts Date 4329 4329_Forecast -550 2017-01-01 NaN 6.971939 -551 2017-01-02 NaN 6.959184 -552 2017-01-03 NaN 6.946429 -553 2017-01-04 NaN 6.933673 -554 2017-01-05 NaN 6.920918 -555 2017-01-06 NaN 6.908163 -556 2017-01-07 NaN 6.895408 -557 2017-01-08 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+609 2017-03-01 NaN 19.0 { - "Dataset": { - "Signal": "4329", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4329": { + "Dataset": { + "Signal": "4329", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4329_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "Diff_4329_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "Difference", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "11.239561640982924", - "MAPE": "0.4463", - "MASE": "1.225", - "RMSE": "24.809127468171603" + "Model_Performance": { + "COMPLEXITY": "4", + "MAE": "7.683673469387755", + "MAPE": "0.3704", + "MASE": "0.8374", + "RMSE": "21.881172598508385" + } } } @@ -3146,8 +3347,8 @@ Forecasts 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'4330' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4330']' 31.021053075790405 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4330' Length=550 Min=150.0 Max=1324.0 Mean=629.8127272727273 StdDev=282.4053621397605 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4330' Min=1.224744871391589 Max=2.345207879911715 Mean=1.7491864624364764 StdDev=0.27412235348626146 @@ -3159,33 +3360,42 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4330_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1323 MAPE_Forecast=0.1316 MAPE_Test=0.14 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.126 SMAPE_Forecast=0.1253 SMAPE_Test=0.1406 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4501 MASE_Forecast=0.5116 MASE_Test=0.4993 -INFO:pyaf.std:MODEL_L1 L1_Fit=72.3522837259324 L1_Forecast=64.00910766825105 L1_Test=84.76972684951805 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Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_Lag7 0.3569458741734162 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_Lag14 0.24836399678605997 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_Lag6 0.19737036678390968 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_Lag1 0.619189080177825 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_Lag7 0.3569458741734159 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_Lag14 0.2483639967860598 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_Lag6 0.19737036678390987 INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.17356135010104845 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_Lag3 0.13046419628685368 -INFO:pyaf.std:AR_MODEL_COEFF 7 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+INFO:pyaf.std:START_FORECASTING '['4330']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4330']' 7.775059461593628 Split Transformation ... ForecastMAPE TestMAPE 0 None Anscombe_4330 ... 0.1316 0.1400 -1 None Anscombe_4330 ... 0.1335 0.1423 -2 None Anscombe_4330 ... 0.1374 0.1425 -3 None Anscombe_4330 ... 0.1381 0.1319 -4 None Anscombe_4330 ... 0.1381 0.1319 +1 None Anscombe_4330 ... 0.1316 0.1400 +2 None Anscombe_4330 ... 0.1335 0.1423 +3 None Anscombe_4330 ... 0.1335 0.1423 +4 None Anscombe_4330 ... 0.1374 0.1425 [5 rows x 8 columns] Forecast Columns Index(['Date', '4330', 'row_number', 'Date_Normalized', 'Anscombe_4330', @@ -3277,31 +3487,33 @@ Forecasts { - "Dataset": { - "Signal": "4330", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4330": { + "Dataset": { + "Signal": "4330", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "Anscombe", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "64.00910766825105", - "MAPE": "0.1316", - "MASE": "0.5116", - "RMSE": "92.31925378185686" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Anscombe_4330_ConstantTrend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Anscombe", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "64.00910766825108", + "MAPE": "0.1316", + "MASE": "0.5116", + "RMSE": "92.31925378185687" + } } } @@ -3310,8 +3522,8 @@ Forecasts 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'4331' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4331']' 27.204013347625732 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4331' Length=550 Min=162.0 Max=1531.0 Mean=600.7418181818182 StdDev=248.31003105788326 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4331' Min=1.224744871391589 Max=2.345207879911715 Mean=1.6530115514906845 StdDev=0.2224556989099025 @@ -3320,36 +3532,45 @@ INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4331_ConstantTrend_residue_Seasonal_ INFO:pyaf.std:TREND_DETAIL 'Anscombe_4331_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1116 MAPE_Forecast=0.1149 MAPE_Test=0.1383 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1077 SMAPE_Forecast=0.1056 SMAPE_Test=0.1361 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3446 MASE_Forecast=0.3672 MASE_Test=0.4326 -INFO:pyaf.std:MODEL_L1 L1_Fit=56.179108442232526 L1_Forecast=54.580480969244796 L1_Test=63.214474269792134 -INFO:pyaf.std:MODEL_L2 L2_Fit=87.07937676548298 L2_Forecast=83.7632863156847 L2_Test=86.439049175736 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1105 MAPE_Forecast=0.1215 MAPE_Test=0.1436 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1084 SMAPE_Forecast=0.1139 SMAPE_Test=0.1454 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3367 MASE_Forecast=0.3784 MASE_Test=0.4439 +INFO:pyaf.std:MODEL_L1 L1_Fit=54.88372730739848 L1_Forecast=56.23766399155371 L1_Test=64.86909651255083 +INFO:pyaf.std:MODEL_L2 L2_Fit=87.96521427331417 L2_Forecast=84.9816377769849 L2_Test=88.91011717318261 INFO:pyaf.std:MODEL_COMPLEXITY 52 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.664059689273369 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek 0.051145322826710915 {2: 0.1835874823589082, 3: 0.17487014209324103, 4: 0.05836917246756501, 5: -0.29725614240955456, 6: -0.23507361529582216, 0: 0.1835874823589082, 1: 0.20754969530388934} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag1 0.5146294400146579 -INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag7 0.12044280971899828 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag3 0.11709866332349542 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag16 -0.06146091824157024 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag6 0.058338546077127545 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag11 0.04343213428000156 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag13 -0.04053495538715046 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag4 0.03802071711265056 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag14 0.03238285447061312 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag5 0.0310857136872118 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag1 0.5205440940989309 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag7 0.14299980616396946 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag3 0.11479630776465682 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag6 0.07110230821017903 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag16 -0.07018964149726317 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag14 0.051604148590330856 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag11 0.03711803385956332 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag4 0.03317260310324849 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag12 -0.029941734377353632 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4331_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag13 -0.028071445178640797 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.9196889400482178 +INFO:pyaf.std:START_FORECASTING '['4331']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4331']' 8.18502926826477 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4331 ... 0.1149 0.1383 -1 None Anscombe_4331 ... 0.1149 0.1383 -2 None Anscombe_4331 ... 0.1218 0.1454 -3 None Anscombe_4331 ... 0.1218 0.1454 -4 None Anscombe_4331 ... 0.1271 0.1468 +0 None Anscombe_4331 ... 0.1215 0.1436 +1 None Anscombe_4331 ... 0.1274 0.1514 +2 None Anscombe_4331 ... 0.1313 0.1490 +3 None Anscombe_4331 ... 0.1408 0.1651 +4 None Anscombe_4331 ... 0.1408 0.1651 [5 rows x 8 columns] Forecast Columns Index(['Date', '4331', 'row_number', 'Date_Normalized', 'Anscombe_4331', @@ -3377,95 +3598,97 @@ memory usage: 14.4 KB None Forecasts Date 4331 4331_Forecast -550 2017-01-01 NaN 177.666374 -551 2017-01-02 NaN 511.221754 -552 2017-01-03 NaN 536.401913 -553 2017-01-04 NaN 528.530247 -554 2017-01-05 NaN 517.792920 -555 2017-01-06 NaN 412.624180 -556 2017-01-07 NaN 148.662959 -557 2017-01-08 NaN 195.698127 -558 2017-01-09 NaN 582.685667 -559 2017-01-10 NaN 609.868265 -560 2017-01-11 NaN 621.336185 -561 2017-01-12 NaN 612.159571 -562 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'4332' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4332']' 26.646286725997925 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4332' Length=550 Min=74.0 Max=6792.0 Mean=463.4672727272727 StdDev=870.7421441828794 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4332' Min=74.0 Max=6792.0 Mean=463.4672727272727 StdDev=870.7421441828794 @@ -3490,20 +3713,29 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.9911 MASE_Test=0.9882 INFO:pyaf.std:MODEL_L1 L1_Fit=129.55357142857142 L1_Forecast=30.255102040816325 L1_Test=88.36666666666666 INFO:pyaf.std:MODEL_L2 L2_Fit=348.9634395504164 L2_Forecast=66.21108791785034 L2_Test=138.96390418618307 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 165.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4332_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.249065637588501 +INFO:pyaf.std:START_FORECASTING '['4332']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4332']' 3.0832431316375732 Split Transformation ... ForecastMAPE TestMAPE -0 None _4332 ... 0.2030 0.2672 -1 None Anscombe_4332 ... 0.2030 0.2672 -2 None Diff_4332 ... 0.2030 0.2672 -3 None Diff_4332 ... 0.2162 0.2984 -4 None Anscombe_4332 ... 0.2224 0.2675 +0 None _4332 ... 0.203 0.2672 +1 None _4332 ... 0.203 0.2672 +2 None Anscombe_4332 ... 0.203 0.2672 +3 None Diff_4332 ... 0.203 0.2672 +4 None Anscombe_4332 ... 0.203 0.2672 [5 rows x 8 columns] Forecast Columns Index(['Date', '4332', 'row_number', 'Date_Normalized', '_4332', @@ -3593,31 +3825,33 @@ Forecasts { - "Dataset": { - "Signal": "4332", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4332": { + "Dataset": { + "Signal": "4332", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4332_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_4332_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "30.255102040816325", - "MAPE": "0.203", - "MASE": "0.9911", - "RMSE": "66.21108791785034" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "30.255102040816325", + "MAPE": "0.203", + "MASE": "0.9911", + "RMSE": "66.21108791785034" + } } } @@ -3627,45 +3861,55 @@ Forecasts 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'4333' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4333' 5.467613697052002 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4333']' 22.981666088104248 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4333' Length=550 Min=248.0 Max=10628.0 Mean=1181.909090909091 StdDev=1453.4582781356305 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4333' Min=248.0 Max=10628.0 Mean=1181.909090909091 StdDev=1453.4582781356305 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_4333_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_4333_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [Lag1Trend + Seasonal_DayOfWeek + NoAR] INFO:pyaf.std:TREND_DETAIL '_4333_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_4333_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_4333_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1318 MAPE_Forecast=0.1336 MAPE_Test=0.144 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1322 SMAPE_Forecast=0.1308 SMAPE_Test=0.1394 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.9909 MASE_Test=0.987 -INFO:pyaf.std:MODEL_L1 L1_Fit=252.10204081632654 L1_Forecast=75.25510204081633 L1_Test=93.98333333333333 -INFO:pyaf.std:MODEL_L2 L2_Fit=689.5326574675674 L2_Forecast=113.43914128514109 L2_Test=161.83082730637778 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:CYCLE_DETAIL '_4333_Lag1Trend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4333_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1307 MAPE_Forecast=0.1257 MAPE_Test=0.1429 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1322 SMAPE_Forecast=0.1247 SMAPE_Test=0.1399 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9904 MASE_Forecast=0.9374 MASE_Test=0.9836 +INFO:pyaf.std:MODEL_L1 L1_Fit=250.3265306122449 L1_Forecast=71.1938775510204 L1_Test=93.65833333333333 +INFO:pyaf.std:MODEL_L2 L2_Fit=690.5640533177889 L2_Forecast=107.76180744056376 L2_Test=160.6609395590602 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 248.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4333_Lag1Trend_residue_Seasonal_DayOfWeek -5.0 {2: 9.0, 3: -34.5, 4: -28.0, 5: -1.0, 6: 22.0, 0: 7.0, 1: -14.0} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.218287706375122 +INFO:pyaf.std:START_FORECASTING '['4333']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4333']' 2.503446578979492 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4333 ... 0.1292 0.1447 -1 None _4333 ... 0.1308 0.1492 -2 None _4333 ... 0.1336 0.1440 -3 None Anscombe_4333 ... 0.1336 0.1440 -4 None Diff_4333 ... 0.1336 0.1440 +0 None Anscombe_4333 ... 0.1226 0.1445 +1 None _4333 ... 0.1248 0.1519 +2 None Anscombe_4333 ... 0.1256 0.1428 +3 None _4333 ... 0.1257 0.1429 +4 None Anscombe_4333 ... 0.1292 0.1447 [5 rows x 8 columns] Forecast Columns Index(['Date', '4333', 'row_number', 'Date_Normalized', '_4333', '_4333_Lag1Trend', '_4333_Lag1Trend_residue', - '_4333_Lag1Trend_residue_zeroCycle', - '_4333_Lag1Trend_residue_zeroCycle_residue', - '_4333_Lag1Trend_residue_zeroCycle_residue_NoAR', - '_4333_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', '_4333_Trend', - '_4333_Trend_residue', '_4333_Cycle', '_4333_Cycle_residue', '_4333_AR', - '_4333_AR_residue', '_4333_TransformedForecast', '4333_Forecast', + '_4333_Lag1Trend_residue_Seasonal_DayOfWeek', + '_4333_Lag1Trend_residue_Seasonal_DayOfWeek_residue', + '_4333_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4333_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4333_Trend', '_4333_Trend_residue', '_4333_Cycle', + '_4333_Cycle_residue', '_4333_AR', '_4333_AR_residue', + '_4333_TransformedForecast', '4333_Forecast', '_4333_TransformedResidue', '4333_Residue'], dtype='object') @@ -3681,95 +3925,97 @@ memory usage: 14.4 KB None Forecasts Date 4333 4333_Forecast -550 2017-01-01 NaN 462.0 -551 2017-01-02 NaN 462.0 -552 2017-01-03 NaN 462.0 -553 2017-01-04 NaN 462.0 -554 2017-01-05 NaN 462.0 -555 2017-01-06 NaN 462.0 -556 2017-01-07 NaN 462.0 -557 2017-01-08 NaN 462.0 -558 2017-01-09 NaN 462.0 -559 2017-01-10 NaN 462.0 -560 2017-01-11 NaN 462.0 -561 2017-01-12 NaN 462.0 -562 2017-01-13 NaN 462.0 -563 2017-01-14 NaN 462.0 -564 2017-01-15 NaN 462.0 -565 2017-01-16 NaN 462.0 -566 2017-01-17 NaN 462.0 -567 2017-01-18 NaN 462.0 -568 2017-01-19 NaN 462.0 -569 2017-01-20 NaN 462.0 -570 2017-01-21 NaN 462.0 -571 2017-01-22 NaN 462.0 -572 2017-01-23 NaN 462.0 -573 2017-01-24 NaN 462.0 -574 2017-01-25 NaN 462.0 -575 2017-01-26 NaN 462.0 -576 2017-01-27 NaN 462.0 -577 2017-01-28 NaN 462.0 -578 2017-01-29 NaN 462.0 -579 2017-01-30 NaN 462.0 -580 2017-01-31 NaN 462.0 -581 2017-02-01 NaN 462.0 -582 2017-02-02 NaN 462.0 -583 2017-02-03 NaN 462.0 -584 2017-02-04 NaN 462.0 -585 2017-02-05 NaN 462.0 -586 2017-02-06 NaN 462.0 -587 2017-02-07 NaN 462.0 -588 2017-02-08 NaN 462.0 -589 2017-02-09 NaN 462.0 -590 2017-02-10 NaN 462.0 -591 2017-02-11 NaN 462.0 -592 2017-02-12 NaN 462.0 -593 2017-02-13 NaN 462.0 -594 2017-02-14 NaN 462.0 -595 2017-02-15 NaN 462.0 -596 2017-02-16 NaN 462.0 -597 2017-02-17 NaN 462.0 -598 2017-02-18 NaN 462.0 -599 2017-02-19 NaN 462.0 -600 2017-02-20 NaN 462.0 -601 2017-02-21 NaN 462.0 -602 2017-02-22 NaN 462.0 -603 2017-02-23 NaN 462.0 -604 2017-02-24 NaN 462.0 -605 2017-02-25 NaN 462.0 -606 2017-02-26 NaN 462.0 -607 2017-02-27 NaN 462.0 -608 2017-02-28 NaN 462.0 -609 2017-03-01 NaN 462.0 +550 2017-01-01 NaN 484.0 +551 2017-01-02 NaN 491.0 +552 2017-01-03 NaN 477.0 +553 2017-01-04 NaN 486.0 +554 2017-01-05 NaN 451.5 +555 2017-01-06 NaN 423.5 +556 2017-01-07 NaN 422.5 +557 2017-01-08 NaN 444.5 +558 2017-01-09 NaN 451.5 +559 2017-01-10 NaN 437.5 +560 2017-01-11 NaN 446.5 +561 2017-01-12 NaN 412.0 +562 2017-01-13 NaN 384.0 +563 2017-01-14 NaN 383.0 +564 2017-01-15 NaN 405.0 +565 2017-01-16 NaN 412.0 +566 2017-01-17 NaN 398.0 +567 2017-01-18 NaN 407.0 +568 2017-01-19 NaN 372.5 +569 2017-01-20 NaN 344.5 +570 2017-01-21 NaN 343.5 +571 2017-01-22 NaN 365.5 +572 2017-01-23 NaN 372.5 +573 2017-01-24 NaN 358.5 +574 2017-01-25 NaN 367.5 +575 2017-01-26 NaN 333.0 +576 2017-01-27 NaN 305.0 +577 2017-01-28 NaN 304.0 +578 2017-01-29 NaN 326.0 +579 2017-01-30 NaN 333.0 +580 2017-01-31 NaN 319.0 +581 2017-02-01 NaN 328.0 +582 2017-02-02 NaN 293.5 +583 2017-02-03 NaN 265.5 +584 2017-02-04 NaN 264.5 +585 2017-02-05 NaN 286.5 +586 2017-02-06 NaN 293.5 +587 2017-02-07 NaN 279.5 +588 2017-02-08 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-INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.2481 SMAPE_Forecast=0.324 SMAPE_Test=0.9124 -INFO:pyaf.std:MODEL_MASE MASE_Fit=2.4406 MASE_Forecast=0.959 MASE_Test=1.391 -INFO:pyaf.std:MODEL_L1 L1_Fit=448.3540148293641 L1_Forecast=34.73275765646738 L1_Test=63.20821610950532 -INFO:pyaf.std:MODEL_L2 L2_Fit=779.6660827990861 L2_Forecast=67.70378120888535 L2_Test=93.38703751089027 -INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4334' Min=27.0 Max=5492.0 Mean=254.4690909090909 StdDev=612.3126488598117 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4334_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [ConstantTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4334_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4334_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4334_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.4314 MAPE_Forecast=0.2644 MAPE_Test=0.3574 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.5606 SMAPE_Forecast=0.3113 SMAPE_Test=0.3751 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.4333 MASE_Forecast=0.9492 MASE_Test=0.8581 +INFO:pyaf.std:MODEL_L1 L1_Fit=263.30867346938777 L1_Forecast=34.37755102040816 L1_Test=38.99166666666667 +INFO:pyaf.std:MODEL_L2 L2_Fit=755.3512850161243 L2_Forecast=66.73974735905898 L2_Test=71.6718505877075 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 318.76275510204084 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4334_ConstantTrend_residue_Seasonal_DayOfWeek -246.76275510204084 {2: -244.76275510204084, 3: -245.76275510204084, 4: -256.26275510204084, 5: -252.76275510204084, 6: -239.26275510204084, 0: -243.26275510204084, 1: -246.26275510204084} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.0637857913970947 +INFO:pyaf.std:START_FORECASTING '['4334']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4334']' 2.4062700271606445 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4334 ... 0.2846 0.6031 -1 None _4334 ... 0.3991 0.4338 -2 None Anscombe_4334 ... 0.3991 0.4338 -3 None Diff_4334 ... 0.3991 0.4338 -4 None Anscombe_4334 ... 0.4516 0.4952 +0 None Anscombe_4334 ... 0.2604 0.3655 +1 None _4334 ... 0.2644 0.3574 +2 None Anscombe_4334 ... 0.2644 0.3574 +3 None Diff_4334 ... 0.2846 0.6031 +4 None Diff_4334 ... 0.2846 0.6031 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4334', 'row_number', 'Date_Normalized', 'Diff_4334', - 'Diff_4334_LinearTrend', 'Diff_4334_LinearTrend_residue', - 'Diff_4334_LinearTrend_residue_zeroCycle', - 'Diff_4334_LinearTrend_residue_zeroCycle_residue', - 'Diff_4334_LinearTrend_residue_zeroCycle_residue_NoAR', - 'Diff_4334_LinearTrend_residue_zeroCycle_residue_NoAR_residue', - 'Diff_4334_Trend', 'Diff_4334_Trend_residue', 'Diff_4334_Cycle', - 'Diff_4334_Cycle_residue', 'Diff_4334_AR', 'Diff_4334_AR_residue', - 'Diff_4334_TransformedForecast', '4334_Forecast', - 'Diff_4334_TransformedResidue', '4334_Residue'], +Forecast Columns Index(['Date', '4334', 'row_number', 'Date_Normalized', '_4334', + '_4334_ConstantTrend', '_4334_ConstantTrend_residue', + '_4334_ConstantTrend_residue_Seasonal_DayOfWeek', + '_4334_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + '_4334_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4334_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4334_Trend', '_4334_Trend_residue', '_4334_Cycle', + '_4334_Cycle_residue', '_4334_AR', '_4334_AR_residue', + '_4334_TransformedForecast', '4334_Forecast', + '_4334_TransformedResidue', '4334_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -3834,95 +4089,97 @@ memory usage: 14.4 KB None Forecasts Date 4334 4334_Forecast -550 2017-01-01 NaN 13.222411 -551 2017-01-02 NaN 12.790348 -552 2017-01-03 NaN 12.360494 -553 2017-01-04 NaN 11.932846 -554 2017-01-05 NaN 11.507407 -555 2017-01-06 NaN 11.084175 -556 2017-01-07 NaN 10.663150 -557 2017-01-08 NaN 10.244333 -558 2017-01-09 NaN 9.827723 -559 2017-01-10 NaN 9.413321 -560 2017-01-11 NaN 9.001127 -561 2017-01-12 NaN 8.591140 -562 2017-01-13 NaN 8.183360 -563 2017-01-14 NaN 7.777788 -564 2017-01-15 NaN 7.374424 -565 2017-01-16 NaN 6.973267 -566 2017-01-17 NaN 6.574318 -567 2017-01-18 NaN 6.177576 -568 2017-01-19 NaN 5.783041 -569 2017-01-20 NaN 5.390715 -570 2017-01-21 NaN 5.000595 -571 2017-01-22 NaN 4.612684 -572 2017-01-23 NaN 4.226979 -573 2017-01-24 NaN 3.843483 -574 2017-01-25 NaN 3.462193 -575 2017-01-26 NaN 3.083112 -576 2017-01-27 NaN 2.706238 -577 2017-01-28 NaN 2.331571 -578 2017-01-29 NaN 1.959112 -579 2017-01-30 NaN 1.588860 -580 2017-01-31 NaN 1.220816 -581 2017-02-01 NaN 0.854980 -582 2017-02-02 NaN 0.491351 -583 2017-02-03 NaN 0.129929 -584 2017-02-04 NaN -0.229285 -585 2017-02-05 NaN -0.586291 -586 2017-02-06 NaN -0.941090 -587 2017-02-07 NaN -1.293681 -588 2017-02-08 NaN -1.644065 -589 2017-02-09 NaN -1.992242 -590 2017-02-10 NaN -2.338210 -591 2017-02-11 NaN -2.681972 -592 2017-02-12 NaN -3.023526 -593 2017-02-13 NaN -3.362872 -594 2017-02-14 NaN -3.700010 -595 2017-02-15 NaN -4.034942 -596 2017-02-16 NaN -4.367665 -597 2017-02-17 NaN -4.698182 -598 2017-02-18 NaN -5.026490 -599 2017-02-19 NaN -5.352591 -600 2017-02-20 NaN -5.676485 -601 2017-02-21 NaN -5.998171 -602 2017-02-22 NaN -6.317649 -603 2017-02-23 NaN -6.634920 -604 2017-02-24 NaN -6.949984 -605 2017-02-25 NaN -7.262840 -606 2017-02-26 NaN -7.573488 -607 2017-02-27 NaN -7.881929 -608 2017-02-28 NaN -8.188163 -609 2017-03-01 NaN -8.492189 +550 2017-01-01 NaN 79.5 +551 2017-01-02 NaN 75.5 +552 2017-01-03 NaN 72.5 +553 2017-01-04 NaN 74.0 +554 2017-01-05 NaN 73.0 +555 2017-01-06 NaN 62.5 +556 2017-01-07 NaN 66.0 +557 2017-01-08 NaN 79.5 +558 2017-01-09 NaN 75.5 +559 2017-01-10 NaN 72.5 +560 2017-01-11 NaN 74.0 +561 2017-01-12 NaN 73.0 +562 2017-01-13 NaN 62.5 +563 2017-01-14 NaN 66.0 +564 2017-01-15 NaN 79.5 +565 2017-01-16 NaN 75.5 +566 2017-01-17 NaN 72.5 +567 2017-01-18 NaN 74.0 +568 2017-01-19 NaN 73.0 +569 2017-01-20 NaN 62.5 +570 2017-01-21 NaN 66.0 +571 2017-01-22 NaN 79.5 +572 2017-01-23 NaN 75.5 +573 2017-01-24 NaN 72.5 +574 2017-01-25 NaN 74.0 +575 2017-01-26 NaN 73.0 +576 2017-01-27 NaN 62.5 +577 2017-01-28 NaN 66.0 +578 2017-01-29 NaN 79.5 +579 2017-01-30 NaN 75.5 +580 2017-01-31 NaN 72.5 +581 2017-02-01 NaN 74.0 +582 2017-02-02 NaN 73.0 +583 2017-02-03 NaN 62.5 +584 2017-02-04 NaN 66.0 +585 2017-02-05 NaN 79.5 +586 2017-02-06 NaN 75.5 +587 2017-02-07 NaN 72.5 +588 2017-02-08 NaN 74.0 +589 2017-02-09 NaN 73.0 +590 2017-02-10 NaN 62.5 +591 2017-02-11 NaN 66.0 +592 2017-02-12 NaN 79.5 +593 2017-02-13 NaN 75.5 +594 2017-02-14 NaN 72.5 +595 2017-02-15 NaN 74.0 +596 2017-02-16 NaN 73.0 +597 2017-02-17 NaN 62.5 +598 2017-02-18 NaN 66.0 +599 2017-02-19 NaN 79.5 +600 2017-02-20 NaN 75.5 +601 2017-02-21 NaN 72.5 +602 2017-02-22 NaN 74.0 +603 2017-02-23 NaN 73.0 +604 2017-02-24 NaN 62.5 +605 2017-02-25 NaN 66.0 +606 2017-02-26 NaN 79.5 +607 2017-02-27 NaN 75.5 +608 2017-02-28 NaN 72.5 +609 2017-03-01 NaN 74.0 { - "Dataset": { - "Signal": "4334", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4334": { + "Dataset": { + "Signal": "4334", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4334_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "Diff_4334_LinearTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "Difference", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "34.73275765646738", - 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+INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4335']' 5.199293375015259 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4335 ... 0.1103 0.1121 +0 None Anscombe_4335 ... 0.1112 0.1121 1 None Anscombe_4335 ... 0.1112 0.1121 -2 None Anscombe_4335 ... 0.1141 0.1120 -3 None Anscombe_4335 ... 0.1144 0.1102 -4 None Anscombe_4335 ... 0.1160 0.1010 +2 None Anscombe_4335 ... 0.1122 0.1027 +3 None Anscombe_4335 ... 0.1141 0.1120 +4 None Anscombe_4335 ... 0.1141 0.1120 [5 rows x 8 columns] Forecast Columns Index(['Date', '4335', 'row_number', 'Date_Normalized', '_4335', @@ -4061,31 +4327,33 @@ Forecasts { - "Dataset": { - "Signal": "4335", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4335": { + "Dataset": { + "Signal": "4335", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4335_ConstantTrend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "16", - "MAE": "176.1167826813093", - "MAPE": "0.1174", - "MASE": "0.9313", - "RMSE": "303.0167737536434" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_4335_ConstantTrend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "16", + "MAE": "176.1167826813094", + "MAPE": "0.1174", + "MASE": "0.9313", + "RMSE": "303.0167737536434" + } } } @@ -4094,8 +4362,8 @@ Forecasts 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'4337' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4337']' 17.502991199493408 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4337' Length=550 Min=7.0 Max=3006.0 Mean=143.11818181818182 StdDev=292.93142020300377 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4337' Min=7.0 Max=3006.0 Mean=143.11818181818182 StdDev=292.93142020300377 @@ -4273,20 +4552,29 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.9904 MASE_Test=0.9925 INFO:pyaf.std:MODEL_L1 L1_Fit=57.58418367346939 L1_Forecast=77.92857142857143 L1_Test=52.3 INFO:pyaf.std:MODEL_L2 L2_Fit=223.35928433346842 L2_Forecast=269.0635878109751 L2_Test=154.09921046736957 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 16.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4337_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.7137799263000488 +INFO:pyaf.std:START_FORECASTING '['4337']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4337']' 2.61059308052063 Split Transformation ... ForecastMAPE TestMAPE -0 None _4337 ... 0.2504 0.4048 -1 None Anscombe_4337 ... 0.2504 0.4048 -2 None Diff_4337 ... 0.2504 0.4048 -3 None Anscombe_4337 ... 0.2763 0.4555 -4 None Anscombe_4337 ... 0.2914 0.6659 +0 None _4337 ... 0.2452 0.3984 +1 None Anscombe_4337 ... 0.2452 0.3985 +2 None Anscombe_4337 ... 0.2467 0.4071 +3 None _4337 ... 0.2468 0.4071 +4 None _4337 ... 0.2498 0.4037 [5 rows x 8 columns] Forecast Columns Index(['Date', '4337', 'row_number', 'Date_Normalized', '_4337', @@ -4376,31 +4664,33 @@ Forecasts { - "Dataset": { - "Signal": "4337", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4337": { + "Dataset": { + "Signal": "4337", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4337_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_4337_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "77.92857142857143", - "MAPE": "0.2504", - "MASE": "0.9904", - "RMSE": "269.0635878109751" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "77.92857142857143", + "MAPE": "0.2504", + "MASE": "0.9904", + "RMSE": "269.0635878109751" + } } } @@ -4410,7 +4700,7 @@ Forecasts {"Date":{"490":"2016-11-02T00:00:00.000Z","491":"2016-11-03T00:00:00.000Z","492":"2016-11-04T00:00:00.000Z","493":"2016-11-05T00:00:00.000Z","494":"2016-11-06T00:00:00.000Z","495":"2016-11-07T00:00:00.000Z","496":"2016-11-08T00:00:00.000Z","497":"2016-11-09T00:00:00.000Z","498":"2016-11-10T00:00:00.000Z","499":"2016-11-11T00:00:00.000Z","500":"2016-11-12T00:00:00.000Z","501":"2016-11-13T00:00:00.000Z","502":"2016-11-14T00:00:00.000Z","503":"2016-11-15T00:00:00.000Z","504":"2016-11-16T00:00:00.000Z","505":"2016-11-17T00:00:00.000Z","506":"2016-11-18T00:00:00.000Z","507":"2016-11-19T00:00:00.000Z","508":"2016-11-20T00:00:00.000Z","509":"2016-11-21T00:00:00.000Z","510":"2016-11-22T00:00:00.000Z","511":"2016-11-23T00:00:00.000Z","512":"2016-11-24T00:00:00.000Z","513":"2016-11-25T00:00:00.000Z","514":"2016-11-26T00:00:00.000Z","515":"2016-11-27T00:00:00.000Z","516":"2016-11-28T00:00:00.000Z","517":"2016-11-29T00:00:00.000Z","518":"2016-11-30T00:00:00.000Z","519":"2016-12-01T00:00:00.000Z","520":"2016-12-02T00:00:00.000Z","521":"2016-12-03T00:00:00.000Z","522":"2016-12-04T00:00:00.000Z","523":"2016-12-05T00:00:00.000Z","524":"2016-12-06T00:00:00.000Z","525":"2016-12-07T00:00:00.000Z","526":"2016-12-08T00:00:00.000Z","527":"2016-12-09T00:00:00.000Z","528":"2016-12-10T00:00:00.000Z","529":"2016-12-11T00:00:00.000Z","530":"2016-12-12T00:00:00.000Z","531":"2016-12-13T00:00:00.000Z","532":"2016-12-14T00:00:00.000Z","533":"2016-12-15T00:00:00.000Z","534":"2016-12-16T00:00:00.000Z","535":"2016-12-17T00:00:00.000Z","536":"2016-12-18T00:00:00.000Z","537":"2016-12-19T00:00:00.000Z","538":"2016-12-20T00:00:00.000Z","539":"2016-12-21T00:00:00.000Z","540":"2016-12-22T00:00:00.000Z","541":"2016-12-23T00:00:00.000Z","542":"2016-12-24T00:00:00.000Z","543":"2016-12-25T00:00:00.000Z","544":"2016-12-26T00:00:00.000Z","545":"2016-12-27T00:00:00.000Z","546":"2016-12-28T00:00:00.000Z","547":"2016-12-29T00:00:00.000Z","548":"2016-12-30T00:00:00.000Z","549":"2016-12-31T00:00:00.000Z","550":"2017-01-01T00:00:00.000Z","551":"2017-01-02T00:00:00.000Z","552":"2017-01-03T00:00:00.000Z","553":"2017-01-04T00:00:00.000Z","554":"2017-01-05T00:00:00.000Z","555":"2017-01-06T00:00:00.000Z","556":"2017-01-07T00:00:00.000Z","557":"2017-01-08T00:00:00.000Z","558":"2017-01-09T00:00:00.000Z","559":"2017-01-10T00:00:00.000Z","560":"2017-01-11T00:00:00.000Z","561":"2017-01-12T00:00:00.000Z","562":"2017-01-13T00:00:00.000Z","563":"2017-01-14T00:00:00.000Z","564":"2017-01-15T00:00:00.000Z","565":"2017-01-16T00:00:00.000Z","566":"2017-01-17T00:00:00.000Z","567":"2017-01-18T00:00:00.000Z","568":"2017-01-19T00:00:00.000Z","569":"2017-01-20T00:00:00.000Z","570":"2017-01-21T00:00:00.000Z","571":"2017-01-22T00:00:00.000Z","572":"2017-01-23T00:00:00.000Z","573":"2017-01-24T00:00:00.000Z","574":"2017-01-25T00:00:00.000Z","575":"2017-01-26T00:00:00.000Z","576":"2017-01-27T00:00:00.000Z","577":"2017-01-28T00:00:00.000Z","578":"2017-01-29T00:00:00.000Z","579":"2017-01-30T00:00:00.000Z","580":"2017-01-31T00:00:00.000Z","581":"2017-02-01T00:00:00.000Z","582":"2017-02-02T00:00:00.000Z","583":"2017-02-03T00:00:00.000Z","584":"2017-02-04T00:00:00.000Z","585":"2017-02-05T00:00:00.000Z","586":"2017-02-06T00:00:00.000Z","587":"2017-02-07T00:00:00.000Z","588":"2017-02-08T00:00:00.000Z","589":"2017-02-09T00:00:00.000Z","590":"2017-02-10T00:00:00.000Z","591":"2017-02-11T00:00:00.000Z","592":"2017-02-12T00:00:00.000Z","593":"2017-02-13T00:00:00.000Z","594":"2017-02-14T00:00:00.000Z","595":"2017-02-15T00:00:00.000Z","596":"2017-02-16T00:00:00.000Z","597":"2017-02-17T00:00:00.000Z","598":"2017-02-18T00:00:00.000Z","599":"2017-02-19T00:00:00.000Z","600":"2017-02-20T00:00:00.000Z","601":"2017-02-21T00:00:00.000Z","602":"2017-02-22T00:00:00.000Z","603":"2017-02-23T00:00:00.000Z","604":"2017-02-24T00:00:00.000Z","605":"2017-02-25T00:00:00.000Z","606":"2017-02-26T00:00:00.000Z","607":"2017-02-27T00:00:00.000Z","608":"2017-02-28T00:00:00.000Z","609":"2017-03-01T00:00:00.000Z"},"4337":{"490":93.0,"491":84.0,"492":84.0,"493":94.0,"494":104.0,"495":81.0,"496":58.0,"497":58.0,"498":93.0,"499":110.0,"500":118.0,"501":97.0,"502":89.0,"503":55.0,"504":67.0,"505":53.0,"506":86.0,"507":60.0,"508":69.0,"509":52.0,"510":72.0,"511":87.0,"512":62.0,"513":50.0,"514":81.0,"515":77.0,"516":92.0,"517":65.0,"518":56.0,"519":74.0,"520":49.0,"521":62.0,"522":80.0,"523":146.0,"524":77.0,"525":56.0,"526":60.0,"527":109.0,"528":49.0,"529":77.0,"530":891.0,"531":1055.0,"532":234.0,"533":87.0,"534":99.0,"535":59.0,"536":95.0,"537":83.0,"538":85.0,"539":65.0,"540":53.0,"541":52.0,"542":30.0,"543":66.0,"544":70.0,"545":82.0,"546":110.0,"547":112.0,"548":83.0,"549":26.0,"550":null,"551":null,"552":null,"553":null,"554":null,"555":null,"556":null,"557":null,"558":null,"559":null,"560":null,"561":null,"562":null,"563":null,"564":null,"565":null,"566":null,"567":null,"568":null,"569":null,"570":null,"571":null,"572":null,"573":null,"574":null,"575":null,"576":null,"577":null,"578":null,"579":null,"580":null,"581":null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'4338' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4338' 5.779028415679932 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4338']' 23.91645073890686 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4338' Length=550 Min=194.0 Max=1905.0 Mean=665.3127272727273 StdDev=298.8793378025474 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4338' Min=194.0 Max=1905.0 Mean=665.3127272727273 StdDev=298.8793378025474 @@ -4422,33 +4712,42 @@ INFO:pyaf.std:AUTOREG_DETAIL '_4338_Lag1Trend_residue_zeroCycle_residue_AR(16)' INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1519 MAPE_Forecast=0.1358 MAPE_Test=0.1525 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1467 SMAPE_Forecast=0.1346 SMAPE_Test=0.1508 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7581 MASE_Forecast=0.7859 MASE_Test=0.71 -INFO:pyaf.std:MODEL_L1 L1_Fit=99.38406099364563 L1_Forecast=70.75976550695553 L1_Test=108.6900126598815 +INFO:pyaf.std:MODEL_L1 L1_Fit=99.38406099364565 L1_Forecast=70.75976550695555 L1_Test=108.69001265988153 INFO:pyaf.std:MODEL_L2 L2_Fit=151.95909859330013 L2_Forecast=108.70468954274476 L2_Test=169.87399208567874 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 381.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4338_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4338_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.40386258797144786 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4338_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.377680484486693 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4338_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.3388494701738035 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4338_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.2970926379031067 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4338_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.22001438376730678 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4338_Lag1Trend_residue_zeroCycle_residue_Lag7 0.17270195641274988 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4338_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.16217448996720157 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4338_Lag1Trend_residue_zeroCycle_residue_Lag14 0.11843017693005663 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4338_Lag1Trend_residue_zeroCycle_residue_Lag15 0.10493980074616856 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4338_Lag1Trend_residue_zeroCycle_residue_Lag12 -0.07963749768143007 +INFO:pyaf.std:AR_MODEL_COEFF 1 _4338_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.4038625879714477 +INFO:pyaf.std:AR_MODEL_COEFF 2 _4338_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.3776804844866923 +INFO:pyaf.std:AR_MODEL_COEFF 3 _4338_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.3388494701738032 +INFO:pyaf.std:AR_MODEL_COEFF 4 _4338_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.29709263790310636 +INFO:pyaf.std:AR_MODEL_COEFF 5 _4338_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.2200143837673057 +INFO:pyaf.std:AR_MODEL_COEFF 6 _4338_Lag1Trend_residue_zeroCycle_residue_Lag7 0.17270195641275046 +INFO:pyaf.std:AR_MODEL_COEFF 7 _4338_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.16217448996720107 +INFO:pyaf.std:AR_MODEL_COEFF 8 _4338_Lag1Trend_residue_zeroCycle_residue_Lag14 0.11843017693005671 +INFO:pyaf.std:AR_MODEL_COEFF 9 _4338_Lag1Trend_residue_zeroCycle_residue_Lag15 0.1049398007461688 +INFO:pyaf.std:AR_MODEL_COEFF 10 _4338_Lag1Trend_residue_zeroCycle_residue_Lag12 -0.07963749768143054 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.8774468898773193 +INFO:pyaf.std:START_FORECASTING '['4338']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4338']' 5.619085788726807 Split Transformation ... ForecastMAPE TestMAPE 0 None _4338 ... 0.1358 0.1525 -1 None Anscombe_4338 ... 0.1368 0.1520 -2 None Anscombe_4338 ... 0.1415 0.1475 -3 None _4338 ... 0.1481 0.1510 -4 None Anscombe_4338 ... 0.1486 0.1551 +1 None _4338 ... 0.1358 0.1525 +2 None Anscombe_4338 ... 0.1368 0.1520 +3 None Anscombe_4338 ... 0.1368 0.1520 +4 None Anscombe_4338 ... 0.1415 0.1475 [5 rows x 8 columns] Forecast Columns Index(['Date', '4338', 'row_number', 'Date_Normalized', '_4338', @@ -4539,31 +4838,33 @@ Forecasts { - "Dataset": { - "Signal": "4338", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4338": { + "Dataset": { + "Signal": "4338", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4338_Lag1Trend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "70.75976550695553", - "MAPE": "0.1358", - "MASE": "0.7859", - "RMSE": "108.70468954274476" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_4338_Lag1Trend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "70.75976550695555", + "MAPE": "0.1358", + "MASE": "0.7859", + "RMSE": "108.70468954274476" + } } } @@ -4572,48 +4873,57 @@ Forecasts 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'4339' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4339']' 26.541166305541992 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4339' Length=550 Min=17.0 Max=9519.0 Mean=124.1509090909091 StdDev=603.2715090157023 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4339' Min=-4301.0 Max=9201.0 Mean=-0.02727272727272727 StdDev=628.0436154381016 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4339_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_4339_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_4339_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_4339_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3349 MAPE_Forecast=0.3719 MAPE_Test=0.3595 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.4199 SMAPE_Forecast=0.3745 SMAPE_Test=0.3054 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8938 MASE_Forecast=0.7824 MASE_Test=0.704 -INFO:pyaf.std:MODEL_L1 L1_Fit=118.63670085381091 L1_Forecast=29.56801853394419 L1_Test=13.412585034013599 -INFO:pyaf.std:MODEL_L2 L2_Fit=720.1058800678577 L2_Forecast=82.57600143340117 L2_Test=18.35398373596149 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4339' Min=17.0 Max=9519.0 Mean=124.1509090909091 StdDev=603.2715090157023 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4339_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [ConstantTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4339_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4339_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4339_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3518 MAPE_Forecast=0.3718 MAPE_Test=0.3403 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.412 SMAPE_Forecast=0.3706 SMAPE_Test=0.293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8889 MASE_Forecast=0.7757 MASE_Test=0.6776 +INFO:pyaf.std:MODEL_L1 L1_Fit=117.99234693877551 L1_Forecast=29.316326530612244 L1_Test=12.908333333333333 +INFO:pyaf.std:MODEL_L2 L2_Fit=719.3843509472215 L2_Forecast=81.619763012923 L2_Test=17.822387606603105 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 152.9719387755102 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4339_ConstantTrend_residue_Seasonal_DayOfWeek -107.47193877551021 {2: -103.97193877551021, 3: -107.97193877551021, 4: -110.97193877551021, 5: -115.47193877551021, 6: -110.47193877551021, 0: -105.97193877551021, 1: -103.97193877551021} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.077223777770996 +INFO:pyaf.std:START_FORECASTING '['4339']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4339']' 3.070399045944214 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4339 ... 0.3719 0.3595 -1 None _4339 ... 0.5077 0.4570 -2 None Anscombe_4339 ... 0.5077 0.4570 -3 None Diff_4339 ... 0.5077 0.4570 -4 None _4339 ... 0.5466 0.4324 +0 None _4339 ... 0.3718 0.3403 +1 None Anscombe_4339 ... 0.3718 0.3403 +2 None Diff_4339 ... 0.3719 0.3595 +3 None Diff_4339 ... 0.3719 0.3595 +4 None _4339 ... 0.3993 0.3702 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4339', 'row_number', 'Date_Normalized', 'Diff_4339', - 'Diff_4339_ConstantTrend', 'Diff_4339_ConstantTrend_residue', - 'Diff_4339_ConstantTrend_residue_zeroCycle', - 'Diff_4339_ConstantTrend_residue_zeroCycle_residue', - 'Diff_4339_ConstantTrend_residue_zeroCycle_residue_NoAR', - 'Diff_4339_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', - 'Diff_4339_Trend', 'Diff_4339_Trend_residue', 'Diff_4339_Cycle', - 'Diff_4339_Cycle_residue', 'Diff_4339_AR', 'Diff_4339_AR_residue', - 'Diff_4339_TransformedForecast', '4339_Forecast', - 'Diff_4339_TransformedResidue', '4339_Residue'], +Forecast Columns Index(['Date', '4339', 'row_number', 'Date_Normalized', '_4339', + '_4339_ConstantTrend', '_4339_ConstantTrend_residue', + '_4339_ConstantTrend_residue_Seasonal_DayOfWeek', + '_4339_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + '_4339_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4339_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4339_Trend', '_4339_Trend_residue', '_4339_Cycle', + '_4339_Cycle_residue', '_4339_AR', '_4339_AR_residue', + '_4339_TransformedForecast', '4339_Forecast', + '_4339_TransformedResidue', '4339_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -4628,95 +4938,97 @@ memory usage: 14.4 KB None Forecasts Date 4339 4339_Forecast -550 2017-01-01 NaN 45.028061 -551 2017-01-02 NaN 45.040816 -552 2017-01-03 NaN 45.053571 -553 2017-01-04 NaN 45.066327 -554 2017-01-05 NaN 45.079082 -555 2017-01-06 NaN 45.091837 -556 2017-01-07 NaN 45.104592 -557 2017-01-08 NaN 45.117347 -558 2017-01-09 NaN 45.130102 -559 2017-01-10 NaN 45.142857 -560 2017-01-11 NaN 45.155612 -561 2017-01-12 NaN 45.168367 -562 2017-01-13 NaN 45.181122 -563 2017-01-14 NaN 45.193878 -564 2017-01-15 NaN 45.206633 -565 2017-01-16 NaN 45.219388 -566 2017-01-17 NaN 45.232143 -567 2017-01-18 NaN 45.244898 -568 2017-01-19 NaN 45.257653 -569 2017-01-20 NaN 45.270408 -570 2017-01-21 NaN 45.283163 -571 2017-01-22 NaN 45.295918 -572 2017-01-23 NaN 45.308673 -573 2017-01-24 NaN 45.321429 -574 2017-01-25 NaN 45.334184 -575 2017-01-26 NaN 45.346939 -576 2017-01-27 NaN 45.359694 -577 2017-01-28 NaN 45.372449 -578 2017-01-29 NaN 45.385204 -579 2017-01-30 NaN 45.397959 -580 2017-01-31 NaN 45.410714 -581 2017-02-01 NaN 45.423469 -582 2017-02-02 NaN 45.436224 -583 2017-02-03 NaN 45.448980 -584 2017-02-04 NaN 45.461735 -585 2017-02-05 NaN 45.474490 -586 2017-02-06 NaN 45.487245 -587 2017-02-07 NaN 45.500000 -588 2017-02-08 NaN 45.512755 -589 2017-02-09 NaN 45.525510 -590 2017-02-10 NaN 45.538265 -591 2017-02-11 NaN 45.551020 -592 2017-02-12 NaN 45.563776 -593 2017-02-13 NaN 45.576531 -594 2017-02-14 NaN 45.589286 -595 2017-02-15 NaN 45.602041 -596 2017-02-16 NaN 45.614796 -597 2017-02-17 NaN 45.627551 -598 2017-02-18 NaN 45.640306 -599 2017-02-19 NaN 45.653061 -600 2017-02-20 NaN 45.665816 -601 2017-02-21 NaN 45.678571 -602 2017-02-22 NaN 45.691327 -603 2017-02-23 NaN 45.704082 -604 2017-02-24 NaN 45.716837 -605 2017-02-25 NaN 45.729592 -606 2017-02-26 NaN 45.742347 -607 2017-02-27 NaN 45.755102 -608 2017-02-28 NaN 45.767857 -609 2017-03-01 NaN 45.780612 +550 2017-01-01 NaN 42.5 +551 2017-01-02 NaN 47.0 +552 2017-01-03 NaN 49.0 +553 2017-01-04 NaN 49.0 +554 2017-01-05 NaN 45.0 +555 2017-01-06 NaN 42.0 +556 2017-01-07 NaN 37.5 +557 2017-01-08 NaN 42.5 +558 2017-01-09 NaN 47.0 +559 2017-01-10 NaN 49.0 +560 2017-01-11 NaN 49.0 +561 2017-01-12 NaN 45.0 +562 2017-01-13 NaN 42.0 +563 2017-01-14 NaN 37.5 +564 2017-01-15 NaN 42.5 +565 2017-01-16 NaN 47.0 +566 2017-01-17 NaN 49.0 +567 2017-01-18 NaN 49.0 +568 2017-01-19 NaN 45.0 +569 2017-01-20 NaN 42.0 +570 2017-01-21 NaN 37.5 +571 2017-01-22 NaN 42.5 +572 2017-01-23 NaN 47.0 +573 2017-01-24 NaN 49.0 +574 2017-01-25 NaN 49.0 +575 2017-01-26 NaN 45.0 +576 2017-01-27 NaN 42.0 +577 2017-01-28 NaN 37.5 +578 2017-01-29 NaN 42.5 +579 2017-01-30 NaN 47.0 +580 2017-01-31 NaN 49.0 +581 2017-02-01 NaN 49.0 +582 2017-02-02 NaN 45.0 +583 2017-02-03 NaN 42.0 +584 2017-02-04 NaN 37.5 +585 2017-02-05 NaN 42.5 +586 2017-02-06 NaN 47.0 +587 2017-02-07 NaN 49.0 +588 2017-02-08 NaN 49.0 +589 2017-02-09 NaN 45.0 +590 2017-02-10 NaN 42.0 +591 2017-02-11 NaN 37.5 +592 2017-02-12 NaN 42.5 +593 2017-02-13 NaN 47.0 +594 2017-02-14 NaN 49.0 +595 2017-02-15 NaN 49.0 +596 2017-02-16 NaN 45.0 +597 2017-02-17 NaN 42.0 +598 2017-02-18 NaN 37.5 +599 2017-02-19 NaN 42.5 +600 2017-02-20 NaN 47.0 +601 2017-02-21 NaN 49.0 +602 2017-02-22 NaN 49.0 +603 2017-02-23 NaN 45.0 +604 2017-02-24 NaN 42.0 +605 2017-02-25 NaN 37.5 +606 2017-02-26 NaN 42.5 +607 2017-02-27 NaN 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'4340' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4340']' 22.48520064353943 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4340' Length=550 Min=15.0 Max=43280.0 Mean=183.04363636363635 StdDev=2014.0958553305015 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4340' Min=-24220.0 Max=42364.0 Mean=-0.0018181818181818182 StdDev=2202.42721105527 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4340_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_4340_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_4340_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_4340_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.385 MAPE_Forecast=0.3336 MAPE_Test=0.4039 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.5358 SMAPE_Forecast=0.4303 SMAPE_Test=0.5515 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8602 MASE_Forecast=1.0234 MASE_Test=1.2913 -INFO:pyaf.std:MODEL_L1 L1_Fit=210.56290347771764 L1_Forecast=20.689842773844237 L1_Test=23.966411564625837 -INFO:pyaf.std:MODEL_L2 L2_Fit=2392.66414636259 L2_Forecast=44.39799425212851 L2_Test=33.50457265878742 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4340' Min=15.0 Max=43280.0 Mean=183.04363636363635 StdDev=2014.0958553305015 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4340_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [ConstantTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4340_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4340_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4340_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3274 MAPE_Forecast=0.2841 MAPE_Test=0.303 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3831 SMAPE_Forecast=0.2684 SMAPE_Test=0.3107 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8347 MASE_Forecast=0.7753 MASE_Test=0.8567 +INFO:pyaf.std:MODEL_L1 L1_Fit=204.3137755102041 L1_Forecast=15.673469387755102 L1_Test=15.9 +INFO:pyaf.std:MODEL_L2 L2_Fit=2391.426156339453 L2_Forecast=39.529696922383685 L2_Test=23.769728648009426 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 238.07908163265307 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4340_ConstantTrend_residue_Seasonal_DayOfWeek -195.07908163265307 {2: -194.57908163265307, 3: -196.57908163265307, 4: -195.57908163265307, 5: -202.57908163265307, 6: -197.07908163265307, 0: -192.07908163265307, 1: -193.57908163265307} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.3727190494537354 +INFO:pyaf.std:START_FORECASTING '['4340']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4340']' 2.6329731941223145 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4340 ... 0.3336 0.4039 -1 None _4340 ... 0.3570 0.3550 -2 None Anscombe_4340 ... 0.3911 0.3890 -3 None _4340 ... 0.3997 0.4019 -4 None Anscombe_4340 ... 0.3997 0.4019 +0 None _4340 ... 0.2841 0.3030 +1 None Anscombe_4340 ... 0.2841 0.3030 +2 None _4340 ... 0.3098 0.3213 +3 None Anscombe_4340 ... 0.3098 0.3213 +4 None Diff_4340 ... 0.3336 0.4039 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4340', 'row_number', 'Date_Normalized', 'Diff_4340', - 'Diff_4340_ConstantTrend', 'Diff_4340_ConstantTrend_residue', - 'Diff_4340_ConstantTrend_residue_zeroCycle', - 'Diff_4340_ConstantTrend_residue_zeroCycle_residue', - 'Diff_4340_ConstantTrend_residue_zeroCycle_residue_NoAR', - 'Diff_4340_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', - 'Diff_4340_Trend', 'Diff_4340_Trend_residue', 'Diff_4340_Cycle', - 'Diff_4340_Cycle_residue', 'Diff_4340_AR', 'Diff_4340_AR_residue', - 'Diff_4340_TransformedForecast', '4340_Forecast', - 'Diff_4340_TransformedResidue', '4340_Residue'], +Forecast Columns Index(['Date', '4340', 'row_number', 'Date_Normalized', '_4340', + '_4340_ConstantTrend', '_4340_ConstantTrend_residue', + '_4340_ConstantTrend_residue_Seasonal_DayOfWeek', + '_4340_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + '_4340_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4340_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4340_Trend', '_4340_Trend_residue', '_4340_Cycle', + '_4340_Cycle_residue', '_4340_AR', '_4340_AR_residue', + '_4340_TransformedForecast', '4340_Forecast', + '_4340_TransformedResidue', '4340_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -4781,95 +5102,97 @@ memory usage: 14.4 KB None Forecasts Date 4340 4340_Forecast -550 2017-01-01 NaN 23.971939 -551 2017-01-02 NaN 23.959184 -552 2017-01-03 NaN 23.946429 -553 2017-01-04 NaN 23.933673 -554 2017-01-05 NaN 23.920918 -555 2017-01-06 NaN 23.908163 -556 2017-01-07 NaN 23.895408 -557 2017-01-08 NaN 23.882653 -558 2017-01-09 NaN 23.869898 -559 2017-01-10 NaN 23.857143 -560 2017-01-11 NaN 23.844388 -561 2017-01-12 NaN 23.831633 -562 2017-01-13 NaN 23.818878 -563 2017-01-14 NaN 23.806122 -564 2017-01-15 NaN 23.793367 -565 2017-01-16 NaN 23.780612 -566 2017-01-17 NaN 23.767857 -567 2017-01-18 NaN 23.755102 -568 2017-01-19 NaN 23.742347 -569 2017-01-20 NaN 23.729592 -570 2017-01-21 NaN 23.716837 -571 2017-01-22 NaN 23.704082 -572 2017-01-23 NaN 23.691327 -573 2017-01-24 NaN 23.678571 -574 2017-01-25 NaN 23.665816 -575 2017-01-26 NaN 23.653061 -576 2017-01-27 NaN 23.640306 -577 2017-01-28 NaN 23.627551 -578 2017-01-29 NaN 23.614796 -579 2017-01-30 NaN 23.602041 -580 2017-01-31 NaN 23.589286 -581 2017-02-01 NaN 23.576531 -582 2017-02-02 NaN 23.563776 -583 2017-02-03 NaN 23.551020 -584 2017-02-04 NaN 23.538265 -585 2017-02-05 NaN 23.525510 -586 2017-02-06 NaN 23.512755 -587 2017-02-07 NaN 23.500000 -588 2017-02-08 NaN 23.487245 -589 2017-02-09 NaN 23.474490 -590 2017-02-10 NaN 23.461735 -591 2017-02-11 NaN 23.448980 -592 2017-02-12 NaN 23.436224 -593 2017-02-13 NaN 23.423469 -594 2017-02-14 NaN 23.410714 -595 2017-02-15 NaN 23.397959 -596 2017-02-16 NaN 23.385204 -597 2017-02-17 NaN 23.372449 -598 2017-02-18 NaN 23.359694 -599 2017-02-19 NaN 23.346939 -600 2017-02-20 NaN 23.334184 -601 2017-02-21 NaN 23.321429 -602 2017-02-22 NaN 23.308673 -603 2017-02-23 NaN 23.295918 -604 2017-02-24 NaN 23.283163 -605 2017-02-25 NaN 23.270408 -606 2017-02-26 NaN 23.257653 -607 2017-02-27 NaN 23.244898 -608 2017-02-28 NaN 23.232143 -609 2017-03-01 NaN 23.219388 +550 2017-01-01 NaN 41.0 +551 2017-01-02 NaN 46.0 +552 2017-01-03 NaN 44.5 +553 2017-01-04 NaN 43.5 +554 2017-01-05 NaN 41.5 +555 2017-01-06 NaN 42.5 +556 2017-01-07 NaN 35.5 +557 2017-01-08 NaN 41.0 +558 2017-01-09 NaN 46.0 +559 2017-01-10 NaN 44.5 +560 2017-01-11 NaN 43.5 +561 2017-01-12 NaN 41.5 +562 2017-01-13 NaN 42.5 +563 2017-01-14 NaN 35.5 +564 2017-01-15 NaN 41.0 +565 2017-01-16 NaN 46.0 +566 2017-01-17 NaN 44.5 +567 2017-01-18 NaN 43.5 +568 2017-01-19 NaN 41.5 +569 2017-01-20 NaN 42.5 +570 2017-01-21 NaN 35.5 +571 2017-01-22 NaN 41.0 +572 2017-01-23 NaN 46.0 +573 2017-01-24 NaN 44.5 +574 2017-01-25 NaN 43.5 +575 2017-01-26 NaN 41.5 +576 2017-01-27 NaN 42.5 +577 2017-01-28 NaN 35.5 +578 2017-01-29 NaN 41.0 +579 2017-01-30 NaN 46.0 +580 2017-01-31 NaN 44.5 +581 2017-02-01 NaN 43.5 +582 2017-02-02 NaN 41.5 +583 2017-02-03 NaN 42.5 +584 2017-02-04 NaN 35.5 +585 2017-02-05 NaN 41.0 +586 2017-02-06 NaN 46.0 +587 2017-02-07 NaN 44.5 +588 2017-02-08 NaN 43.5 +589 2017-02-09 NaN 41.5 +590 2017-02-10 NaN 42.5 +591 2017-02-11 NaN 35.5 +592 2017-02-12 NaN 41.0 +593 2017-02-13 NaN 46.0 +594 2017-02-14 NaN 44.5 +595 2017-02-15 NaN 43.5 +596 2017-02-16 NaN 41.5 +597 2017-02-17 NaN 42.5 +598 2017-02-18 NaN 35.5 +599 2017-02-19 NaN 41.0 +600 2017-02-20 NaN 46.0 +601 2017-02-21 NaN 44.5 +602 2017-02-22 NaN 43.5 +603 2017-02-23 NaN 41.5 +604 2017-02-24 NaN 42.5 +605 2017-02-25 NaN 35.5 +606 2017-02-26 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'4341' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4341']' 21.212392807006836 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4341' Length=550 Min=0.0 Max=36225.0 Mean=455.3818181818182 StdDev=2377.1258068279863 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4341' Min=0.0 Max=36225.0 Mean=455.3818181818182 StdDev=2377.1258068279863 @@ -4894,20 +5217,29 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.99 MASE_Test=0.9838 INFO:pyaf.std:MODEL_L1 L1_Fit=137.2908163265306 L1_Forecast=418.94897959183675 L1_Test=1451.9666666666667 INFO:pyaf.std:MODEL_L2 L2_Fit=1015.5907600193765 L2_Forecast=1830.781524321217 L2_Test=5569.322819397944 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 0.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4341_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.290820598602295 +INFO:pyaf.std:START_FORECASTING '['4341']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4341']' 2.489309072494507 Split Transformation ... ForecastMAPE TestMAPE -0 None _4341 ... 0.4010 0.5479 -1 None Anscombe_4341 ... 0.4010 0.5479 -2 None Diff_4341 ... 0.4010 0.5479 -3 None Anscombe_4341 ... 0.4235 0.5601 -4 None Diff_4341 ... 0.6467 0.7254 +0 None _4341 ... 0.401 0.5479 +1 None _4341 ... 0.401 0.5479 +2 None _4341 ... 0.401 0.5479 +3 None _4341 ... 0.401 0.5479 +4 None _4341 ... 0.401 0.5479 [5 rows x 8 columns] Forecast Columns Index(['Date', '4341', 'row_number', 'Date_Normalized', '_4341', @@ -4997,31 +5329,33 @@ Forecasts { - "Dataset": { - "Signal": "4341", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4341": { + "Dataset": { + "Signal": "4341", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_4341_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "418.94897959183675", - "MAPE": "0.401", - "MASE": "0.99", - "RMSE": "1830.781524321217" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4341_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "418.94897959183675", + "MAPE": "0.401", + "MASE": "0.99", + "RMSE": "1830.781524321217" + } } } @@ -5031,53 +5365,62 @@ Forecasts 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'4342' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4342' 4.627288818359375 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4342']' 16.811508893966675 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4342' Length=550 Min=43.0 Max=6155.0 Mean=223.56363636363636 StdDev=464.6495713247042 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4342' Min=1.224744871391589 Max=2.345207879911715 Mean=1.268647711622966 StdDev=0.09328935030302783 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4342_LinearTrend_residue_zeroCycle_residue_AR(16)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(16)' [LinearTrend + Seasonal_WeekOfMonth + AR] INFO:pyaf.std:TREND_DETAIL 'Anscombe_4342_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4342_LinearTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4342_LinearTrend_residue_zeroCycle_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3303 MAPE_Forecast=0.3474 MAPE_Test=0.3254 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2831 SMAPE_Forecast=0.3284 SMAPE_Test=0.33 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9015 MASE_Forecast=0.7517 MASE_Test=0.8595 -INFO:pyaf.std:MODEL_L1 L1_Fit=65.56543163656623 L1_Forecast=160.51469559853834 L1_Test=119.54165094947231 -INFO:pyaf.std:MODEL_L2 L2_Fit=293.46653060982965 L2_Forecast=649.097281899607 L2_Test=324.04349665449143 -INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth' [Seasonal_WeekOfMonth] +INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(16)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3193 MAPE_Forecast=0.3356 MAPE_Test=0.3171 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2759 SMAPE_Forecast=0.322 SMAPE_Test=0.3281 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8916 MASE_Forecast=0.746 MASE_Test=0.8572 +INFO:pyaf.std:MODEL_L1 L1_Fit=64.84240527951059 L1_Forecast=159.2910853714394 L1_Test=119.22260501041092 +INFO:pyaf.std:MODEL_L2 L2_Fit=294.07140490677057 L2_Forecast=649.4769592319788 L2_Test=324.6545351346751 +INFO:pyaf.std:MODEL_COMPLEXITY 68 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.268507179917283, array([-0.01552538])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth -0.011440115793472705 {1: -0.012556381527345839, 2: -0.012905499228557993, 3: -0.011551502818377801, 4: -0.014994570238134552, 5: -0.009462811388396553, 6: 0.009841914861203338, -51: 0.0046963229357999126, -50: -0.004927810415557676, -49: -0.0024083201166771318, -48: 0.043560855999617676} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag1 0.4751377712119672 -INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag16 0.05376993674163788 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag4 0.04982987534194936 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag3 0.02796568230378173 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag5 0.02577922359141691 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag2 -0.017958996235566713 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag7 0.01093161779800465 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag15 -0.00854209979055545 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag8 0.006637833925429441 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4342_LinearTrend_residue_zeroCycle_residue_Lag10 -0.005772008509301754 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag1 0.4717041554225847 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag16 0.052404052156203834 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag4 0.04687921688820145 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag3 0.028608436794350014 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag5 0.02584479614082376 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag2 -0.021130289278051634 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag7 0.008675903679531242 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag8 0.007394810603228037 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag15 -0.007200783919744528 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_Lag6 -0.0070664626083369415 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.4610483646392822 +INFO:pyaf.std:START_FORECASTING '['4342']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4342']' 4.073855400085449 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4342 ... 0.3474 0.3254 -1 None Anscombe_4342 ... 0.3563 0.2831 -2 None _4342 ... 0.3716 0.4189 -3 None Anscombe_4342 ... 0.3716 0.4189 -4 None Diff_4342 ... 0.3716 0.4189 +0 None Anscombe_4342 ... 0.3356 0.3171 +1 None Anscombe_4342 ... 0.3402 0.2878 +2 None Anscombe_4342 ... 0.3474 0.3254 +3 None Anscombe_4342 ... 0.3474 0.3254 +4 None Anscombe_4342 ... 0.3567 0.3391 [5 rows x 8 columns] Forecast Columns Index(['Date', '4342', 'row_number', 'Date_Normalized', 'Anscombe_4342', 'Anscombe_4342_LinearTrend', 'Anscombe_4342_LinearTrend_residue', - 'Anscombe_4342_LinearTrend_residue_zeroCycle', - 'Anscombe_4342_LinearTrend_residue_zeroCycle_residue', - 'Anscombe_4342_LinearTrend_residue_zeroCycle_residue_AR(16)', - 'Anscombe_4342_LinearTrend_residue_zeroCycle_residue_AR(16)_residue', + 'Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth', + 'Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue', + 'Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(16)', + 'Anscombe_4342_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(16)_residue', 'Anscombe_4342_Trend', 'Anscombe_4342_Trend_residue', 'Anscombe_4342_Cycle', 'Anscombe_4342_Cycle_residue', 'Anscombe_4342_AR', 'Anscombe_4342_AR_residue', @@ -5097,95 +5440,97 @@ memory usage: 14.4 KB None Forecasts Date 4342 4342_Forecast -550 2017-01-01 NaN 154.190309 -551 2017-01-02 NaN 168.012065 -552 2017-01-03 NaN 161.903220 -553 2017-01-04 NaN 159.079074 -554 2017-01-05 NaN 149.659123 -555 2017-01-06 NaN 144.058969 -556 2017-01-07 NaN 134.349897 -557 2017-01-08 NaN 127.798598 -558 2017-01-09 NaN 124.717306 -559 2017-01-10 NaN 121.416834 -560 2017-01-11 NaN 119.642726 -561 2017-01-12 NaN 118.128096 -562 2017-01-13 NaN 127.824337 -563 2017-01-14 NaN 145.479292 -564 2017-01-15 NaN 143.602453 -565 2017-01-16 NaN 132.610247 -566 2017-01-17 NaN 128.882579 -567 2017-01-18 NaN 129.201912 -568 2017-01-19 NaN 129.146341 -569 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'4343' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4343']' 18.596692085266113 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4343' Length=550 Min=5.0 Max=22943.0 Mean=140.33272727272728 StdDev=1064.2265310038845 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4343' Min=5.0 Max=22943.0 Mean=140.33272727272728 StdDev=1064.2265310038845 @@ -5207,33 +5552,42 @@ INFO:pyaf.std:AUTOREG_DETAIL '_4343_Lag1Trend_residue_zeroCycle_residue_AR(16)' INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.7854 MAPE_Forecast=0.4525 MAPE_Test=0.2805 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.4016 SMAPE_Forecast=0.3437 SMAPE_Test=0.2715 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9581 MASE_Forecast=1.0295 MASE_Test=0.8517 -INFO:pyaf.std:MODEL_L1 L1_Fit=186.43210029161105 L1_Forecast=10.348306993595934 L1_Test=6.424138657613691 -INFO:pyaf.std:MODEL_L2 L2_Fit=1277.9166752676194 L2_Forecast=24.209585885097134 L2_Test=8.655469721264556 +INFO:pyaf.std:MODEL_L1 L1_Fit=186.43210029161108 L1_Forecast=10.348306993595939 L1_Test=6.4241386576136925 +INFO:pyaf.std:MODEL_L2 L2_Fit=1277.9166752676194 L2_Forecast=24.209585885097137 L2_Test=8.655469721264557 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 68.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4343_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4343_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.8102509308067859 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4343_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.7509846264225828 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4343_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.6944683584168565 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4343_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.6370766531312659 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4343_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.5840783987222404 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4343_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.5323217322868903 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4343_Lag1Trend_residue_zeroCycle_residue_Lag7 -0.4542820138318799 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4343_Lag1Trend_residue_zeroCycle_residue_Lag8 -0.41278150802231567 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4343_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.37079733678074395 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4343_Lag1Trend_residue_zeroCycle_residue_Lag10 -0.3260212720991018 +INFO:pyaf.std:AR_MODEL_COEFF 1 _4343_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.8102509308067861 +INFO:pyaf.std:AR_MODEL_COEFF 2 _4343_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.7509846264225839 +INFO:pyaf.std:AR_MODEL_COEFF 3 _4343_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.6944683584168578 +INFO:pyaf.std:AR_MODEL_COEFF 4 _4343_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.6370766531312668 +INFO:pyaf.std:AR_MODEL_COEFF 5 _4343_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.5840783987222418 +INFO:pyaf.std:AR_MODEL_COEFF 6 _4343_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.5323217322868917 +INFO:pyaf.std:AR_MODEL_COEFF 7 _4343_Lag1Trend_residue_zeroCycle_residue_Lag7 -0.4542820138318814 +INFO:pyaf.std:AR_MODEL_COEFF 8 _4343_Lag1Trend_residue_zeroCycle_residue_Lag8 -0.4127815080223157 +INFO:pyaf.std:AR_MODEL_COEFF 9 _4343_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.370797336780744 +INFO:pyaf.std:AR_MODEL_COEFF 10 _4343_Lag1Trend_residue_zeroCycle_residue_Lag10 -0.3260212720991023 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.8766968250274658 +INFO:pyaf.std:START_FORECASTING '['4343']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4343']' 5.023519992828369 Split Transformation ... ForecastMAPE TestMAPE 0 None _4343 ... 0.4525 0.2805 -1 None Anscombe_4343 ... 0.4548 0.2868 -2 None _4343 ... 0.4787 0.3319 -3 None Anscombe_4343 ... 0.4787 0.3319 -4 None Diff_4343 ... 0.4787 0.3319 +1 None _4343 ... 0.4525 0.2805 +2 None Anscombe_4343 ... 0.4548 0.2868 +3 None Anscombe_4343 ... 0.4548 0.2868 +4 None Anscombe_4343 ... 0.4746 0.3241 [5 rows x 8 columns] Forecast Columns Index(['Date', '4343', 'row_number', 'Date_Normalized', '_4343', @@ -5324,31 +5678,33 @@ Forecasts { - "Dataset": { - "Signal": "4343", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4343": { + "Dataset": { + "Signal": "4343", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_4343_Lag1Trend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4343_Lag1Trend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "10.348306993595934", - "MAPE": "0.4525", - "MASE": "1.0295", - "RMSE": "24.209585885097134" + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "10.348306993595939", + "MAPE": "0.4525", + "MASE": "1.0295", + "RMSE": "24.209585885097137" + } } } @@ -5358,46 +5714,57 @@ Forecasts {"Date":{"490":"2016-11-02T00:00:00.000Z","491":"2016-11-03T00:00:00.000Z","492":"2016-11-04T00:00:00.000Z","493":"2016-11-05T00:00:00.000Z","494":"2016-11-06T00:00:00.000Z","495":"2016-11-07T00:00:00.000Z","496":"2016-11-08T00:00:00.000Z","497":"2016-11-09T00:00:00.000Z","498":"2016-11-10T00:00:00.000Z","499":"2016-11-11T00:00:00.000Z","500":"2016-11-12T00:00:00.000Z","501":"2016-11-13T00:00:00.000Z","502":"2016-11-14T00:00:00.000Z","503":"2016-11-15T00:00:00.000Z","504":"2016-11-16T00:00:00.000Z","505":"2016-11-17T00:00:00.000Z","506":"2016-11-18T00:00:00.000Z","507":"2016-11-19T00:00:00.000Z","508":"2016-11-20T00:00:00.000Z","509":"2016-11-21T00:00:00.000Z","510":"2016-11-22T00:00:00.000Z","511":"2016-11-23T00:00:00.000Z","512":"2016-11-24T00:00:00.000Z","513":"2016-11-25T00:00:00.000Z","514":"2016-11-26T00:00:00.000Z","515":"2016-11-27T00:00:00.000Z","516":"2016-11-28T00:00:00.000Z","517":"2016-11-29T00:00:00.000Z","518":"2016-11-30T00:00:00.000Z","519":"2016-12-01T00:00:00.000Z","520":"2016-12-02T00:00:00.000Z","521":"2016-12-03T00:00:00.000Z","522":"2016-12-04T00:00:00.000Z","523":"2016-12-05T00:00:00.000Z","524":"2016-12-06T00:00:00.000Z","525":"2016-12-07T00:00:00.000Z","526":"2016-12-08T00:00:00.000Z","527":"2016-12-09T00:00:00.000Z","528":"2016-12-10T00:00:00.000Z","529":"2016-12-11T00:00:00.000Z","530":"2016-12-12T00:00:00.000Z","531":"2016-12-13T00:00:00.000Z","532":"2016-12-14T00:00:00.000Z","533":"2016-12-15T00:00:00.000Z","534":"2016-12-16T00:00:00.000Z","535":"2016-12-17T00:00:00.000Z","536":"2016-12-18T00:00:00.000Z","537":"2016-12-19T00:00:00.000Z","538":"2016-12-20T00:00:00.000Z","539":"2016-12-21T00:00:00.000Z","540":"2016-12-22T00:00:00.000Z","541":"2016-12-23T00:00:00.000Z","542":"2016-12-24T00:00:00.000Z","543":"2016-12-25T00:00:00.000Z","544":"2016-12-26T00:00:00.000Z","545":"2016-12-27T00:00:00.000Z","546":"2016-12-28T00:00:00.000Z","547":"2016-12-29T00:00:00.000Z","548":"2016-12-30T00:00:00.000Z","549":"2016-12-31T00:00:00.000Z","550":"2017-01-01T00:00:00.000Z","551":"2017-01-02T00:00:00.000Z","552":"2017-01-03T00:00:00.000Z","553":"2017-01-04T00:00:00.000Z","554":"2017-01-05T00:00:00.000Z","555":"2017-01-06T00:00:00.000Z","556":"2017-01-07T00:00:00.000Z","557":"2017-01-08T00:00:00.000Z","558":"2017-01-09T00:00:00.000Z","559":"2017-01-10T00:00:00.000Z","560":"2017-01-11T00:00:00.000Z","561":"2017-01-12T00:00:00.000Z","562":"2017-01-13T00:00:00.000Z","563":"2017-01-14T00:00:00.000Z","564":"2017-01-15T00:00:00.000Z","565":"2017-01-16T00:00:00.000Z","566":"2017-01-17T00:00:00.000Z","567":"2017-01-18T00:00:00.000Z","568":"2017-01-19T00:00:00.000Z","569":"2017-01-20T00:00:00.000Z","570":"2017-01-21T00:00:00.000Z","571":"2017-01-22T00:00:00.000Z","572":"2017-01-23T00:00:00.000Z","573":"2017-01-24T00:00:00.000Z","574":"2017-01-25T00:00:00.000Z","575":"2017-01-26T00:00:00.000Z","576":"2017-01-27T00:00:00.000Z","577":"2017-01-28T00:00:00.000Z","578":"2017-01-29T00:00:00.000Z","579":"2017-01-30T00:00:00.000Z","580":"2017-01-31T00:00:00.000Z","581":"2017-02-01T00:00:00.000Z","582":"2017-02-02T00:00:00.000Z","583":"2017-02-03T00:00:00.000Z","584":"2017-02-04T00:00:00.000Z","585":"2017-02-05T00:00:00.000Z","586":"2017-02-06T00:00:00.000Z","587":"2017-02-07T00:00:00.000Z","588":"2017-02-08T00:00:00.000Z","589":"2017-02-09T00:00:00.000Z","590":"2017-02-10T00:00:00.000Z","591":"2017-02-11T00:00:00.000Z","592":"2017-02-12T00:00:00.000Z","593":"2017-02-13T00:00:00.000Z","594":"2017-02-14T00:00:00.000Z","595":"2017-02-15T00:00:00.000Z","596":"2017-02-16T00:00:00.000Z","597":"2017-02-17T00:00:00.000Z","598":"2017-02-18T00:00:00.000Z","599":"2017-02-19T00:00:00.000Z","600":"2017-02-20T00:00:00.000Z","601":"2017-02-21T00:00:00.000Z","602":"2017-02-22T00:00:00.000Z","603":"2017-02-23T00:00:00.000Z","604":"2017-02-24T00:00:00.000Z","605":"2017-02-25T00:00:00.000Z","606":"2017-02-26T00:00:00.000Z","607":"2017-02-27T00:00:00.000Z","608":"2017-02-28T00:00:00.000Z","609":"2017-03-01T00:00:00.000Z"},"4343":{"490":25.0,"491":22.0,"492":15.0,"493":15.0,"494":29.0,"495":17.0,"496":25.0,"497":20.0,"498":14.0,"499":19.0,"500":14.0,"501":24.0,"502":23.0,"503":24.0,"504":21.0,"505":21.0,"506":40.0,"507":29.0,"508":28.0,"509":28.0,"510":39.0,"511":27.0,"512":34.0,"513":20.0,"514":15.0,"515":32.0,"516":22.0,"517":28.0,"518":28.0,"519":26.0,"520":18.0,"521":28.0,"522":27.0,"523":30.0,"524":20.0,"525":29.0,"526":33.0,"527":33.0,"528":21.0,"529":18.0,"530":30.0,"531":27.0,"532":22.0,"533":35.0,"534":11.0,"535":27.0,"536":15.0,"537":19.0,"538":17.0,"539":19.0,"540":13.0,"541":14.0,"542":12.0,"543":16.0,"544":15.0,"545":17.0,"546":56.0,"547":41.0,"548":29.0,"549":14.0,"550":null,"551":null,"552":null,"553":null,"554":null,"555":null,"556":null,"557":null,"558":null,"559":null,"560":null,"561":null,"562":null,"563":null,"564":null,"565":null,"566":null,"567":null,"568":null,"569":null,"570":null,"571":null,"572":null,"573":null,"574":null,"575":null,"576":null,"577":null,"578":null,"579":null,"580":null,"581":null,"582":null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'4344' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4344' 4.411564111709595 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4344']' 25.450669288635254 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4344' Length=550 Min=349.0 Max=14934.0 Mean=908.0654545454546 StdDev=818.5770443482301 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4344' Min=349.0 Max=14934.0 Mean=908.0654545454546 StdDev=818.5770443482301 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_4344_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_4344_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_4344_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_4344_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2269 MAPE_Forecast=0.2445 MAPE_Test=0.1783 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.182 SMAPE_Forecast=0.2117 SMAPE_Test=0.1659 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.9904 MASE_Test=1.0031 -INFO:pyaf.std:MODEL_L1 L1_Fit=232.6173469387755 L1_Forecast=301.8265306122449 L1_Test=154.83333333333334 -INFO:pyaf.std:MODEL_L2 L2_Fit=1061.718932597858 L2_Forecast=714.5094720955669 L2_Test=278.35486942630143 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4344' Min=1.224744871391589 Max=2.345207879911715 Mean=1.2839308946380574 StdDev=0.06962472505106991 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4344_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [ConstantTrend + Seasonal_WeekOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL 'Anscombe_4344_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4344_ConstantTrend_residue_Seasonal_WeekOfYear' [Seasonal_WeekOfYear] +INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4344_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1418 MAPE_Forecast=0.1985 MAPE_Test=0.1717 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1416 SMAPE_Forecast=0.2433 SMAPE_Test=0.1684 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6727 MASE_Forecast=1.3126 MASE_Test=0.9721 +INFO:pyaf.std:MODEL_L1 L1_Fit=156.8877551020409 L1_Forecast=399.9897959183673 L1_Test=150.04999999999987 +INFO:pyaf.std:MODEL_L2 L2_Fit=763.0967104683511 L2_Forecast=1160.2876182838706 L2_Test=246.66539414086154 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.2812936104231647 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Anscombe_4344_ConstantTrend_residue_Seasonal_WeekOfYear -0.002594954833512575 {27: -0.033917599441837476, 28: -0.035237270111601715, 29: -0.03534746005347589, 30: -0.028432640396571784, 31: -0.03732978210229132, 32: -0.044737520583937984, 33: -0.03799136239771017, 34: -0.03578746135615596, 35: -0.030075486093102066, 36: -0.023844091040778226, 37: -0.016884035350888782, 38: -0.008452923429337345, 39: -0.002970338561035568, 40: 0.014925939537557387, 41: 0.011536224408414286, 42: 0.003129562260687946, 43: 0.0004577411478130511, 44: 0.0014202378658405568, 45: 0.004410064215125464, 46: 0.006008900466306866, 47: 0.009944232217265592, 48: 0.033204538012971074, 49: 0.0020615013124622994, 50: -0.00630008028840412, 51: -0.010825264376526977, 52: -0.03314791689775465, 53: -0.021447234885482835, 1: 0.002382012906479547, 2: 0.013126258628057608, 3: 0.00387667678540482, 4: 0.00590237316729425, 5: 0.0019546463249342505, 6: -0.003614127470290107, 7: 0.020942013741422683, 8: 0.006115418950665363, 9: 0.021047310846785017, 10: 0.0218893814017338, 11: 0.008881812748979812, 12: 0.0140793423332779, 13: 0.01217247264829413, 14: 0.013020317116693825, 15: 0.006647879229184417, 16: 0.011217982839294427, 17: 0.012384485858162986, 18: -0.005117564587121537, 19: 0.039552670893741926, 20: 0.03101200964120121, 21: 0.003449807400036642, 22: 0.014502710075030345, 23: 0.00302279613571943, 24: 0.02125787952113667, 25: -0.00866840804618052, 26: -0.014175647166515315} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.1023435592651367 +INFO:pyaf.std:START_FORECASTING '['4344']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4344']' 3.1518523693084717 Split Transformation ... ForecastMAPE TestMAPE -0 None _4344 ... 0.2445 0.1783 -1 None Anscombe_4344 ... 0.2445 0.1783 -2 None Diff_4344 ... 0.2445 0.1783 -3 None Anscombe_4344 ... 0.2493 0.1954 -4 None Anscombe_4344 ... 0.2493 0.1954 +0 None Anscombe_4344 ... 0.1985 0.1717 +1 None Anscombe_4344 ... 0.2138 0.1605 +2 None _4344 ... 0.2139 0.1604 +3 None Anscombe_4344 ... 0.2220 0.1721 +4 None _4344 ... 0.2227 0.1720 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4344', 'row_number', 'Date_Normalized', '_4344', - '_4344_Lag1Trend', '_4344_Lag1Trend_residue', - '_4344_Lag1Trend_residue_zeroCycle', - '_4344_Lag1Trend_residue_zeroCycle_residue', - '_4344_Lag1Trend_residue_zeroCycle_residue_NoAR', - '_4344_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', '_4344_Trend', - '_4344_Trend_residue', '_4344_Cycle', '_4344_Cycle_residue', '_4344_AR', - '_4344_AR_residue', '_4344_TransformedForecast', '4344_Forecast', - '_4344_TransformedResidue', '4344_Residue'], +Forecast Columns Index(['Date', '4344', 'row_number', 'Date_Normalized', 'Anscombe_4344', + 'Anscombe_4344_ConstantTrend', 'Anscombe_4344_ConstantTrend_residue', + 'Anscombe_4344_ConstantTrend_residue_Seasonal_WeekOfYear', + 'Anscombe_4344_ConstantTrend_residue_Seasonal_WeekOfYear_residue', + 'Anscombe_4344_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR', + 'Anscombe_4344_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR_residue', + 'Anscombe_4344_Trend', 'Anscombe_4344_Trend_residue', + 'Anscombe_4344_Cycle', 'Anscombe_4344_Cycle_residue', + 'Anscombe_4344_AR', 'Anscombe_4344_AR_residue', + 'Anscombe_4344_TransformedForecast', '4344_Forecast', + 'Anscombe_4344_TransformedResidue', '4344_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -5412,95 +5779,97 @@ memory usage: 14.4 KB None Forecasts Date 4344 4344_Forecast -550 2017-01-01 NaN 643.0 -551 2017-01-02 NaN 643.0 -552 2017-01-03 NaN 643.0 -553 2017-01-04 NaN 643.0 -554 2017-01-05 NaN 643.0 -555 2017-01-06 NaN 643.0 -556 2017-01-07 NaN 643.0 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Anscombe_4345_Lag1Trend_residue_zeroCycle_residue_Lag11 -0.005991056914121842 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.4529287815093994 +INFO:pyaf.std:START_FORECASTING '['4345']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4345']' 5.061995267868042 Split Transformation ... ForecastMAPE TestMAPE 0 None Anscombe_4345 ... 0.2586 0.1808 -1 None _4345 ... 0.2690 0.1979 -2 None Anscombe_4345 ... 0.2690 0.1979 -3 None Diff_4345 ... 0.2690 0.1979 -4 None _4345 ... 0.2737 0.1481 +1 None Anscombe_4345 ... 0.2586 0.1808 +2 None _4345 ... 0.2690 0.1979 +3 None _4345 ... 0.2690 0.1979 +4 None Anscombe_4345 ... 0.2690 0.1979 [5 rows x 8 columns] Forecast Columns Index(['Date', '4345', 'row_number', 'Date_Normalized', 'Anscombe_4345', @@ -5640,31 +6018,33 @@ Forecasts { - "Dataset": { - "Signal": "4345", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4345": { + "Dataset": { + "Signal": "4345", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Anscombe_4345_Lag1Trend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Anscombe", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Anscombe_4345_Lag1Trend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "Anscombe", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "80", - "MAE": "21.108037846787145", - "MAPE": "0.2586", - "MASE": "0.9797", - "RMSE": "47.7435217053263" + "Model_Performance": { + "COMPLEXITY": "80", + "MAE": "21.108037846787127", + "MAPE": "0.2586", + "MASE": "0.9797", + "RMSE": "47.7435217053263" + } } } @@ -5674,7 +6054,7 @@ Forecasts {"Date":{"490":"2016-11-02T00:00:00.000Z","491":"2016-11-03T00:00:00.000Z","492":"2016-11-04T00:00:00.000Z","493":"2016-11-05T00:00:00.000Z","494":"2016-11-06T00:00:00.000Z","495":"2016-11-07T00:00:00.000Z","496":"2016-11-08T00:00:00.000Z","497":"2016-11-09T00:00:00.000Z","498":"2016-11-10T00:00:00.000Z","499":"2016-11-11T00:00:00.000Z","500":"2016-11-12T00:00:00.000Z","501":"2016-11-13T00:00:00.000Z","502":"2016-11-14T00:00:00.000Z","503":"2016-11-15T00:00:00.000Z","504":"2016-11-16T00:00:00.000Z","505":"2016-11-17T00:00:00.000Z","506":"2016-11-18T00:00:00.000Z","507":"2016-11-19T00:00:00.000Z","508":"2016-11-20T00:00:00.000Z","509":"2016-11-21T00:00:00.000Z","510":"2016-11-22T00:00:00.000Z","511":"2016-11-23T00:00:00.000Z","512":"2016-11-24T00:00:00.000Z","513":"2016-11-25T00:00:00.000Z","514":"2016-11-26T00:00:00.000Z","515":"2016-11-27T00:00:00.000Z","516":"2016-11-28T00:00:00.000Z","517":"2016-11-29T00:00:00.000Z","518":"2016-11-30T00:00:00.000Z","519":"2016-12-01T00:00:00.000Z","520":"2016-12-02T00:00:00.000Z","521":"2016-12-03T00:00:00.000Z","522":"2016-12-04T00:00:00.000Z","523":"2016-12-05T00:00:00.000Z","524":"2016-12-06T00:00:00.000Z","525":"2016-12-07T00:00:00.000Z","526":"2016-12-08T00:00:00.000Z","527":"2016-12-09T00:00:00.000Z","528":"2016-12-10T00:00:00.000Z","529":"2016-12-11T00:00:00.000Z","530":"2016-12-12T00:00:00.000Z","531":"2016-12-13T00:00:00.000Z","532":"2016-12-14T00:00:00.000Z","533":"2016-12-15T00:00:00.000Z","534":"2016-12-16T00:00:00.000Z","535":"2016-12-17T00:00:00.000Z","536":"2016-12-18T00:00:00.000Z","537":"2016-12-19T00:00:00.000Z","538":"2016-12-20T00:00:00.000Z","539":"2016-12-21T00:00:00.000Z","540":"2016-12-22T00:00:00.000Z","541":"2016-12-23T00:00:00.000Z","542":"2016-12-24T00:00:00.000Z","543":"2016-12-25T00:00:00.000Z","544":"2016-12-26T00:00:00.000Z","545":"2016-12-27T00:00:00.000Z","546":"2016-12-28T00:00:00.000Z","547":"2016-12-29T00:00:00.000Z","548":"2016-12-30T00:00:00.000Z","549":"2016-12-31T00:00:00.000Z","550":"2017-01-01T00:00:00.000Z","551":"2017-01-02T00:00:00.000Z","552":"2017-01-03T00:00:00.000Z","553":"2017-01-04T00:00:00.000Z","554":"2017-01-05T00:00:00.000Z","555":"2017-01-06T00:00:00.000Z","556":"2017-01-07T00:00:00.000Z","557":"2017-01-08T00:00:00.000Z","558":"2017-01-09T00:00:00.000Z","559":"2017-01-10T00:00:00.000Z","560":"2017-01-11T00:00:00.000Z","561":"2017-01-12T00:00:00.000Z","562":"2017-01-13T00:00:00.000Z","563":"2017-01-14T00:00:00.000Z","564":"2017-01-15T00:00:00.000Z","565":"2017-01-16T00:00:00.000Z","566":"2017-01-17T00:00:00.000Z","567":"2017-01-18T00:00:00.000Z","568":"2017-01-19T00:00:00.000Z","569":"2017-01-20T00:00:00.000Z","570":"2017-01-21T00:00:00.000Z","571":"2017-01-22T00:00:00.000Z","572":"2017-01-23T00:00:00.000Z","573":"2017-01-24T00:00:00.000Z","574":"2017-01-25T00:00:00.000Z","575":"2017-01-26T00:00:00.000Z","576":"2017-01-27T00:00:00.000Z","577":"2017-01-28T00:00:00.000Z","578":"2017-01-29T00:00:00.000Z","579":"2017-01-30T00:00:00.000Z","580":"2017-01-31T00:00:00.000Z","581":"2017-02-01T00:00:00.000Z","582":"2017-02-02T00:00:00.000Z","583":"2017-02-03T00:00:00.000Z","584":"2017-02-04T00:00:00.000Z","585":"2017-02-05T00:00:00.000Z","586":"2017-02-06T00:00:00.000Z","587":"2017-02-07T00:00:00.000Z","588":"2017-02-08T00:00:00.000Z","589":"2017-02-09T00:00:00.000Z","590":"2017-02-10T00:00:00.000Z","591":"2017-02-11T00:00:00.000Z","592":"2017-02-12T00:00:00.000Z","593":"2017-02-13T00:00:00.000Z","594":"2017-02-14T00:00:00.000Z","595":"2017-02-15T00:00:00.000Z","596":"2017-02-16T00:00:00.000Z","597":"2017-02-17T00:00:00.000Z","598":"2017-02-18T00:00:00.000Z","599":"2017-02-19T00:00:00.000Z","600":"2017-02-20T00:00:00.000Z","601":"2017-02-21T00:00:00.000Z","602":"2017-02-22T00:00:00.000Z","603":"2017-02-23T00:00:00.000Z","604":"2017-02-24T00:00:00.000Z","605":"2017-02-25T00:00:00.000Z","606":"2017-02-26T00:00:00.000Z","607":"2017-02-27T00:00:00.000Z","608":"2017-02-28T00:00:00.000Z","609":"2017-03-01T00:00:00.000Z"},"4345":{"490":60.0,"491":42.0,"492":36.0,"493":48.0,"494":81.0,"495":57.0,"496":49.0,"497":43.0,"498":53.0,"499":50.0,"500":66.0,"501":73.0,"502":56.0,"503":57.0,"504":49.0,"505":49.0,"506":54.0,"507":62.0,"508":75.0,"509":53.0,"510":46.0,"511":56.0,"512":80.0,"513":57.0,"514":62.0,"515":75.0,"516":48.0,"517":56.0,"518":42.0,"519":69.0,"520":74.0,"521":56.0,"522":84.0,"523":77.0,"524":59.0,"525":73.0,"526":59.0,"527":51.0,"528":55.0,"529":77.0,"530":66.0,"531":59.0,"532":58.0,"533":39.0,"534":55.0,"535":59.0,"536":67.0,"537":90.0,"538":72.0,"539":81.0,"540":79.0,"541":68.0,"542":75.0,"543":72.0,"544":62.0,"545":79.0,"546":68.0,"547":66.0,"548":68.0,"549":53.0,"550":null,"551":null,"552":null,"553":null,"554":null,"555":null,"556":null,"557":null,"558":null,"559":null,"560":null,"561":null,"562":null,"563":null,"564":null,"565":null,"566":null,"567":null,"568":null,"569":null,"570":null,"571":null,"572":null,"573":null,"574":null,"575":null,"576":null,"577":null,"578":null,"579":null,"580":null,"581":null,"582":null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'4346' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4346' 4.686946153640747 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4346']' 23.569448232650757 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4346' Length=550 Min=227.0 Max=3942.0 Mean=534.1345454545454 StdDev=266.81341128769225 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4346' Min=227.0 Max=3942.0 Mean=534.1345454545454 StdDev=266.81341128769225 @@ -5686,32 +6066,41 @@ INFO:pyaf.std:AUTOREG_DETAIL '_4346_ConstantTrend_residue_zeroCycle_residue_AR(1 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1523 MAPE_Forecast=0.1914 MAPE_Test=0.2456 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1485 SMAPE_Forecast=0.1854 SMAPE_Test=0.2285 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8477 MASE_Forecast=0.8213 MASE_Test=0.8323 -INFO:pyaf.std:MODEL_L1 L1_Fit=79.14183341107271 L1_Forecast=100.22595231104582 L1_Test=224.52076653887858 -INFO:pyaf.std:MODEL_L2 L2_Fit=116.42638612316146 L2_Forecast=169.2301894997674 L2_Test=533.5814998872031 +INFO:pyaf.std:MODEL_L1 L1_Fit=79.14183341107271 L1_Forecast=100.2259523110458 L1_Test=224.52076653887863 +INFO:pyaf.std:MODEL_L2 L2_Fit=116.42638612316149 L2_Forecast=169.23018949976742 L2_Test=533.5814998872031 INFO:pyaf.std:MODEL_COMPLEXITY 16 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 514.4566326530612 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4346_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4346_ConstantTrend_residue_zeroCycle_residue_Lag1 0.546829572721971 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4346_ConstantTrend_residue_zeroCycle_residue_Lag7 0.18551414507365716 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4346_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.11744657803307898 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4346_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.1121736165924532 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4346_ConstantTrend_residue_zeroCycle_residue_Lag5 0.09834421339750304 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4346_ConstantTrend_residue_zeroCycle_residue_Lag13 0.09465658647908103 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4346_ConstantTrend_residue_zeroCycle_residue_Lag6 0.07720720286288198 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4346_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.07119156144148953 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4346_ConstantTrend_residue_zeroCycle_residue_Lag14 0.06855594943701548 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4346_ConstantTrend_residue_zeroCycle_residue_Lag10 0.04826210349394752 +INFO:pyaf.std:AR_MODEL_COEFF 1 _4346_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5468295727219707 +INFO:pyaf.std:AR_MODEL_COEFF 2 _4346_ConstantTrend_residue_zeroCycle_residue_Lag7 0.18551414507365702 +INFO:pyaf.std:AR_MODEL_COEFF 3 _4346_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.1174465780330791 +INFO:pyaf.std:AR_MODEL_COEFF 4 _4346_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.11217361659245323 +INFO:pyaf.std:AR_MODEL_COEFF 5 _4346_ConstantTrend_residue_zeroCycle_residue_Lag5 0.09834421339750307 +INFO:pyaf.std:AR_MODEL_COEFF 6 _4346_ConstantTrend_residue_zeroCycle_residue_Lag13 0.09465658647908101 +INFO:pyaf.std:AR_MODEL_COEFF 7 _4346_ConstantTrend_residue_zeroCycle_residue_Lag6 0.07720720286288177 +INFO:pyaf.std:AR_MODEL_COEFF 8 _4346_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.07119156144148955 +INFO:pyaf.std:AR_MODEL_COEFF 9 _4346_ConstantTrend_residue_zeroCycle_residue_Lag14 0.06855594943701518 +INFO:pyaf.std:AR_MODEL_COEFF 10 _4346_ConstantTrend_residue_zeroCycle_residue_Lag10 0.048262103493947495 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.9288899898529053 +INFO:pyaf.std:START_FORECASTING '['4346']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4346']' 4.422168493270874 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4346 ... 0.1881 0.2366 -1 None Anscombe_4346 ... 0.1885 0.2369 -2 None _4346 ... 0.1894 0.2714 -3 None _4346 ... 0.1896 0.2533 +0 None _4346 ... 0.1874 0.2278 +1 None _4346 ... 0.1876 0.2279 +2 None _4346 ... 0.1880 0.2738 +3 None _4346 ... 0.1892 0.2750 4 None Anscombe_4346 ... 0.1897 0.2318 [5 rows x 8 columns] @@ -5803,31 +6192,33 @@ Forecasts { - "Dataset": { - "Signal": "4346", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4346": { + "Dataset": { + "Signal": "4346", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_4346_ConstantTrend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4346_ConstantTrend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "16", - "MAE": "100.22595231104582", - "MAPE": "0.1914", - "MASE": "0.8213", - "RMSE": "169.2301894997674" + "Model_Performance": { + "COMPLEXITY": "16", + "MAE": "100.2259523110458", + "MAPE": "0.1914", + "MASE": "0.8213", + "RMSE": "169.23018949976742" + } } } @@ -5836,8 +6227,8 @@ Forecasts 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'4347' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4347']' 19.735084056854248 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4347' Length=550 Min=24.0 Max=2960.0 Mean=149.7490909090909 StdDev=212.86654536478292 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4347' Min=1.224744871391589 Max=2.345207879911715 Mean=1.2893879916885689 StdDev=0.0938024212448929 @@ -5849,33 +6240,42 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4347_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.4874 MAPE_Forecast=0.2919 MAPE_Test=0.229 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.396 SMAPE_Forecast=0.275 SMAPE_Test=0.2357 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9095 MASE_Forecast=0.8534 MASE_Test=0.9275 -INFO:pyaf.std:MODEL_L1 L1_Fit=59.04724763849656 L1_Forecast=59.81695526453907 L1_Test=56.59246360436076 -INFO:pyaf.std:MODEL_L2 L2_Fit=169.0265270641396 L2_Forecast=145.86944441692535 L2_Test=96.03947955163413 +INFO:pyaf.std:MODEL_L1 L1_Fit=59.047247638496565 L1_Forecast=59.81695526453908 L1_Test=56.59246360436077 +INFO:pyaf.std:MODEL_L2 L2_Fit=169.02652706413957 L2_Forecast=145.86944441692535 L2_Test=96.03947955163413 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.2804757590890112 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Anscombe_4347_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5207162921609474 -INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag7 0.10129232699661955 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag4 0.05921638931840352 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag13 0.05866209353312731 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.03290794046804043 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag3 0.0257135267868256 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag15 0.024158990022717257 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag5 0.02355842735023916 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.014177694642289067 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag16 0.009698191284967794 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5207162921609476 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag7 0.10129232699661975 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag4 0.05921638931840317 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag13 0.05866209353312746 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.03290794046804034 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag3 0.02571352678682555 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag15 0.024158990022717108 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag5 0.023558427350239095 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.01417769464228899 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_Lag16 0.00969819128496796 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.8133366107940674 +INFO:pyaf.std:START_FORECASTING '['4347']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4347']' 6.272543907165527 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4347 ... 0.2919 0.2290 -1 None Anscombe_4347 ... 0.3088 0.2300 -2 None _4347 ... 0.3210 0.2477 -3 None Anscombe_4347 ... 0.3210 0.2477 -4 None Diff_4347 ... 0.3210 0.2477 +0 None Anscombe_4347 ... 0.2857 0.2228 +1 None Anscombe_4347 ... 0.2893 0.2304 +2 None Anscombe_4347 ... 0.2919 0.2290 +3 None Anscombe_4347 ... 0.2919 0.2290 +4 None Anscombe_4347 ... 0.2934 0.2278 [5 rows x 8 columns] Forecast Columns Index(['Date', '4347', 'row_number', 'Date_Normalized', 'Anscombe_4347', @@ -5967,31 +6367,33 @@ Forecasts { - "Dataset": { - "Signal": "4347", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4347": { + "Dataset": { + "Signal": "4347", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "Anscombe", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "59.81695526453907", - "MAPE": "0.2919", - "MASE": "0.8534", - "RMSE": "145.86944441692535" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Anscombe_4347_ConstantTrend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Anscombe", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "59.81695526453908", + "MAPE": "0.2919", + "MASE": "0.8534", + "RMSE": "145.86944441692535" + } } } @@ -6001,58 +6403,56 @@ Forecasts {"Date":{"490":"2016-11-02T00:00:00.000Z","491":"2016-11-03T00:00:00.000Z","492":"2016-11-04T00:00:00.000Z","493":"2016-11-05T00:00:00.000Z","494":"2016-11-06T00:00:00.000Z","495":"2016-11-07T00:00:00.000Z","496":"2016-11-08T00:00:00.000Z","497":"2016-11-09T00:00:00.000Z","498":"2016-11-10T00:00:00.000Z","499":"2016-11-11T00:00:00.000Z","500":"2016-11-12T00:00:00.000Z","501":"2016-11-13T00:00:00.000Z","502":"2016-11-14T00:00:00.000Z","503":"2016-11-15T00:00:00.000Z","504":"2016-11-16T00:00:00.000Z","505":"2016-11-17T00:00:00.000Z","506":"2016-11-18T00:00:00.000Z","507":"2016-11-19T00:00:00.000Z","508":"2016-11-20T00:00:00.000Z","509":"2016-11-21T00:00:00.000Z","510":"2016-11-22T00:00:00.000Z","511":"2016-11-23T00:00:00.000Z","512":"2016-11-24T00:00:00.000Z","513":"2016-11-25T00:00:00.000Z","514":"2016-11-26T00:00:00.000Z","515":"2016-11-27T00:00:00.000Z","516":"2016-11-28T00:00:00.000Z","517":"2016-11-29T00:00:00.000Z","518":"2016-11-30T00:00:00.000Z","519":"2016-12-01T00:00:00.000Z","520":"2016-12-02T00:00:00.000Z","521":"2016-12-03T00:00:00.000Z","522":"2016-12-04T00:00:00.000Z","523":"2016-12-05T00:00:00.000Z","524":"2016-12-06T00:00:00.000Z","525":"2016-12-07T00:00:00.000Z","526":"2016-12-08T00:00:00.000Z","527":"2016-12-09T00:00:00.000Z","528":"2016-12-10T00:00:00.000Z","529":"2016-12-11T00:00:00.000Z","530":"2016-12-12T00:00:00.000Z","531":"2016-12-13T00:00:00.000Z","532":"2016-12-14T00:00:00.000Z","533":"2016-12-15T00:00:00.000Z","534":"2016-12-16T00:00:00.000Z","535":"2016-12-17T00:00:00.000Z","536":"2016-12-18T00:00:00.000Z","537":"2016-12-19T00:00:00.000Z","538":"2016-12-20T00:00:00.000Z","539":"2016-12-21T00:00:00.000Z","540":"2016-12-22T00:00:00.000Z","541":"2016-12-23T00:00:00.000Z","542":"2016-12-24T00:00:00.000Z","543":"2016-12-25T00:00:00.000Z","544":"2016-12-26T00:00:00.000Z","545":"2016-12-27T00:00:00.000Z","546":"2016-12-28T00:00:00.000Z","547":"2016-12-29T00:00:00.000Z","548":"2016-12-30T00:00:00.000Z","549":"2016-12-31T00:00:00.000Z","550":"2017-01-01T00:00:00.000Z","551":"2017-01-02T00:00:00.000Z","552":"2017-01-03T00:00:00.000Z","553":"2017-01-04T00:00:00.000Z","554":"2017-01-05T00:00:00.000Z","555":"2017-01-06T00:00:00.000Z","556":"2017-01-07T00:00:00.000Z","557":"2017-01-08T00:00:00.000Z","558":"2017-01-09T00:00:00.000Z","559":"2017-01-10T00:00:00.000Z","560":"2017-01-11T00:00:00.000Z","561":"2017-01-12T00:00:00.000Z","562":"2017-01-13T00:00:00.000Z","563":"2017-01-14T00:00:00.000Z","564":"2017-01-15T00:00:00.000Z","565":"2017-01-16T00:00:00.000Z","566":"2017-01-17T00:00:00.000Z","567":"2017-01-18T00:00:00.000Z","568":"2017-01-19T00:00:00.000Z","569":"2017-01-20T00:00:00.000Z","570":"2017-01-21T00:00:00.000Z","571":"2017-01-22T00:00:00.000Z","572":"2017-01-23T00:00:00.000Z","573":"2017-01-24T00:00:00.000Z","574":"2017-01-25T00:00:00.000Z","575":"2017-01-26T00:00:00.000Z","576":"2017-01-27T00:00:00.000Z","577":"2017-01-28T00:00:00.000Z","578":"2017-01-29T00:00:00.000Z","579":"2017-01-30T00:00:00.000Z","580":"2017-01-31T00:00:00.000Z","581":"2017-02-01T00:00:00.000Z","582":"2017-02-02T00:00:00.000Z","583":"2017-02-03T00:00:00.000Z","584":"2017-02-04T00:00:00.000Z","585":"2017-02-05T00:00:00.000Z","586":"2017-02-06T00:00:00.000Z","587":"2017-02-07T00:00:00.000Z","588":"2017-02-08T00:00:00.000Z","589":"2017-02-09T00:00:00.000Z","590":"2017-02-10T00:00:00.000Z","591":"2017-02-11T00:00:00.000Z","592":"2017-02-12T00:00:00.000Z","593":"2017-02-13T00:00:00.000Z","594":"2017-02-14T00:00:00.000Z","595":"2017-02-15T00:00:00.000Z","596":"2017-02-16T00:00:00.000Z","597":"2017-02-17T00:00:00.000Z","598":"2017-02-18T00:00:00.000Z","599":"2017-02-19T00:00:00.000Z","600":"2017-02-20T00:00:00.000Z","601":"2017-02-21T00:00:00.000Z","602":"2017-02-22T00:00:00.000Z","603":"2017-02-23T00:00:00.000Z","604":"2017-02-24T00:00:00.000Z","605":"2017-02-25T00:00:00.000Z","606":"2017-02-26T00:00:00.000Z","607":"2017-02-27T00:00:00.000Z","608":"2017-02-28T00:00:00.000Z","609":"2017-03-01T00:00:00.000Z"},"4347":{"490":135.0,"491":132.0,"492":95.0,"493":96.0,"494":107.0,"495":120.0,"496":83.0,"497":87.0,"498":102.0,"499":132.0,"500":113.0,"501":83.0,"502":128.0,"503":199.0,"504":211.0,"505":182.0,"506":341.0,"507":256.0,"508":212.0,"509":614.0,"510":693.0,"511":356.0,"512":272.0,"513":326.0,"514":224.0,"515":255.0,"516":477.0,"517":280.0,"518":243.0,"519":193.0,"520":232.0,"521":205.0,"522":210.0,"523":233.0,"524":219.0,"525":220.0,"526":190.0,"527":142.0,"528":133.0,"529":181.0,"530":141.0,"531":196.0,"532":195.0,"533":179.0,"534":191.0,"535":132.0,"536":139.0,"537":191.0,"538":305.0,"539":261.0,"540":261.0,"541":270.0,"542":263.0,"543":236.0,"544":320.0,"545":289.0,"546":294.0,"547":247.0,"548":485.0,"549":217.0,"550":null,"551":null,"552":null,"553":null,"554":null,"555":null,"556":null,"557":null,"558":null,"559":null,"560":null,"561":null,"562":null,"563":null,"564":null,"565":null,"566":null,"567":null,"568":null,"569":null,"570":null,"571":null,"572":null,"573":null,"574":null,"575":null,"576":null,"577":null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'4348' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4348' 3.862905502319336 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4348']' 21.764936923980713 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4348' Length=550 Min=572.0 Max=20217.0 Mean=1175.0727272727272 StdDev=861.131252893973 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4348' Min=1.224744871391589 Max=2.345207879911715 Mean=1.2728665308286657 StdDev=0.05103857523242857 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(16)' [LinearTrend + Seasonal_DayOfWeek + AR] -INFO:pyaf.std:TREND_DETAIL 'Anscombe_4348_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] -INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1305 MAPE_Forecast=0.1977 MAPE_Test=0.1107 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1268 SMAPE_Forecast=0.1849 SMAPE_Test=0.102 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.972 MASE_Forecast=0.7183 MASE_Test=0.7183 -INFO:pyaf.std:MODEL_L1 L1_Fit=148.60269637484717 L1_Forecast=379.72277258111114 L1_Test=104.10530038938401 -INFO:pyaf.std:MODEL_L2 L2_Fit=231.43048632214243 L2_Forecast=1943.389466264457 L2_Test=140.08516755731705 -INFO:pyaf.std:MODEL_COMPLEXITY 68 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4348' Min=572.0 Max=20217.0 Mean=1175.0727272727272 StdDev=861.131252893973 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4348_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [LinearTrend + Seasonal_WeekOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4348_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4348_LinearTrend_residue_Seasonal_WeekOfYear' [Seasonal_WeekOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_4348_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1082 MAPE_Forecast=0.1383 MAPE_Test=0.2503 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1056 SMAPE_Forecast=0.1424 SMAPE_Test=0.211 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8239 MASE_Forecast=0.622 MASE_Test=1.6215 +INFO:pyaf.std:MODEL_L1 L1_Fit=125.96158487206206 L1_Forecast=328.8352325055807 L1_Test=235.00150905097095 +INFO:pyaf.std:MODEL_L2 L2_Fit=225.94477129262384 L2_Forecast=1952.5205485444055 L2_Test=275.1005991939555 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1217.272483651409, array([-68.91741628])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4348_LinearTrend_residue_Seasonal_WeekOfYear 3.3265245435992483 {27: -353.96838459096693, 28: -283.41082833087876, 29: -302.2562192501159, 30: -257.39477736441904, 31: -238.1609617276497, 32: -346.4559242209243, 33: -337.86958983079194, 34: -281.4595148173412, 35: -225.2256991805716, 36: -167.52066167384623, 37: -109.75806790703268, 38: -30.524252270263332, 39: -7.81921476353773, 40: 30.59086024991302, 41: 177.6484165100012, 42: 297.41101027681475, 43: -9.3551740864159, 44: 77.52612279699088, 45: 196.11245718712325, 46: 141.99375407053003, 47: 224.05131033061798, 48: 342.46138534406896, 49: 170.0477197342011, 50: 80.28153537097069, 51: 36.51535100774004, 52: -250.4270927321718, 53: -109.19327709540221, 1: 366.86427916468574, 2: 254.4506135548179, 3: 380.50816981490607, 4: 293.56572607499425, 5: 179.1520604651264, 6: 250.03335734853317, 7: 133.61969173866532, 8: 197.8535073754349, 9: 259.7348042588417, 10: 262.1448792722924, 11: 270.2024355323806, 12: 117.43625116915018, 13: 18.198844935963734, 14: 29.25640119605191, 15: 40.961438702777286, 16: 484.37151371622804, 17: 132.9578481063602, 18: -48.984595633551635, 19: 218.42547937989934, 20: -1.8694831133752814, 21: -43.28314872324313, 22: 38.12692629020762, 23: -69.4629986963414, 24: -97.93422056629743, 25: -305.1716267994839, 26: -371.9378111627143} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag1 0.05875818813960627 -INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag2 0.05143013856109522 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag3 0.04336969788197394 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag4 0.040183058201254326 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag5 0.03746596228333225 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag8 0.034732285912098766 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag6 0.03376764029109821 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag7 0.03074169247730624 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag9 0.029246393272577335 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag10 0.02316605574849301 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.032500982284546 +INFO:pyaf.std:START_FORECASTING '['4348']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4348']' 2.8151891231536865 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4348 ... 0.1977 0.1107 -1 None Anscombe_4348 ... 0.1977 0.1107 -2 None Anscombe_4348 ... 0.2108 0.1310 -3 None Anscombe_4348 ... 0.2108 0.1310 -4 None Anscombe_4348 ... 0.2162 0.1457 +0 None _4348 ... 0.1383 0.2503 +1 None Anscombe_4348 ... 0.1387 0.2513 +2 None Anscombe_4348 ... 0.1429 0.2325 +3 None _4348 ... 0.1608 0.3058 +4 None Anscombe_4348 ... 0.1608 0.3058 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4348', 'row_number', 'Date_Normalized', 'Anscombe_4348', - 'Anscombe_4348_LinearTrend', 'Anscombe_4348_LinearTrend_residue', - 'Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek', - 'Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue', - 'Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(16)', - 'Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(16)_residue', - 'Anscombe_4348_Trend', 'Anscombe_4348_Trend_residue', - 'Anscombe_4348_Cycle', 'Anscombe_4348_Cycle_residue', - 'Anscombe_4348_AR', 'Anscombe_4348_AR_residue', - 'Anscombe_4348_TransformedForecast', '4348_Forecast', - 'Anscombe_4348_TransformedResidue', '4348_Residue'], +Forecast Columns Index(['Date', '4348', 'row_number', 'Date_Normalized', '_4348', + '_4348_LinearTrend', '_4348_LinearTrend_residue', + '_4348_LinearTrend_residue_Seasonal_WeekOfYear', + '_4348_LinearTrend_residue_Seasonal_WeekOfYear_residue', + '_4348_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR', + '_4348_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR_residue', + '_4348_Trend', '_4348_Trend_residue', '_4348_Cycle', + '_4348_Cycle_residue', '_4348_AR', '_4348_AR_residue', + '_4348_TransformedForecast', '4348_Forecast', + '_4348_TransformedResidue', '4348_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -6067,95 +6467,97 @@ memory usage: 14.4 KB None Forecasts Date 4348 4348_Forecast -550 2017-01-01 NaN 936.530299 -551 2017-01-02 NaN 1109.187599 -552 2017-01-03 NaN 1081.775604 -553 2017-01-04 NaN 1091.866189 -554 2017-01-05 NaN 1080.881655 -555 2017-01-06 NaN 942.301854 -556 2017-01-07 NaN 837.502713 -557 2017-01-08 NaN 974.320565 -558 2017-01-09 NaN 1147.586832 -559 2017-01-10 NaN 1119.652868 -560 2017-01-11 NaN 1131.919629 -561 2017-01-12 NaN 1126.488675 -562 2017-01-13 NaN 985.759112 -563 2017-01-14 NaN 880.588879 -564 2017-01-15 NaN 1014.980235 -565 2017-01-16 NaN 1187.263978 -566 2017-01-17 NaN 1157.291301 -567 2017-01-18 NaN 1163.710646 -568 2017-01-19 NaN 1153.724451 -569 2017-01-20 NaN 1012.269114 -570 2017-01-21 NaN 903.063340 -571 2017-01-22 NaN 1034.052739 -572 2017-01-23 NaN 1204.570425 -573 2017-01-24 NaN 1171.878609 -574 2017-01-25 NaN 1177.300509 -575 2017-01-26 NaN 1166.318169 -576 2017-01-27 NaN 1023.810287 -577 2017-01-28 NaN 913.634436 -578 2017-01-29 NaN 1043.754089 -579 2017-01-30 NaN 1213.454354 -580 2017-01-31 NaN 1179.839118 -581 2017-02-01 NaN 1184.432235 -582 2017-02-02 NaN 1172.665255 -583 2017-02-03 NaN 1029.365150 -584 2017-02-04 NaN 918.519762 -585 2017-02-05 NaN 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2017-02-08 NaN 1363.665328 +589 2017-02-09 NaN 1363.489068 +590 2017-02-10 NaN 1363.312809 +591 2017-02-11 NaN 1363.136549 +592 2017-02-12 NaN 1362.960290 +593 2017-02-13 NaN 1246.370365 +594 2017-02-14 NaN 1246.194106 +595 2017-02-15 NaN 1246.017846 +596 2017-02-16 NaN 1245.841587 +597 2017-02-17 NaN 1245.665328 +598 2017-02-18 NaN 1245.489068 +599 2017-02-19 NaN 1245.312809 +600 2017-02-20 NaN 1309.370365 +601 2017-02-21 NaN 1309.194106 +602 2017-02-22 NaN 1309.017846 +603 2017-02-23 NaN 1308.841587 +604 2017-02-24 NaN 1308.665328 +605 2017-02-25 NaN 1308.489068 +606 2017-02-26 NaN 1308.312809 +607 2017-02-27 NaN 1370.017846 +608 2017-02-28 NaN 1369.841587 +609 2017-03-01 NaN 1369.665328 { - "Dataset": { - "Signal": "4348", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4348": { + "Dataset": { + "Signal": "4348", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Anscombe_4348_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(16)", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "Anscombe", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "68", - "MAE": "379.72277258111114", - "MAPE": "0.1977", - "MASE": "0.7183", - "RMSE": "1943.389466264457" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4348_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR", + "Cycle": "Seasonal_WeekOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "328.8352325055807", + "MAPE": "0.1383", + "MASE": "0.622", + "RMSE": "1952.5205485444055" + } } } @@ -6164,8 +6566,8 @@ Forecasts 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'4349' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4349']' 19.116188049316406 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4349' Length=550 Min=662.0 Max=7156.0 Mean=1876.1854545454546 StdDev=792.236113890339 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4349' Min=662.0 Max=7156.0 Mean=1876.1854545454546 StdDev=792.236113890339 @@ -6180,20 +6582,29 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.9921 MASE_Test=0.9857 INFO:pyaf.std:MODEL_L1 L1_Fit=271.8290816326531 L1_Forecast=266.2551020408163 L1_Test=225.5 INFO:pyaf.std:MODEL_L2 L2_Fit=572.1939271838896 L2_Forecast=422.5618780916311 L2_Test=328.2701631278725 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 1098.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4349_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0804834365844727 +INFO:pyaf.std:START_FORECASTING '['4349']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4349']' 1.6422593593597412 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4349 ... 0.1573 0.1625 -1 None _4349 ... 0.1614 0.1633 -2 None Anscombe_4349 ... 0.1614 0.1633 -3 None Diff_4349 ... 0.1614 0.1633 -4 None Anscombe_4349 ... 0.1617 0.1763 +0 None Anscombe_4349 ... 0.1557 0.1633 +1 None Anscombe_4349 ... 0.1573 0.1625 +2 None Anscombe_4349 ... 0.1573 0.1567 +3 None Anscombe_4349 ... 0.1573 0.1625 +4 None _4349 ... 0.1591 0.1573 [5 rows x 8 columns] Forecast Columns Index(['Date', '4349', 'row_number', 'Date_Normalized', '_4349', @@ -6283,31 +6694,33 @@ Forecasts { - "Dataset": { - "Signal": "4349", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4349": { + "Dataset": { + "Signal": "4349", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4349_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_4349_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "266.2551020408163", - "MAPE": "0.1614", - "MASE": "0.9921", - "RMSE": "422.5618780916311" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "266.2551020408163", + "MAPE": "0.1614", + "MASE": "0.9921", + "RMSE": "422.5618780916311" + } } } @@ -6317,47 +6730,56 @@ Forecasts {"Date":{"490":"2016-11-02T00:00:00.000Z","491":"2016-11-03T00:00:00.000Z","492":"2016-11-04T00:00:00.000Z","493":"2016-11-05T00:00:00.000Z","494":"2016-11-06T00:00:00.000Z","495":"2016-11-07T00:00:00.000Z","496":"2016-11-08T00:00:00.000Z","497":"2016-11-09T00:00:00.000Z","498":"2016-11-10T00:00:00.000Z","499":"2016-11-11T00:00:00.000Z","500":"2016-11-12T00:00:00.000Z","501":"2016-11-13T00:00:00.000Z","502":"2016-11-14T00:00:00.000Z","503":"2016-11-15T00:00:00.000Z","504":"2016-11-16T00:00:00.000Z","505":"2016-11-17T00:00:00.000Z","506":"2016-11-18T00:00:00.000Z","507":"2016-11-19T00:00:00.000Z","508":"2016-11-20T00:00:00.000Z","509":"2016-11-21T00:00:00.000Z","510":"2016-11-22T00:00:00.000Z","511":"2016-11-23T00:00:00.000Z","512":"2016-11-24T00:00:00.000Z","513":"2016-11-25T00:00:00.000Z","514":"2016-11-26T00:00:00.000Z","515":"2016-11-27T00:00:00.000Z","516":"2016-11-28T00:00:00.000Z","517":"2016-11-29T00:00:00.000Z","518":"2016-11-30T00:00:00.000Z","519":"2016-12-01T00:00:00.000Z","520":"2016-12-02T00:00:00.000Z","521":"2016-12-03T00:00:00.000Z","522":"2016-12-04T00:00:00.000Z","523":"2016-12-05T00:00:00.000Z","524":"2016-12-06T00:00:00.000Z","525":"2016-12-07T00:00:00.000Z","526":"2016-12-08T00:00:00.000Z","527":"2016-12-09T00:00:00.000Z","528":"2016-12-10T00:00:00.000Z","529":"2016-12-11T00:00:00.000Z","530":"2016-12-12T00:00:00.000Z","531":"2016-12-13T00:00:00.000Z","532":"2016-12-14T00:00:00.000Z","533":"2016-12-15T00:00:00.000Z","534":"2016-12-16T00:00:00.000Z","535":"2016-12-17T00:00:00.000Z","536":"2016-12-18T00:00:00.000Z","537":"2016-12-19T00:00:00.000Z","538":"2016-12-20T00:00:00.000Z","539":"2016-12-21T00:00:00.000Z","540":"2016-12-22T00:00:00.000Z","541":"2016-12-23T00:00:00.000Z","542":"2016-12-24T00:00:00.000Z","543":"2016-12-25T00:00:00.000Z","544":"2016-12-26T00:00:00.000Z","545":"2016-12-27T00:00:00.000Z","546":"2016-12-28T00:00:00.000Z","547":"2016-12-29T00:00:00.000Z","548":"2016-12-30T00:00:00.000Z","549":"2016-12-31T00:00:00.000Z","550":"2017-01-01T00:00:00.000Z","551":"2017-01-02T00:00:00.000Z","552":"2017-01-03T00:00:00.000Z","553":"2017-01-04T00:00:00.000Z","554":"2017-01-05T00:00:00.000Z","555":"2017-01-06T00:00:00.000Z","556":"2017-01-07T00:00:00.000Z","557":"2017-01-08T00:00:00.000Z","558":"2017-01-09T00:00:00.000Z","559":"2017-01-10T00:00:00.000Z","560":"2017-01-11T00:00:00.000Z","561":"2017-01-12T00:00:00.000Z","562":"2017-01-13T00:00:00.000Z","563":"2017-01-14T00:00:00.000Z","564":"2017-01-15T00:00:00.000Z","565":"2017-01-16T00:00:00.000Z","566":"2017-01-17T00:00:00.000Z","567":"2017-01-18T00:00:00.000Z","568":"2017-01-19T00:00:00.000Z","569":"2017-01-20T00:00:00.000Z","570":"2017-01-21T00:00:00.000Z","571":"2017-01-22T00:00:00.000Z","572":"2017-01-23T00:00:00.000Z","573":"2017-01-24T00:00:00.000Z","574":"2017-01-25T00:00:00.000Z","575":"2017-01-26T00:00:00.000Z","576":"2017-01-27T00:00:00.000Z","577":"2017-01-28T00:00:00.000Z","578":"2017-01-29T00:00:00.000Z","579":"2017-01-30T00:00:00.000Z","580":"2017-01-31T00:00:00.000Z","581":"2017-02-01T00:00:00.000Z","582":"2017-02-02T00:00:00.000Z","583":"2017-02-03T00:00:00.000Z","584":"2017-02-04T00:00:00.000Z","585":"2017-02-05T00:00:00.000Z","586":"2017-02-06T00:00:00.000Z","587":"2017-02-07T00:00:00.000Z","588":"2017-02-08T00:00:00.000Z","589":"2017-02-09T00:00:00.000Z","590":"2017-02-10T00:00:00.000Z","591":"2017-02-11T00:00:00.000Z","592":"2017-02-12T00:00:00.000Z","593":"2017-02-13T00:00:00.000Z","594":"2017-02-14T00:00:00.000Z","595":"2017-02-15T00:00:00.000Z","596":"2017-02-16T00:00:00.000Z","597":"2017-02-17T00:00:00.000Z","598":"2017-02-18T00:00:00.000Z","599":"2017-02-19T00:00:00.000Z","600":"2017-02-20T00:00:00.000Z","601":"2017-02-21T00:00:00.000Z","602":"2017-02-22T00:00:00.000Z","603":"2017-02-23T00:00:00.000Z","604":"2017-02-24T00:00:00.000Z","605":"2017-02-25T00:00:00.000Z","606":"2017-02-26T00:00:00.000Z","607":"2017-02-27T00:00:00.000Z","608":"2017-02-28T00:00:00.000Z","609":"2017-03-01T00:00:00.000Z"},"4349":{"490":1089.0,"491":1163.0,"492":1115.0,"493":1166.0,"494":1227.0,"495":1319.0,"496":1453.0,"497":1153.0,"498":1077.0,"499":1255.0,"500":1974.0,"501":1693.0,"502":1777.0,"503":1693.0,"504":1659.0,"505":1324.0,"506":1297.0,"507":1312.0,"508":1295.0,"509":1408.0,"510":1344.0,"511":1191.0,"512":1462.0,"513":1397.0,"514":1340.0,"515":2165.0,"516":1820.0,"517":1299.0,"518":1223.0,"519":1727.0,"520":2013.0,"521":1471.0,"522":1398.0,"523":1258.0,"524":1495.0,"525":1441.0,"526":1294.0,"527":1320.0,"528":967.0,"529":974.0,"530":1867.0,"531":1311.0,"532":1020.0,"533":1073.0,"534":935.0,"535":975.0,"536":1082.0,"537":1206.0,"538":1234.0,"539":1064.0,"540":1867.0,"541":1056.0,"542":1090.0,"543":905.0,"544":1335.0,"545":1741.0,"546":1008.0,"547":1078.0,"548":992.0,"549":922.0,"550":null,"551":null,"552":null,"553":null,"554":null,"555":null,"556":null,"557":null,"558":null,"559":null,"560":null,"561":null,"562":null,"563":null,"564":null,"565":null,"566":null,"567":null,"568":null,"569":null,"570":null,"571":null,"572":n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'4350' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4350' 3.9921329021453857 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4350']' 17.636701107025146 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4350' Length=550 Min=276.0 Max=5134.0 Mean=602.5109090909091 StdDev=271.9999112117016 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4350' Min=-4748.0 Max=4619.0 Mean=-0.0890909090909091 StdDev=354.1943755894122 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_4350_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_4350_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_4350_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_4350_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.201 MAPE_Forecast=0.1615 MAPE_Test=0.1981 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2176 SMAPE_Forecast=0.178 SMAPE_Test=0.219 -INFO:pyaf.std:MODEL_MASE MASE_Fit=1.0584 MASE_Forecast=0.79 MASE_Test=0.9327 -INFO:pyaf.std:MODEL_L1 L1_Fit=138.42315701790915 L1_Forecast=119.95980841316123 L1_Test=151.93605442176877 -INFO:pyaf.std:MODEL_L2 L2_Fit=305.7466194652891 L2_Forecast=208.01032747164382 L2_Test=232.39485271638114 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4350' Min=276.0 Max=5134.0 Mean=602.5109090909091 StdDev=271.9999112117016 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4350_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [ConstantTrend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4350_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4350_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4350_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1921 MAPE_Forecast=0.1595 MAPE_Test=0.1916 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1917 SMAPE_Forecast=0.1757 SMAPE_Test=0.2199 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.949 MASE_Forecast=0.7809 MASE_Test=0.932 +INFO:pyaf.std:MODEL_L1 L1_Fit=124.12244897959184 L1_Forecast=118.58163265306122 L1_Test=151.825 +INFO:pyaf.std:MODEL_L2 L2_Fit=296.7673064602526 L2_Forecast=206.38176681448724 L2_Test=235.60432048953035 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 588.2295918367347 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4350_ConstantTrend_residue_Seasonal_DayOfWeek -27.7295918367347 {2: 18.7704081632653, 3: 19.2704081632653, 4: -53.7295918367347, 5: -119.7295918367347, 6: -84.2295918367347, 0: -9.729591836734699, 1: -3.7295918367346985} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.077364206314087 +INFO:pyaf.std:START_FORECASTING '['4350']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4350']' 2.4117233753204346 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4350 ... 0.1606 0.2009 -1 None Diff_4350 ... 0.1615 0.1981 -2 None Anscombe_4350 ... 0.1682 0.1768 -3 None Anscombe_4350 ... 0.1726 0.1823 -4 None Anscombe_4350 ... 0.1730 0.1885 +0 None Anscombe_4350 ... 0.1518 0.2173 +1 None _4350 ... 0.1527 0.2105 +2 None Anscombe_4350 ... 0.1593 0.2028 +3 None _4350 ... 0.1595 0.1916 +4 None Anscombe_4350 ... 0.1595 0.1916 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4350', 'row_number', 'Date_Normalized', 'Diff_4350', - 'Diff_4350_ConstantTrend', 'Diff_4350_ConstantTrend_residue', - 'Diff_4350_ConstantTrend_residue_zeroCycle', - 'Diff_4350_ConstantTrend_residue_zeroCycle_residue', - 'Diff_4350_ConstantTrend_residue_zeroCycle_residue_NoAR', - 'Diff_4350_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', - 'Diff_4350_Trend', 'Diff_4350_Trend_residue', 'Diff_4350_Cycle', - 'Diff_4350_Cycle_residue', 'Diff_4350_AR', 'Diff_4350_AR_residue', - 'Diff_4350_TransformedForecast', '4350_Forecast', - 'Diff_4350_TransformedResidue', '4350_Residue'], +Forecast Columns Index(['Date', '4350', 'row_number', 'Date_Normalized', '_4350', + '_4350_ConstantTrend', '_4350_ConstantTrend_residue', + '_4350_ConstantTrend_residue_Seasonal_DayOfWeek', + '_4350_ConstantTrend_residue_Seasonal_DayOfWeek_residue', + '_4350_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4350_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4350_Trend', '_4350_Trend_residue', '_4350_Cycle', + '_4350_Cycle_residue', '_4350_AR', '_4350_AR_residue', + '_4350_TransformedForecast', '4350_Forecast', + '_4350_TransformedResidue', '4350_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -6372,95 +6794,97 @@ memory usage: 14.4 KB None Forecasts Date 4350 4350_Forecast -550 2017-01-01 NaN 575.448980 -551 2017-01-02 NaN 575.653061 -552 2017-01-03 NaN 575.857143 -553 2017-01-04 NaN 576.061224 -554 2017-01-05 NaN 576.265306 -555 2017-01-06 NaN 576.469388 -556 2017-01-07 NaN 576.673469 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'4351' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4351']' 18.67135763168335 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4351' Length=550 Min=126.0 Max=2511.0 Mean=469.59090909090907 StdDev=225.7825460463531 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4351' Min=126.0 Max=2511.0 Mean=469.59090909090907 StdDev=225.7825460463531 @@ -6482,33 +6906,42 @@ INFO:pyaf.std:AUTOREG_DETAIL '_4351_Lag1Trend_residue_zeroCycle_residue_AR(16)' INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1558 MAPE_Forecast=0.1661 MAPE_Test=0.4462 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1512 SMAPE_Forecast=0.1621 SMAPE_Test=0.3529 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6611 MASE_Forecast=0.7335 MASE_Test=1.0621 -INFO:pyaf.std:MODEL_L1 L1_Fit=70.26818014244121 L1_Forecast=64.59163539029159 L1_Test=254.19466303788852 -INFO:pyaf.std:MODEL_L2 L2_Fit=118.61303082978596 L2_Forecast=93.08222703131476 L2_Test=463.3505791187042 +INFO:pyaf.std:MODEL_L1 L1_Fit=70.26818014244121 L1_Forecast=64.59163539029161 L1_Test=254.19466303788852 +INFO:pyaf.std:MODEL_L2 L2_Fit=118.61303082978596 L2_Forecast=93.08222703131477 L2_Test=463.3505791187044 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 605.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4351_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4351_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.4483490568582331 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4351_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.42074846767288787 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4351_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.4179650601965346 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4351_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.3294049141923312 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4351_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.32522019926081314 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4351_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.2381537637561199 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4351_Lag1Trend_residue_zeroCycle_residue_Lag12 -0.20514708202816828 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4351_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.13766496652523025 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4351_Lag1Trend_residue_zeroCycle_residue_Lag14 0.13276037036561691 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4351_Lag1Trend_residue_zeroCycle_residue_Lag11 -0.10778086690230775 +INFO:pyaf.std:AR_MODEL_COEFF 1 _4351_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.44834905685823334 +INFO:pyaf.std:AR_MODEL_COEFF 2 _4351_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.4207484676728881 +INFO:pyaf.std:AR_MODEL_COEFF 3 _4351_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.41796506019653573 +INFO:pyaf.std:AR_MODEL_COEFF 4 _4351_Lag1Trend_residue_zeroCycle_residue_Lag5 -0.3294049141923324 +INFO:pyaf.std:AR_MODEL_COEFF 5 _4351_Lag1Trend_residue_zeroCycle_residue_Lag4 -0.32522019926081397 +INFO:pyaf.std:AR_MODEL_COEFF 6 _4351_Lag1Trend_residue_zeroCycle_residue_Lag6 -0.23815376375612024 +INFO:pyaf.std:AR_MODEL_COEFF 7 _4351_Lag1Trend_residue_zeroCycle_residue_Lag12 -0.20514708202816856 +INFO:pyaf.std:AR_MODEL_COEFF 8 _4351_Lag1Trend_residue_zeroCycle_residue_Lag9 -0.13766496652523028 +INFO:pyaf.std:AR_MODEL_COEFF 9 _4351_Lag1Trend_residue_zeroCycle_residue_Lag14 0.13276037036561672 +INFO:pyaf.std:AR_MODEL_COEFF 10 _4351_Lag1Trend_residue_zeroCycle_residue_Lag11 -0.10778086690230784 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.0858633518218994 +INFO:pyaf.std:START_FORECASTING '['4351']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4351']' 3.1524362564086914 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4351 ... 0.1631 0.2680 -1 None Anscombe_4351 ... 0.1631 0.2680 +0 None Anscombe_4351 ... 0.1574 0.2699 +1 None _4351 ... 0.1661 0.4462 2 None _4351 ... 0.1661 0.4462 3 None Anscombe_4351 ... 0.1700 0.4004 -4 None Anscombe_4351 ... 0.1728 0.2534 +4 None Anscombe_4351 ... 0.1700 0.4004 [5 rows x 8 columns] Forecast Columns Index(['Date', '4351', 'row_number', 'Date_Normalized', '_4351', @@ -6599,31 +7032,33 @@ Forecasts { - "Dataset": { - "Signal": "4351", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4351": { + "Dataset": { + "Signal": "4351", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_4351_Lag1Trend_residue_zeroCycle_residue_AR(16)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_4351_Lag1Trend_residue_zeroCycle_residue_AR(16)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "48", - "MAE": "64.59163539029159", - "MAPE": "0.1661", - "MASE": "0.7335", - "RMSE": "93.08222703131476" + "Model_Performance": { + "COMPLEXITY": "48", + "MAE": "64.59163539029161", + "MAPE": "0.1661", + "MASE": "0.7335", + "RMSE": "93.08222703131477" + } } } @@ -6633,7 +7068,7 @@ Forecasts 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'4352' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4352' 4.2536327838897705 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4352']' 16.578117847442627 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4352' Length=550 Min=210.0 Max=10439.0 Mean=490.55636363636364 StdDev=469.78096202132963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4352' Min=210.0 Max=10439.0 Mean=490.55636363636364 StdDev=469.78096202132963 @@ -6642,36 +7077,45 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_4352_Lag1Trend_residue_Seasonal_DayOfWeek_re INFO:pyaf.std:TREND_DETAIL '_4352_Lag1Trend' [Lag1Trend] INFO:pyaf.std:CYCLE_DETAIL '_4352_Lag1Trend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] INFO:pyaf.std:AUTOREG_DETAIL '_4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2046 MAPE_Forecast=0.1464 MAPE_Test=0.1422 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1892 SMAPE_Forecast=0.14 SMAPE_Test=0.1438 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9039 MASE_Forecast=0.865 MASE_Test=0.8168 -INFO:pyaf.std:MODEL_L1 L1_Fit=132.27123465090057 L1_Forecast=63.80721130702295 L1_Test=70.730777534118 -INFO:pyaf.std:MODEL_L2 L2_Fit=532.0910561070184 L2_Forecast=94.96588763091178 L2_Test=102.36828224868043 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.171 MAPE_Forecast=0.113 MAPE_Test=0.1123 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1584 SMAPE_Forecast=0.1113 SMAPE_Test=0.1144 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.795 MASE_Forecast=0.6484 MASE_Test=0.6176 +INFO:pyaf.std:MODEL_L1 L1_Fit=116.33522767510688 L1_Forecast=47.8304215647765 L1_Test=53.481434113326365 +INFO:pyaf.std:MODEL_L2 L2_Fit=536.2075168010472 L2_Forecast=70.99414923786578 L2_Test=78.53206013605295 INFO:pyaf.std:MODEL_COMPLEXITY 52 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 477.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4352_Lag1Trend_residue_Seasonal_DayOfWeek 0.0 {2: 0.5, 3: -3.0, 4: -48.0, 5: -111.0, 6: 28.0, 0: 123.0, 1: -9.0} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag1 -0.6670299814526817 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag2 -0.5805969286199415 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag3 -0.5040287551496007 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag4 -0.4721542100619868 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag5 -0.4308736301715609 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag6 -0.3964045863071543 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag7 -0.38159106609588633 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag8 -0.31856916442504324 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag9 -0.29282162562013314 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag10 -0.25020482209403694 +INFO:pyaf.std:AR_MODEL_COEFF 1 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag1 -0.6737021137049897 +INFO:pyaf.std:AR_MODEL_COEFF 2 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag2 -0.5862692757429346 +INFO:pyaf.std:AR_MODEL_COEFF 3 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag3 -0.5099513699655378 +INFO:pyaf.std:AR_MODEL_COEFF 4 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag4 -0.47756493315028825 +INFO:pyaf.std:AR_MODEL_COEFF 5 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag5 -0.4367697417274952 +INFO:pyaf.std:AR_MODEL_COEFF 6 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag6 -0.4081873517588188 +INFO:pyaf.std:AR_MODEL_COEFF 7 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag7 -0.37571918168626695 +INFO:pyaf.std:AR_MODEL_COEFF 8 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag8 -0.3230787230687781 +INFO:pyaf.std:AR_MODEL_COEFF 9 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag9 -0.2968217049979618 +INFO:pyaf.std:AR_MODEL_COEFF 10 _4352_Lag1Trend_residue_Seasonal_DayOfWeek_residue_Lag10 -0.2547504495392865 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.679020881652832 +INFO:pyaf.std:START_FORECASTING '['4352']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4352']' 4.111361742019653 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4352 ... 0.1455 0.1414 -1 None Anscombe_4352 ... 0.1455 0.1414 -2 None _4352 ... 0.1464 0.1422 -3 None _4352 ... 0.1464 0.1422 -4 None Anscombe_4352 ... 0.1643 0.1594 +0 None _4352 ... 0.1130 0.1123 +1 None Anscombe_4352 ... 0.1150 0.1218 +2 None Anscombe_4352 ... 0.1168 0.1193 +3 None Anscombe_4352 ... 0.1250 0.1338 +4 None _4352 ... 0.1253 0.1340 [5 rows x 8 columns] Forecast Columns Index(['Date', '4352', 'row_number', 'Date_Normalized', '_4352', @@ -6698,95 +7142,97 @@ memory usage: 14.4 KB None Forecasts Date 4352 4352_Forecast -550 2017-01-01 NaN 319.743848 -551 2017-01-02 NaN 484.985422 -552 2017-01-03 NaN 465.075473 -553 2017-01-04 NaN 435.285850 -554 2017-01-05 NaN 627.135916 -555 2017-01-06 NaN 435.140407 -556 2017-01-07 NaN 326.211614 -557 2017-01-08 NaN 337.940452 -558 2017-01-09 NaN 488.179365 -559 2017-01-10 NaN 467.920270 -560 2017-01-11 NaN 436.926831 -561 2017-01-12 NaN 628.111874 -562 2017-01-13 NaN 440.492023 -563 2017-01-14 NaN 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'4353' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4353']' 18.937693119049072 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4353' Length=550 Min=308.0 Max=5648.0 Mean=716.9345454545454 StdDev=293.2697165150084 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4353' Min=1.224744871391589 Max=2.345207879911715 Mean=1.3421283311487182 StdDev=0.07077820500970299 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [Lag1Trend + Seasonal_DayOfWeek + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Anscombe_4353_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4353_Lag1Trend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] -INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1465 MAPE_Forecast=0.1374 MAPE_Test=0.1262 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1354 SMAPE_Forecast=0.136 SMAPE_Test=0.1254 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7582 MASE_Forecast=0.7084 MASE_Test=0.6514 -INFO:pyaf.std:MODEL_L1 L1_Fit=119.40629092450797 L1_Forecast=86.1115165880053 L1_Test=92.44666367269977 -INFO:pyaf.std:MODEL_L2 L2_Fit=370.95824357544916 L2_Forecast=121.9740855996732 L2_Test=136.93342974776016 -INFO:pyaf.std:MODEL_COMPLEXITY 68 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4353' Min=308.0 Max=5648.0 Mean=716.9345454545454 StdDev=293.2697165150084 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [Lag1Trend + Seasonal_DayOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4353_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_4353_Lag1Trend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1418 MAPE_Forecast=0.1349 MAPE_Test=0.1242 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1316 SMAPE_Forecast=0.1348 SMAPE_Test=0.1243 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7348 MASE_Forecast=0.6953 MASE_Test=0.6428 +INFO:pyaf.std:MODEL_L1 L1_Fit=115.71938775510205 L1_Forecast=84.5204081632653 L1_Test=91.21666666666667 +INFO:pyaf.std:MODEL_L2 L2_Fit=374.45256300201163 L2_Forecast=122.88016810814393 L2_Test=131.91755632464796 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 700.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4353_Lag1Trend_residue_Seasonal_DayOfWeek 2.0 {2: -48.5, 3: 15.0, 4: -69.5, 5: -199.0, 6: 78.5, 0: 179.0, 1: 0.0} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.3648359775543213 +INFO:pyaf.std:START_FORECASTING '['4353']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4353']' 3.428983211517334 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4353 ... 0.1281 0.1132 -1 None Anscombe_4353 ... 0.1281 0.1132 -2 None Anscombe_4353 ... 0.1374 0.1262 -3 None Anscombe_4353 ... 0.1374 0.1262 -4 None _4353 ... 0.1417 0.1158 +0 None Anscombe_4353 ... 0.1287 0.1121 +1 None Anscombe_4353 ... 0.1335 0.1235 +2 None _4353 ... 0.1349 0.1242 +3 None Anscombe_4353 ... 0.1425 0.1180 +4 None _4353 ... 0.1446 0.1164 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4353', 'row_number', 'Date_Normalized', 'Anscombe_4353', - 'Anscombe_4353_Lag1Trend', 'Anscombe_4353_Lag1Trend_residue', - 'Anscombe_4353_Lag1Trend_residue_Seasonal_DayOfWeek', - 'Anscombe_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue', - 'Anscombe_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR', - 'Anscombe_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', - 'Anscombe_4353_Trend', 'Anscombe_4353_Trend_residue', - 'Anscombe_4353_Cycle', 'Anscombe_4353_Cycle_residue', - 'Anscombe_4353_AR', 'Anscombe_4353_AR_residue', - 'Anscombe_4353_TransformedForecast', '4353_Forecast', - 'Anscombe_4353_TransformedResidue', '4353_Residue'], +Forecast Columns Index(['Date', '4353', 'row_number', 'Date_Normalized', '_4353', + '_4353_Lag1Trend', '_4353_Lag1Trend_residue', + '_4353_Lag1Trend_residue_Seasonal_DayOfWeek', + '_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue', + '_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR', + '_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR_residue', + '_4353_Trend', '_4353_Trend_residue', '_4353_Cycle', + '_4353_Cycle_residue', '_4353_AR', '_4353_AR_residue', + '_4353_TransformedForecast', '4353_Forecast', + '_4353_TransformedResidue', '4353_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -6852,95 +7306,97 @@ memory usage: 14.4 KB None Forecasts Date 4353 4353_Forecast -550 2017-01-01 NaN 470.456969 -551 2017-01-02 NaN 653.755672 -552 2017-01-03 NaN 661.416694 -553 2017-01-04 NaN 685.692043 -554 2017-01-05 NaN 656.676644 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2017-01-12 NaN 563.5 +562 2017-01-13 NaN 494.0 +563 2017-01-14 NaN 295.0 +564 2017-01-15 NaN 373.5 +565 2017-01-16 NaN 552.5 +566 2017-01-17 NaN 552.5 +567 2017-01-18 NaN 504.0 +568 2017-01-19 NaN 519.0 +569 2017-01-20 NaN 449.5 +570 2017-01-21 NaN 250.5 +571 2017-01-22 NaN 329.0 +572 2017-01-23 NaN 508.0 +573 2017-01-24 NaN 508.0 +574 2017-01-25 NaN 459.5 +575 2017-01-26 NaN 474.5 +576 2017-01-27 NaN 405.0 +577 2017-01-28 NaN 206.0 +578 2017-01-29 NaN 284.5 +579 2017-01-30 NaN 463.5 +580 2017-01-31 NaN 463.5 +581 2017-02-01 NaN 415.0 +582 2017-02-02 NaN 430.0 +583 2017-02-03 NaN 360.5 +584 2017-02-04 NaN 161.5 +585 2017-02-05 NaN 240.0 +586 2017-02-06 NaN 419.0 +587 2017-02-07 NaN 419.0 +588 2017-02-08 NaN 370.5 +589 2017-02-09 NaN 385.5 +590 2017-02-10 NaN 316.0 +591 2017-02-11 NaN 117.0 +592 2017-02-12 NaN 195.5 +593 2017-02-13 NaN 374.5 +594 2017-02-14 NaN 374.5 +595 2017-02-15 NaN 326.0 +596 2017-02-16 NaN 341.0 +597 2017-02-17 NaN 271.5 +598 2017-02-18 NaN 72.5 +599 2017-02-19 NaN 151.0 +600 2017-02-20 NaN 330.0 +601 2017-02-21 NaN 330.0 +602 2017-02-22 NaN 281.5 +603 2017-02-23 NaN 296.5 +604 2017-02-24 NaN 227.0 +605 2017-02-25 NaN 28.0 +606 2017-02-26 NaN 106.5 +607 2017-02-27 NaN 285.5 +608 2017-02-28 NaN 285.5 +609 2017-03-01 NaN 237.0 { - "Dataset": { - "Signal": "4353", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4353": { + "Dataset": { + "Signal": "4353", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "Anscombe_4353_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "Anscombe", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "68", - "MAE": "86.1115165880053", - "MAPE": "0.1374", - "MASE": "0.7084", - "RMSE": "121.9740855996732" + "Model_Performance": { + "COMPLEXITY": "36", + "MAE": "84.5204081632653", + "MAPE": "0.1349", + "MASE": "0.6953", + "RMSE": "122.88016810814393" + } } } @@ -6949,8 +7405,8 @@ Forecasts 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'4354' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4354']' 17.893507957458496 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4354' Length=550 Min=43.0 Max=39714.0 Mean=249.9618181818182 StdDev=1685.865105246444 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_4354' Min=-39531.0 Max=39492.0 Mean=-0.14363636363636365 StdDev=2383.312664664545 @@ -6965,20 +7421,29 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=1.8422 MASE_Forecast=0.5372 MASE_Test=1.6223 INFO:pyaf.std:MODEL_L1 L1_Fit=82.76280716368181 L1_Forecast=457.900041649313 L1_Test=52.71088435374158 INFO:pyaf.std:MODEL_L2 L2_Fit=101.49189242203168 L2_Forecast=4001.8279818313363 L2_Test=63.41287058670426 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 133.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend -0.061224489795918366 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_4354_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.1294147968292236 +INFO:pyaf.std:START_FORECASTING '['4354']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4354']' 2.096177577972412 Split Transformation ... ForecastMAPE TestMAPE 0 None Diff_4354 ... 0.3731 0.3442 -1 None Anscombe_4354 ... 0.5180 0.4095 -2 None Anscombe_4354 ... 0.5180 0.4095 -3 None _4354 ... 0.5183 0.4098 -4 None _4354 ... 0.5183 0.4098 +1 None Diff_4354 ... 0.3731 0.3442 +2 None Anscombe_4354 ... 0.4028 0.7868 +3 None _4354 ... 0.4868 0.9039 +4 None Anscombe_4354 ... 0.4868 0.9039 [5 rows x 8 columns] Forecast Columns Index(['Date', '4354', 'row_number', 'Date_Normalized', 'Diff_4354', @@ -7069,31 +7534,33 @@ Forecasts { - "Dataset": { - "Signal": "4354", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4354": { + "Dataset": { + "Signal": "4354", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "Diff_4354_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "Difference", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "Diff_4354_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "Difference", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "457.900041649313", - "MAPE": "0.3731", - "MASE": "0.5372", - "RMSE": "4001.8279818313363" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "457.900041649313", + "MAPE": "0.3731", + "MASE": "0.5372", + "RMSE": "4001.8279818313363" + } } } @@ -7103,7 +7570,7 @@ Forecasts {"Date":{"490":"2016-11-02T00:00:00.000Z","491":"2016-11-03T00:00:00.000Z","492":"2016-11-04T00:00:00.000Z","493":"2016-11-05T00:00:00.000Z","494":"2016-11-06T00:00:00.000Z","495":"2016-11-07T00:00:00.000Z","496":"2016-11-08T00:00:00.000Z","497":"2016-11-09T00:00:00.000Z","498":"2016-11-10T00:00:00.000Z","499":"2016-11-11T00:00:00.000Z","500":"2016-11-12T00:00:00.000Z","501":"2016-11-13T00:00:00.000Z","502":"2016-11-14T00:00:00.000Z","503":"2016-11-15T00:00:00.000Z","504":"2016-11-16T00:00:00.000Z","505":"2016-11-17T00:00:00.000Z","506":"2016-11-18T00:00:00.000Z","507":"2016-11-19T00:00:00.000Z","508":"2016-11-20T00:00:00.000Z","509":"2016-11-21T00:00:00.000Z","510":"2016-11-22T00:00:00.000Z","511":"2016-11-23T00:00:00.000Z","512":"2016-11-24T00:00:00.000Z","513":"2016-11-25T00:00:00.000Z","514":"2016-11-26T00:00:00.000Z","515":"2016-11-27T00:00:00.000Z","516":"2016-11-28T00:00:00.000Z","517":"2016-11-29T00:00:00.000Z","518":"2016-11-30T00:00:00.000Z","519":"2016-12-01T00:00:00.000Z","520":"2016-12-02T00:00:00.000Z","521":"2016-12-03T00:00:00.000Z","522":"2016-12-04T00:00:00.000Z","523":"2016-12-05T00:00:00.000Z","524":"2016-12-06T00:00:00.000Z","525":"2016-12-07T00:00:00.000Z","526":"2016-12-08T00:00:00.000Z","527":"2016-12-09T00:00:00.000Z","528":"2016-12-10T00:00:00.000Z","529":"2016-12-11T00:00:00.000Z","530":"2016-12-12T00:00:00.000Z","531":"2016-12-13T00:00:00.000Z","532":"2016-12-14T00:00:00.000Z","533":"2016-12-15T00:00:00.000Z","534":"2016-12-16T00:00:00.000Z","535":"2016-12-17T00:00:00.000Z","536":"2016-12-18T00:00:00.000Z","537":"2016-12-19T00:00:00.000Z","538":"2016-12-20T00:00:00.000Z","539":"2016-12-21T00:00:00.000Z","540":"2016-12-22T00:00:00.000Z","541":"2016-12-23T00:00:00.000Z","542":"2016-12-24T00:00:00.000Z","543":"2016-12-25T00:00:00.000Z","544":"2016-12-26T00:00:00.000Z","545":"2016-12-27T00:00:00.000Z","546":"2016-12-28T00:00:00.000Z","547":"2016-12-29T00:00:00.000Z","548":"2016-12-30T00:00:00.000Z","549":"2016-12-31T00:00:00.000Z","550":"2017-01-01T00:00:00.000Z","551":"2017-01-02T00:00:00.000Z","552":"2017-01-03T00:00:00.000Z","553":"2017-01-04T00:00:00.000Z","554":"2017-01-05T00:00:00.000Z","555":"2017-01-06T00:00:00.000Z","556":"2017-01-07T00:00:00.000Z","557":"2017-01-08T00:00:00.000Z","558":"2017-01-09T00:00:00.000Z","559":"2017-01-10T00:00:00.000Z","560":"2017-01-11T00:00:00.000Z","561":"2017-01-12T00:00:00.000Z","562":"2017-01-13T00:00:00.000Z","563":"2017-01-14T00:00:00.000Z","564":"2017-01-15T00:00:00.000Z","565":"2017-01-16T00:00:00.000Z","566":"2017-01-17T00:00:00.000Z","567":"2017-01-18T00:00:00.000Z","568":"2017-01-19T00:00:00.000Z","569":"2017-01-20T00:00:00.000Z","570":"2017-01-21T00:00:00.000Z","571":"2017-01-22T00:00:00.000Z","572":"2017-01-23T00:00:00.000Z","573":"2017-01-24T00:00:00.000Z","574":"2017-01-25T00:00:00.000Z","575":"2017-01-26T00:00:00.000Z","576":"2017-01-27T00:00:00.000Z","577":"2017-01-28T00:00:00.000Z","578":"2017-01-29T00:00:00.000Z","579":"2017-01-30T00:00:00.000Z","580":"2017-01-31T00:00:00.000Z","581":"2017-02-01T00:00:00.000Z","582":"2017-02-02T00:00:00.000Z","583":"2017-02-03T00:00:00.000Z","584":"2017-02-04T00:00:00.000Z","585":"2017-02-05T00:00:00.000Z","586":"2017-02-06T00:00:00.000Z","587":"2017-02-07T00:00:00.000Z","588":"2017-02-08T00:00:00.000Z","589":"2017-02-09T00:00:00.000Z","590":"2017-02-10T00:00:00.000Z","591":"2017-02-11T00:00:00.000Z","592":"2017-02-12T00:00:00.000Z","593":"2017-02-13T00:00:00.000Z","594":"2017-02-14T00:00:00.000Z","595":"2017-02-15T00:00:00.000Z","596":"2017-02-16T00:00:00.000Z","597":"2017-02-17T00:00:00.000Z","598":"2017-02-18T00:00:00.000Z","599":"2017-02-19T00:00:00.000Z","600":"2017-02-20T00:00:00.000Z","601":"2017-02-21T00:00:00.000Z","602":"2017-02-22T00:00:00.000Z","603":"2017-02-23T00:00:00.000Z","604":"2017-02-24T00:00:00.000Z","605":"2017-02-25T00:00:00.000Z","606":"2017-02-26T00:00:00.000Z","607":"2017-02-27T00:00:00.000Z","608":"2017-02-28T00:00:00.000Z","609":"2017-03-01T00:00:00.000Z"},"4354":{"490":223.0,"491":218.0,"492":164.0,"493":111.0,"494":142.0,"495":223.0,"496":239.0,"497":172.0,"498":148.0,"499":116.0,"500":115.0,"501":128.0,"502":196.0,"503":203.0,"504":214.0,"505":187.0,"506":152.0,"507":87.0,"508":132.0,"509":188.0,"510":177.0,"511":190.0,"512":155.0,"513":161.0,"514":85.0,"515":125.0,"516":207.0,"517":198.0,"518":157.0,"519":157.0,"520":146.0,"521":89.0,"522":133.0,"523":184.0,"524":194.0,"525":142.0,"526":194.0,"527":167.0,"528":86.0,"529":128.0,"530":202.0,"531":167.0,"532":174.0,"533":159.0,"534":120.0,"535":61.0,"536":79.0,"537":131.0,"538":131.0,"539":126.0,"540":109.0,"541":62.0,"542":62.0,"543":64.0,"544":103.0,"545":114.0,"546":117.0,"547":103.0,"548":89.0,"549":54.0,"550":null,"551":null,"552":null,"553":null,"554":null,"555":null,"556":null,"557":null,"558":null,"559":null,"560":null,"561":null,"562":null,"563":null,"564":null,"565":null,"566":null,"567":null,"568":null,"569":null,"570":null,"571":null,"572":null,"573":null,"574":null,"575":null,"576":null,"577":null,"578"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'4355' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4355' 4.0622718334198 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4355']' 16.047382831573486 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4355' Length=550 Min=195.0 Max=1064.0 Mean=472.74545454545455 StdDev=104.61411658294377 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4355' Min=195.0 Max=1064.0 Mean=472.74545454545455 StdDev=104.61411658294377 @@ -7112,26 +7579,35 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_4355_ConstantTrend_residue_Seasonal_DayOfWee INFO:pyaf.std:TREND_DETAIL '_4355_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_4355_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] INFO:pyaf.std:AUTOREG_DETAIL '_4355_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1169 MAPE_Forecast=0.0927 MAPE_Test=0.1867 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1128 SMAPE_Forecast=0.0902 SMAPE_Test=0.163 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7036 MASE_Forecast=0.5404 MASE_Test=0.9636 -INFO:pyaf.std:MODEL_L1 L1_Fit=54.238975947521865 L1_Forecast=44.83126822157435 L1_Test=70.50833333333335 -INFO:pyaf.std:MODEL_L2 L2_Fit=78.07948324209612 L2_Forecast=75.24125858490297 L2_Test=84.18705630056455 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1156 MAPE_Forecast=0.0906 MAPE_Test=0.1809 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1118 SMAPE_Forecast=0.0884 SMAPE_Test=0.1579 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.697 MASE_Forecast=0.5314 MASE_Test=0.9308 +INFO:pyaf.std:MODEL_L1 L1_Fit=53.734693877551024 L1_Forecast=44.08163265306123 L1_Test=68.10833333333333 +INFO:pyaf.std:MODEL_L2 L2_Fit=78.51705911092552 L2_Forecast=75.94745095210536 L2_Test=82.28145295265513 INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 481.4030612244898 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4355_ConstantTrend_residue_Seasonal_DayOfWeek -2.403061224489818 {2: 48.59693877551018, 3: 18.596938775510182, 4: -11.903061224489818, 5: -134.40306122448982, 6: -69.40306122448982, 0: 68.09693877551018, 1: 72.09693877551018} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.0713505744934082 +INFO:pyaf.std:START_FORECASTING '['4355']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4355']' 1.836937427520752 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4355 ... 0.0858 0.1564 -1 None Diff_4355 ... 0.0858 0.1564 -2 None Anscombe_4355 ... 0.0912 0.1822 -3 None Anscombe_4355 ... 0.0912 0.1822 -4 None _4355 ... 0.0927 0.1867 +0 None _4355 ... 0.0906 0.1809 +1 None Anscombe_4355 ... 0.0906 0.1809 +2 None Anscombe_4355 ... 0.0933 0.1071 +3 None Anscombe_4355 ... 0.0946 0.1780 +4 None _4355 ... 0.0947 0.1780 [5 rows x 8 columns] Forecast Columns Index(['Date', '4355', 'row_number', 'Date_Normalized', '_4355', @@ -7158,95 +7634,97 @@ memory usage: 14.4 KB None Forecasts Date 4355 4355_Forecast -550 2017-01-01 NaN 407.250000 -551 2017-01-02 NaN 551.714286 -552 2017-01-03 NaN 542.250000 -553 2017-01-04 NaN 543.017857 -554 2017-01-05 NaN 508.482143 -555 2017-01-06 NaN 463.321429 -556 2017-01-07 NaN 353.785714 -557 2017-01-08 NaN 407.250000 -558 2017-01-09 NaN 551.714286 -559 2017-01-10 NaN 542.250000 -560 2017-01-11 NaN 543.017857 -561 2017-01-12 NaN 508.482143 -562 2017-01-13 NaN 463.321429 -563 2017-01-14 NaN 353.785714 -564 2017-01-15 NaN 407.250000 -565 2017-01-16 NaN 551.714286 -566 2017-01-17 NaN 542.250000 -567 2017-01-18 NaN 543.017857 -568 2017-01-19 NaN 508.482143 -569 2017-01-20 NaN 463.321429 -570 2017-01-21 NaN 353.785714 -571 2017-01-22 NaN 407.250000 -572 2017-01-23 NaN 551.714286 -573 2017-01-24 NaN 542.250000 -574 2017-01-25 NaN 543.017857 -575 2017-01-26 NaN 508.482143 -576 2017-01-27 NaN 463.321429 -577 2017-01-28 NaN 353.785714 -578 2017-01-29 NaN 407.250000 -579 2017-01-30 NaN 551.714286 -580 2017-01-31 NaN 542.250000 -581 2017-02-01 NaN 543.017857 -582 2017-02-02 NaN 508.482143 -583 2017-02-03 NaN 463.321429 -584 2017-02-04 NaN 353.785714 -585 2017-02-05 NaN 407.250000 -586 2017-02-06 NaN 551.714286 -587 2017-02-07 NaN 542.250000 -588 2017-02-08 NaN 543.017857 -589 2017-02-09 NaN 508.482143 -590 2017-02-10 NaN 463.321429 -591 2017-02-11 NaN 353.785714 -592 2017-02-12 NaN 407.250000 -593 2017-02-13 NaN 551.714286 -594 2017-02-14 NaN 542.250000 -595 2017-02-15 NaN 543.017857 -596 2017-02-16 NaN 508.482143 -597 2017-02-17 NaN 463.321429 -598 2017-02-18 NaN 353.785714 -599 2017-02-19 NaN 407.250000 -600 2017-02-20 NaN 551.714286 -601 2017-02-21 NaN 542.250000 -602 2017-02-22 NaN 543.017857 -603 2017-02-23 NaN 508.482143 -604 2017-02-24 NaN 463.321429 -605 2017-02-25 NaN 353.785714 -606 2017-02-26 NaN 407.250000 -607 2017-02-27 NaN 551.714286 -608 2017-02-28 NaN 542.250000 -609 2017-03-01 NaN 543.017857 +550 2017-01-01 NaN 412.0 +551 2017-01-02 NaN 549.5 +552 2017-01-03 NaN 553.5 +553 2017-01-04 NaN 530.0 +554 2017-01-05 NaN 500.0 +555 2017-01-06 NaN 469.5 +556 2017-01-07 NaN 347.0 +557 2017-01-08 NaN 412.0 +558 2017-01-09 NaN 549.5 +559 2017-01-10 NaN 553.5 +560 2017-01-11 NaN 530.0 +561 2017-01-12 NaN 500.0 +562 2017-01-13 NaN 469.5 +563 2017-01-14 NaN 347.0 +564 2017-01-15 NaN 412.0 +565 2017-01-16 NaN 549.5 +566 2017-01-17 NaN 553.5 +567 2017-01-18 NaN 530.0 +568 2017-01-19 NaN 500.0 +569 2017-01-20 NaN 469.5 +570 2017-01-21 NaN 347.0 +571 2017-01-22 NaN 412.0 +572 2017-01-23 NaN 549.5 +573 2017-01-24 NaN 553.5 +574 2017-01-25 NaN 530.0 +575 2017-01-26 NaN 500.0 +576 2017-01-27 NaN 469.5 +577 2017-01-28 NaN 347.0 +578 2017-01-29 NaN 412.0 +579 2017-01-30 NaN 549.5 +580 2017-01-31 NaN 553.5 +581 2017-02-01 NaN 530.0 +582 2017-02-02 NaN 500.0 +583 2017-02-03 NaN 469.5 +584 2017-02-04 NaN 347.0 +585 2017-02-05 NaN 412.0 +586 2017-02-06 NaN 549.5 +587 2017-02-07 NaN 553.5 +588 2017-02-08 NaN 530.0 +589 2017-02-09 NaN 500.0 +590 2017-02-10 NaN 469.5 +591 2017-02-11 NaN 347.0 +592 2017-02-12 NaN 412.0 +593 2017-02-13 NaN 549.5 +594 2017-02-14 NaN 553.5 +595 2017-02-15 NaN 530.0 +596 2017-02-16 NaN 500.0 +597 2017-02-17 NaN 469.5 +598 2017-02-18 NaN 347.0 +599 2017-02-19 NaN 412.0 +600 2017-02-20 NaN 549.5 +601 2017-02-21 NaN 553.5 +602 2017-02-22 NaN 530.0 +603 2017-02-23 NaN 500.0 +604 2017-02-24 NaN 469.5 +605 2017-02-25 NaN 347.0 +606 2017-02-26 NaN 412.0 +607 2017-02-27 NaN 549.5 +608 2017-02-28 NaN 553.5 +609 2017-03-01 NaN 530.0 { - "Dataset": { - "Signal": "4355", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4355": { + "Dataset": { + "Signal": "4355", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_4355_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", - "Cycle": "Seasonal_DayOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "44.83126822157435", - "MAPE": "0.0927", - "MASE": "0.5404", - "RMSE": "75.24125858490297" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4355_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR", + "Cycle": "Seasonal_DayOfWeek", + "Signal_Transoformation": "NoTransf", + 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'_4356_LinearTrend_residue_zeroCycle_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1612 MAPE_Forecast=0.2018 MAPE_Test=0.1297 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1561 SMAPE_Forecast=0.2033 SMAPE_Test=0.1317 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8288 MASE_Forecast=0.7901 MASE_Test=0.8463 -INFO:pyaf.std:MODEL_L1 L1_Fit=80.61239454314737 L1_Forecast=149.54150586486165 L1_Test=55.11292663685314 -INFO:pyaf.std:MODEL_L2 L2_Fit=211.63312125244147 L2_Forecast=585.2613207439848 L2_Test=68.5188390434117 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:CYCLE_DETAIL '_4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth' [Seasonal_DayOfNthWeekOfMonth] +INFO:pyaf.std:AUTOREG_DETAIL '_4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(16)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1276 MAPE_Forecast=0.1896 MAPE_Test=0.1347 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.125 SMAPE_Forecast=0.1964 SMAPE_Test=0.1423 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6712 MASE_Forecast=0.773 MASE_Test=0.9043 +INFO:pyaf.std:MODEL_L1 L1_Fit=65.28469674838416 L1_Forecast=146.30216997162407 L1_Test=58.88732425182855 +INFO:pyaf.std:MODEL_L2 L2_Fit=201.4787576188944 L2_Forecast=583.0250919984645 L2_Test=73.98874854080638 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (531.8215097551606, array([-135.14812155])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth -21.21837259176752 {9: 18.58905114152867, 10: 34.753832547720094, 11: -49.33450158567166, 12: -140.42283571906341, 13: -37.20922534319192, 14: 21.20244052341633, 15: 2.982069396200103, 16: -4.94532791293571, 17: -1.648509512919759, 18: -59.463798967479704, 19: -90.57206224012009, 20: -37.67758590074402, 21: 38.00986425671249, 22: 19.289493129496265, 23: -14.781651476663285, 24: 23.480787868888285, 25: -62.33450158567166, 26: -140.31072786448755, 27: -50.19203581595971, 28: 27.153611553736255, 29: 0.8871510688564115, 30: -13.948067524952194, 31: -61.47038316143164, 32: -73.62473579173565, 33: -95.61815159778371, 34: -72.04554890691949, 35: -64.19990153722352, 36: -48.81090442188031, 37: -9.396498943255637, 38: 72.24212224452083, 39: 28.748706438472766, 40: -85.65253065224806, 41: 27.91733242659967, 42: 41.26297979629567, 8: 118.67738527492043, -357: 47.814548377992196, -356: 78.16019574768825, -355: 131.5058431173842, -354: 25.85149048708024, -353: 151.19713785677624, -352: 40.54278522647223, -351: 232.88843259616823, -350: 170.23407996586423, -349: 145.57972733556022, -348: 129.92537470525622, -347: 138.27102207495227, -346: 154.61666944464827, -345: 197.96231681434426, -344: 193.30796418404026, -343: 161.65361155373625, -342: 142.9992589234323, -341: 122.3449062931283, -340: 138.6905536628243, -339: 177.0362010325203, -338: 33.38184840221629, -337: 511.7274957719123, -336: 535.0731431416083, -335: 153.41879051130434, -334: 143.76443788100033, -333: 120.11008525069633, -332: 17.455732620392325, -331: -53.19862000991168, -330: 366.1470273597843, 7: 115.49267472948031, 43: 114.97035909300087} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _4356_LinearTrend_residue_zeroCycle_residue_Lag1 0.31448018841373604 -INFO:pyaf.std:AR_MODEL_COEFF 2 _4356_LinearTrend_residue_zeroCycle_residue_Lag7 0.0669724153432826 -INFO:pyaf.std:AR_MODEL_COEFF 3 _4356_LinearTrend_residue_zeroCycle_residue_Lag14 0.056006835845109126 -INFO:pyaf.std:AR_MODEL_COEFF 4 _4356_LinearTrend_residue_zeroCycle_residue_Lag13 0.05185842960224231 -INFO:pyaf.std:AR_MODEL_COEFF 5 _4356_LinearTrend_residue_zeroCycle_residue_Lag12 -0.047941101551343346 -INFO:pyaf.std:AR_MODEL_COEFF 6 _4356_LinearTrend_residue_zeroCycle_residue_Lag11 0.04356911962263043 -INFO:pyaf.std:AR_MODEL_COEFF 7 _4356_LinearTrend_residue_zeroCycle_residue_Lag4 0.041978518991740855 -INFO:pyaf.std:AR_MODEL_COEFF 8 _4356_LinearTrend_residue_zeroCycle_residue_Lag3 0.0414721313556744 -INFO:pyaf.std:AR_MODEL_COEFF 9 _4356_LinearTrend_residue_zeroCycle_residue_Lag5 0.03384105043874579 -INFO:pyaf.std:AR_MODEL_COEFF 10 _4356_LinearTrend_residue_zeroCycle_residue_Lag6 0.01869068817203775 +INFO:pyaf.std:AR_MODEL_COEFF 1 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag1 0.2905955424699138 +INFO:pyaf.std:AR_MODEL_COEFF 2 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag4 0.04490708166253987 +INFO:pyaf.std:AR_MODEL_COEFF 3 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag9 0.04041340071988652 +INFO:pyaf.std:AR_MODEL_COEFF 4 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag16 0.040367194125103395 +INFO:pyaf.std:AR_MODEL_COEFF 5 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag13 0.0402319732922648 +INFO:pyaf.std:AR_MODEL_COEFF 6 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag2 0.03750483746413183 +INFO:pyaf.std:AR_MODEL_COEFF 7 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag11 0.03748511414299354 +INFO:pyaf.std:AR_MODEL_COEFF 8 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag15 0.03528283546747481 +INFO:pyaf.std:AR_MODEL_COEFF 9 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag5 0.02440988569762002 +INFO:pyaf.std:AR_MODEL_COEFF 10 _4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag3 0.02412837307628337 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.697066068649292 +INFO:pyaf.std:START_FORECASTING '['4356']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4356']' 3.5120813846588135 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4356 ... 0.1943 0.1335 -1 None Anscombe_4356 ... 0.1956 0.1265 -2 None Anscombe_4356 ... 0.1956 0.1265 -3 None _4356 ... 0.2018 0.1297 -4 None _4356 ... 0.2060 0.1293 +0 None Anscombe_4356 ... 0.1837 0.1418 +1 None Anscombe_4356 ... 0.1852 0.1189 +2 None _4356 ... 0.1896 0.1347 +3 None Anscombe_4356 ... 0.1923 0.1402 +4 None _4356 ... 0.1937 0.1135 [5 rows x 8 columns] Forecast Columns Index(['Date', '4356', 'row_number', 'Date_Normalized', '_4356', '_4356_LinearTrend', '_4356_LinearTrend_residue', - '_4356_LinearTrend_residue_zeroCycle', - '_4356_LinearTrend_residue_zeroCycle_residue', - '_4356_LinearTrend_residue_zeroCycle_residue_AR(16)', - '_4356_LinearTrend_residue_zeroCycle_residue_AR(16)_residue', + '_4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth', + '_4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue', + '_4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(16)', + '_4356_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(16)_residue', '_4356_Trend', '_4356_Trend_residue', '_4356_Cycle', '_4356_Cycle_residue', '_4356_AR', '_4356_AR_residue', '_4356_TransformedForecast', '4356_Forecast', @@ -7321,95 +7808,97 @@ memory usage: 14.4 KB None Forecasts Date 4356 4356_Forecast -550 2017-01-01 NaN 329.131381 -551 2017-01-02 NaN 347.149195 -552 2017-01-03 NaN 342.546057 -553 2017-01-04 NaN 345.868522 -554 2017-01-05 NaN 350.525700 -555 2017-01-06 NaN 342.305873 -556 2017-01-07 NaN 332.065201 -557 2017-01-08 NaN 327.727671 -558 2017-01-09 NaN 340.595418 -559 2017-01-10 NaN 339.734505 -560 2017-01-11 NaN 341.232300 -561 2017-01-12 NaN 347.324085 -562 2017-01-13 NaN 341.049845 -563 2017-01-14 NaN 336.348166 -564 2017-01-15 NaN 336.726807 -565 2017-01-16 NaN 338.015401 -566 2017-01-17 NaN 338.106128 -567 2017-01-18 NaN 338.439037 -568 2017-01-19 NaN 338.871157 -569 2017-01-20 NaN 338.340761 -570 2017-01-21 NaN 336.361210 -571 2017-01-22 NaN 335.992641 -572 2017-01-23 NaN 336.407693 -573 2017-01-24 NaN 335.903062 -574 2017-01-25 NaN 336.230157 -575 2017-01-26 NaN 336.450796 -576 2017-01-27 NaN 335.884185 -577 2017-01-28 NaN 335.155193 -578 2017-01-29 NaN 334.733385 -579 2017-01-30 NaN 334.499539 -580 2017-01-31 NaN 334.217575 -581 2017-02-01 NaN 333.972782 -582 2017-02-02 NaN 333.815116 -583 2017-02-03 NaN 333.473832 -584 2017-02-04 NaN 332.996715 -585 2017-02-05 NaN 332.672543 -586 2017-02-06 NaN 332.351275 -587 2017-02-07 NaN 332.017347 -588 2017-02-08 NaN 331.750636 -589 2017-02-09 NaN 331.484931 -590 2017-02-10 NaN 331.160985 -591 2017-02-11 NaN 330.797590 -592 2017-02-12 NaN 330.451057 -593 2017-02-13 NaN 330.119138 -594 2017-02-14 NaN 329.782321 -595 2017-02-15 NaN 329.458716 -596 2017-02-16 NaN 329.146388 -597 2017-02-17 NaN 328.811646 -598 2017-02-18 NaN 328.467238 -599 2017-02-19 NaN 328.129082 -600 2017-02-20 NaN 327.789669 -601 2017-02-21 NaN 327.451028 -602 2017-02-22 NaN 327.117789 -603 2017-02-23 NaN 326.787115 -604 2017-02-24 NaN 326.451311 -605 2017-02-25 NaN 326.110667 -606 2017-02-26 NaN 325.770265 -607 2017-02-27 NaN 325.429738 -608 2017-02-28 NaN 325.088315 -609 2017-03-01 NaN 324.748824 +550 2017-01-01 NaN 333.612178 +551 2017-01-02 NaN 542.176404 +552 2017-01-03 NaN 508.034212 +553 2017-01-04 NaN 498.376360 +554 2017-01-05 NaN 503.793378 +555 2017-01-06 NaN 521.433018 +556 2017-01-07 NaN 566.220655 +557 2017-01-08 NaN 554.053746 +558 2017-01-09 NaN 523.616539 +559 2017-01-10 NaN 502.013621 +560 2017-01-11 NaN 486.278633 +561 2017-01-12 NaN 500.696126 +562 2017-01-13 NaN 535.701706 +563 2017-01-14 NaN 389.208967 +564 2017-01-15 NaN 868.255424 +565 2017-01-16 NaN 892.843163 +566 2017-01-17 NaN 511.133617 +567 2017-01-18 NaN 500.950870 +568 2017-01-19 NaN 476.472433 +569 2017-01-20 NaN 373.940870 +570 2017-01-21 NaN 302.489875 +571 2017-01-22 NaN 721.621992 +572 2017-01-23 NaN 333.512232 +573 2017-01-24 NaN 333.017955 +574 2017-01-25 NaN 332.518298 +575 2017-01-26 NaN 332.110763 +576 2017-01-27 NaN 331.793506 +577 2017-01-28 NaN 331.283584 +578 2017-01-29 NaN 330.741684 +579 2017-01-30 NaN 330.239602 +580 2017-01-31 NaN 329.950378 +581 2017-02-01 NaN 369.422758 +582 2017-02-02 NaN 385.256460 +583 2017-02-03 NaN 300.752720 +584 2017-02-04 NaN 209.291223 +585 2017-02-05 NaN 312.128787 +586 2017-02-06 NaN 370.150754 +587 2017-02-07 NaN 351.544979 +588 2017-02-08 NaN 343.221446 +589 2017-02-09 NaN 346.139935 +590 2017-02-10 NaN 287.943707 +591 2017-02-11 NaN 256.465818 +592 2017-02-12 NaN 308.986020 +593 2017-02-13 NaN 384.301709 +594 2017-02-14 NaN 365.205847 +595 2017-02-15 NaN 330.769561 +596 2017-02-16 NaN 368.673273 +597 2017-02-17 NaN 282.500495 +598 2017-02-18 NaN 204.165868 +599 2017-02-19 NaN 293.923451 +600 2017-02-20 NaN 370.910557 +601 2017-02-21 NaN 344.285013 +602 2017-02-22 NaN 329.091544 +603 2017-02-23 NaN 281.210708 +604 2017-02-24 NaN 268.699163 +605 2017-02-25 NaN 246.349434 +606 2017-02-26 NaN 269.566813 +607 2017-02-27 NaN 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'4357' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4357']' 14.176506996154785 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4357' Length=550 Min=22.0 Max=441.0 Mean=50.28545454545455 StdDev=28.507484938532567 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4357' Min=1.224744871391589 Max=2.345207879911715 Mean=1.3277294325739504 StdDev=0.08463308786394161 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_AR(16)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL 'Anscombe_4357_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4357_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_AR(16)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2016 MAPE_Forecast=0.2579 MAPE_Test=0.166 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1936 SMAPE_Forecast=0.2228 SMAPE_Test=0.1647 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8015 MASE_Forecast=0.9167 MASE_Test=0.8691 -INFO:pyaf.std:MODEL_L1 L1_Fit=11.06120973131924 L1_Forecast=13.02308886961737 L1_Test=8.999931100777037 -INFO:pyaf.std:MODEL_L2 L2_Fit=23.63969922906546 L2_Forecast=32.17829643211005 L2_Test=13.085634175339486 -INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4357' Min=22.0 Max=441.0 Mean=50.28545454545455 StdDev=28.507484938532567 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_4357_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [ConstantTrend + Seasonal_WeekOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_4357_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_4357_ConstantTrend_residue_Seasonal_WeekOfYear' [Seasonal_WeekOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_4357_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1613 MAPE_Forecast=0.1945 MAPE_Test=0.1779 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.162 SMAPE_Forecast=0.1947 SMAPE_Test=0.1854 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6917 MASE_Forecast=0.7987 MASE_Test=0.9705 +INFO:pyaf.std:MODEL_L1 L1_Fit=9.545918367346939 L1_Forecast=11.346938775510203 L1_Test=10.05 +INFO:pyaf.std:MODEL_L2 L2_Fit=24.334798087495685 L2_Forecast=34.301186984876026 L2_Test=15.418062999828049 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 51.255102040816325 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _4357_ConstantTrend_residue_Seasonal_WeekOfYear -4.255102040816325 {27: -9.255102040816325, 28: -3.2551020408163254, 29: -0.7551020408163254, 30: -7.255102040816325, 31: -3.2551020408163254, 32: -10.255102040816325, 33: -12.255102040816325, 34: -8.255102040816325, 35: -13.255102040816325, 36: -7.255102040816325, 37: -16.255102040816325, 38: -17.255102040816325, 39: -10.255102040816325, 40: -12.255102040816325, 41: -5.255102040816325, 42: -12.255102040816325, 43: -4.255102040816325, 44: -4.255102040816325, 45: -5.255102040816325, 46: -0.25510204081632537, 47: -2.2551020408163254, 48: -5.255102040816325, 49: -11.255102040816325, 50: -1.2551020408163254, 51: -6.255102040816325, 52: 3.7448979591836746, 53: 38.744897959183675, 1: 23.744897959183675, 2: 13.744897959183675, 3: 3.7448979591836746, 4: 3.7448979591836746, 5: -1.2551020408163254, 6: 5.744897959183675, 7: 4.744897959183675, 8: 2.7448979591836746, 9: 5.744897959183675, 10: 10.744897959183675, 11: -5.255102040816325, 12: -10.255102040816325, 13: 3.7448979591836746, 14: -0.25510204081632537, 15: -3.2551020408163254, 16: -1.2551020408163254, 17: 24.744897959183675, 18: -10.255102040816325, 19: -9.255102040816325, 20: -9.255102040816325, 21: -8.255102040816325, 22: -7.255102040816325, 23: -10.255102040816325, 24: -16.255102040816325, 25: -8.255102040816325, 26: -8.255102040816325} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3497175384927793 -INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag2 0.10616123311696365 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag13 0.06388386936394763 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag6 0.04081896142760744 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag15 0.03384756084721026 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.03006426142317488 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag7 0.028650212549469603 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.026328725046308427 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag12 0.02388425224568736 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_Lag4 0.023866774953405917 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 2.028247356414795 +INFO:pyaf.std:START_FORECASTING '['4357']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4357']' 2.0722806453704834 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4357 ... 0.2481 0.4615 -1 None Anscombe_4357 ... 0.2497 0.4634 -2 None Anscombe_4357 ... 0.2579 0.1660 -3 None Anscombe_4357 ... 0.2602 0.1675 -4 None Anscombe_4357 ... 0.2611 0.1727 +0 None _4357 ... 0.1945 0.1779 +1 None Anscombe_4357 ... 0.1945 0.1779 +2 None _4357 ... 0.1969 0.1787 +3 None Anscombe_4357 ... 0.1972 0.1789 +4 None _4357 ... 0.2221 0.1718 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4357', 'row_number', 'Date_Normalized', 'Anscombe_4357', - 'Anscombe_4357_ConstantTrend', 'Anscombe_4357_ConstantTrend_residue', - 'Anscombe_4357_ConstantTrend_residue_zeroCycle', - 'Anscombe_4357_ConstantTrend_residue_zeroCycle_residue', - 'Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_AR(16)', - 'Anscombe_4357_ConstantTrend_residue_zeroCycle_residue_AR(16)_residue', - 'Anscombe_4357_Trend', 'Anscombe_4357_Trend_residue', - 'Anscombe_4357_Cycle', 'Anscombe_4357_Cycle_residue', - 'Anscombe_4357_AR', 'Anscombe_4357_AR_residue', - 'Anscombe_4357_TransformedForecast', '4357_Forecast', - 'Anscombe_4357_TransformedResidue', '4357_Residue'], +Forecast Columns Index(['Date', '4357', 'row_number', 'Date_Normalized', '_4357', + '_4357_ConstantTrend', '_4357_ConstantTrend_residue', + '_4357_ConstantTrend_residue_Seasonal_WeekOfYear', + '_4357_ConstantTrend_residue_Seasonal_WeekOfYear_residue', + '_4357_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR', + '_4357_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR_residue', + '_4357_Trend', '_4357_Trend_residue', '_4357_Cycle', + '_4357_Cycle_residue', '_4357_AR', '_4357_AR_residue', + '_4357_TransformedForecast', '4357_Forecast', + '_4357_TransformedResidue', '4357_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -7485,95 +7972,97 @@ memory usage: 14.4 KB None Forecasts Date 4357 4357_Forecast -550 2017-01-01 NaN 45.968457 -551 2017-01-02 NaN 47.776278 -552 2017-01-03 NaN 49.159952 -553 2017-01-04 NaN 49.762417 -554 2017-01-05 NaN 48.993090 -555 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L2_Forecast=135.1412635002199 L2_Test=72.17783420924134 +INFO:pyaf.std:MODEL_L1 L1_Fit=100.58000458856664 L1_Forecast=79.45938910670283 L1_Test=55.959831534950084 +INFO:pyaf.std:MODEL_L2 L2_Fit=194.24861072663654 L2_Forecast=135.1412635002199 L2_Test=72.17783420924138 INFO:pyaf.std:MODEL_COMPLEXITY 16 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (623.3890818100617, array([-176.10469423])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _4358_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.140197992324829 +INFO:pyaf.std:START_FORECASTING '['4358']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4358']' 1.6631550788879395 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_4358 ... 0.1348 0.1118 -1 None Anscombe_4358 ... 0.1361 0.1118 -2 None Anscombe_4358 ... 0.1361 0.1118 -3 None Anscombe_4358 ... 0.1375 0.1185 -4 None _4358 ... 0.1377 0.1209 +0 None Anscombe_4358 ... 0.1333 0.1017 +1 None Diff_4358 ... 0.1348 0.1118 +2 None Diff_4358 ... 0.1348 0.1118 +3 None _4358 ... 0.1372 0.1033 +4 None Anscombe_4358 ... 0.1375 0.1185 [5 rows x 8 columns] Forecast Columns Index(['Date', '4358', 'row_number', 'Date_Normalized', '_4358', @@ -7702,31 +8200,33 @@ Forecasts { - "Dataset": { - "Signal": "4358", - "Time": { - "Horizon": 60, - "TimeMinMax": [ - "2015-07-01 00:00:00", - "2016-12-31 00:00:00" - ], - "TimeVariable": "Date" + "4358": { + "Dataset": { + "Signal": "4358", + "Time": { + "Horizon": 60, + "TimeMinMax": [ + "2015-07-01 00:00:00", + "2016-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 550 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_4358_LinearTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 550 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_4358_LinearTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "16", - "MAE": "79.45938910670283", - "MAPE": "0.1377", - "MASE": "0.9294", - "RMSE": "135.1412635002199" + "Model_Performance": { + "COMPLEXITY": "16", + "MAE": "79.45938910670283", + "MAPE": "0.1377", + "MASE": "0.9294", + "RMSE": "135.1412635002199" + } } } @@ -7736,46 +8236,67 @@ Forecasts {"Date":{"490":"2016-11-02T00:00:00.000Z","491":"2016-11-03T00:00:00.000Z","492":"2016-11-04T00:00:00.000Z","493":"2016-11-05T00:00:00.000Z","494":"2016-11-06T00:00:00.000Z","495":"2016-11-07T00:00:00.000Z","496":"2016-11-08T00:00:00.000Z","497":"2016-11-09T00:00:00.000Z","498":"2016-11-10T00:00:00.000Z","499":"2016-11-11T00:00:00.000Z","500":"2016-11-12T00:00:00.000Z","501":"2016-11-13T00:00:00.000Z","502":"2016-11-14T00:00:00.000Z","503":"2016-11-15T00:00:00.000Z","504":"2016-11-16T00:00:00.000Z","505":"2016-11-17T00:00:00.000Z","506":"2016-11-18T00:00:00.000Z","507":"2016-11-19T00:00:00.000Z","508":"2016-11-20T00:00:00.000Z","509":"2016-11-21T00:00:00.000Z","510":"2016-11-22T00:00:00.000Z","511":"2016-11-23T00:00:00.000Z","512":"2016-11-24T00:00:00.000Z","513":"2016-11-25T00:00:00.000Z","514":"2016-11-26T00:00:00.000Z","515":"2016-11-27T00:00:00.000Z","516":"2016-11-28T00:00:00.000Z","517":"2016-11-29T00:00:00.000Z","518":"2016-11-30T00:00:00.000Z","519":"2016-12-01T00:00:00.000Z","520":"2016-12-02T00:00:00.000Z","521":"2016-12-03T00:00:00.000Z","522":"2016-12-04T00:00:00.000Z","523":"2016-12-05T00:00:00.000Z","524":"2016-12-06T00:00:00.000Z","525":"2016-12-07T00:00:00.000Z","526":"2016-12-08T00:00:00.000Z","527":"2016-12-09T00:00:00.000Z","528":"2016-12-10T00:00:00.000Z","529":"2016-12-11T00:00:00.000Z","530":"2016-12-12T00:00:00.000Z","531":"2016-12-13T00:00:00.000Z","532":"2016-12-14T00:00:00.000Z","533":"2016-12-15T00:00:00.000Z","534":"2016-12-16T00:00:00.000Z","535":"2016-12-17T00:00:00.000Z","536":"2016-12-18T00:00:00.000Z","537":"2016-12-19T00:00:00.000Z","538":"2016-12-20T00:00:00.000Z","539":"2016-12-21T00:00:00.000Z","540":"2016-12-22T00:00:00.000Z","541":"2016-12-23T00:00:00.000Z","542":"2016-12-24T00:00:00.000Z","543":"2016-12-25T00:00:00.000Z","544":"2016-12-26T00:00:00.000Z","545":"2016-12-27T00:00:00.000Z","546":"2016-12-28T00:00:00.000Z","547":"2016-12-29T00:00:00.000Z","548":"2016-12-30T00:00:00.000Z","549":"2016-12-31T00:00:00.000Z","550":"2017-01-01T00:00:00.000Z","551":"2017-01-02T00:00:00.000Z","552":"2017-01-03T00:00:00.000Z","553":"2017-01-04T00:00:00.000Z","554":"2017-01-05T00:00:00.000Z","555":"2017-01-06T00:00:00.000Z","556":"2017-01-07T00:00:00.000Z","557":"2017-01-08T00:00:00.000Z","558":"2017-01-09T00:00:00.000Z","559":"2017-01-10T00:00:00.000Z","560":"2017-01-11T00:00:00.000Z","561":"2017-01-12T00:00:00.000Z","562":"2017-01-13T00:00:00.000Z","563":"2017-01-14T00:00:00.000Z","564":"2017-01-15T00:00:00.000Z","565":"2017-01-16T00:00:00.000Z","566":"2017-01-17T00:00:00.000Z","567":"2017-01-18T00:00:00.000Z","568":"2017-01-19T00:00:00.000Z","569":"2017-01-20T00:00:00.000Z","570":"2017-01-21T00:00:00.000Z","571":"2017-01-22T00:00:00.000Z","572":"2017-01-23T00:00:00.000Z","573":"2017-01-24T00:00:00.000Z","574":"2017-01-25T00:00:00.000Z","575":"2017-01-26T00:00:00.000Z","576":"2017-01-27T00:00:00.000Z","577":"2017-01-28T00:00:00.000Z","578":"2017-01-29T00:00:00.000Z","579":"2017-01-30T00:00:00.000Z","580":"2017-01-31T00:00:00.000Z","581":"2017-02-01T00:00:00.000Z","582":"2017-02-02T00:00:00.000Z","583":"2017-02-03T00:00:00.000Z","584":"2017-02-04T00:00:00.000Z","585":"2017-02-05T00:00:00.000Z","586":"2017-02-06T00:00:00.000Z","587":"2017-02-07T00:00:00.000Z","588":"2017-02-08T00:00:00.000Z","589":"2017-02-09T00:00:00.000Z","590":"2017-02-10T00:00:00.000Z","591":"2017-02-11T00:00:00.000Z","592":"2017-02-12T00:00:00.000Z","593":"2017-02-13T00:00:00.000Z","594":"2017-02-14T00:00:00.000Z","595":"2017-02-15T00:00:00.000Z","596":"2017-02-16T00:00:00.000Z","597":"2017-02-17T00:00:00.000Z","598":"2017-02-18T00:00:00.000Z","599":"2017-02-19T00:00:00.000Z","600":"2017-02-20T00:00:00.000Z","601":"2017-02-21T00:00:00.000Z","602":"2017-02-22T00:00:00.000Z","603":"2017-02-23T00:00:00.000Z","604":"2017-02-24T00:00:00.000Z","605":"2017-02-25T00:00:00.000Z","606":"2017-02-26T00:00:00.000Z","607":"2017-02-27T00:00:00.000Z","608":"2017-02-28T00:00:00.000Z","609":"2017-03-01T00:00:00.000Z"},"4358":{"490":481.0,"491":413.0,"492":383.0,"493":426.0,"494":507.0,"495":416.0,"496":401.0,"497":328.0,"498":333.0,"499":587.0,"500":553.0,"501":575.0,"502":529.0,"503":553.0,"504":495.0,"505":428.0,"506":478.0,"507":479.0,"508":484.0,"509":457.0,"510":412.0,"511":417.0,"512":379.0,"513":417.0,"514":453.0,"515":467.0,"516":413.0,"517":391.0,"518":406.0,"519":365.0,"520":390.0,"521":372.0,"522":486.0,"523":471.0,"524":365.0,"525":340.0,"526":398.0,"527":409.0,"528":400.0,"529":401.0,"530":409.0,"531":367.0,"532":398.0,"533":367.0,"534":414.0,"535":506.0,"536":490.0,"537":442.0,"538":428.0,"539":442.0,"540":419.0,"541":402.0,"542":324.0,"543":371.0,"544":471.0,"545":444.0,"546":470.0,"547":467.0,"548":458.0,"549":420.0,"550":null,"551":null,"552":null,"553":null,"554":null,"555":null,"556":null,"557":null,"558":null,"559":null,"560":null,"561":null,"562":null,"563":null,"564":null,"565":null,"566":null,"567":null,"568":null,"569":null,"570":null,"571":null,"572":null,"573":null,"574":null,"575":null,"576":null,"577"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'4359' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '4359' 4.23968768119812 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['4359']' 12.286267042160034 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2015-07-01T00:00:00.000000 TimeMax=2016-07-26T00:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='4359' Length=550 Min=24.0 Max=1905.0 Mean=107.80909090909091 StdDev=194.30429253923742 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_4359' Min=24.0 Max=1905.0 Mean=107.80909090909091 StdDev=194.30429253923742 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_4359_Lag1Trend_residue_zeroCycle_residue_NoAR' [Lag1Trend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_4359_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_4359_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_4359_Lag1Trend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.286 MAPE_Forecast=0.3172 MAPE_Test=0.2438 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2677 SMAPE_Forecast=0.2702 SMAPE_Test=0.2352 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9974 MASE_Forecast=0.9939 MASE_Test=1.0046 -INFO:pyaf.std:MODEL_L1 L1_Fit=30.801020408163264 L1_Forecast=74.77551020408163 L1_Test=10.233333333333333 -INFO:pyaf.std:MODEL_L2 L2_Fit=79.65922317431105 L2_Forecast=207.66662844912895 L2_Test=13.397761006974262 -INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_4359' Min=1.224744871391589 Max=2.345207879911715 Mean=1.289085772236084 StdDev=0.12837557151377743 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_4359_LinearTrend_residue_zeroCycle_residue_AR(16)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL 'Anscombe_4359_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_4359_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_4359_LinearTrend_residue_zeroCycle_residue_AR(16)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3264 MAPE_Forecast=0.3105 MAPE_Test=0.2269 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2885 SMAPE_Forecast=0.3117 SMAPE_Test=0.2084 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.0328 MASE_Forecast=1.1341 MASE_Test=0.8866 +INFO:pyaf.std:MODEL_L1 L1_Fit=31.89400248134495 L1_Forecast=85.32401278593734 L1_Test=9.031743580981047 +INFO:pyaf.std:MODEL_L2 L2_Fit=78.02950034659494 L2_Forecast=220.9736497915182 L2_Test=11.11225230445867 +INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.2976586536359371, array([-0.03849628])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Anscombe_4359_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag1 0.4532482594836592 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag2 0.16019466800670573 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag8 -0.07725610819861344 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag5 0.051888860994556196 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag3 0.03430226237067123 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag4 0.030005650343974025 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag6 0.027823372760330787 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag7 -0.02095876205484002 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag9 -0.013569592871201373 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_4359_LinearTrend_residue_zeroCycle_residue_Lag11 -0.009335864368158454 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 1.123389482498169 +INFO:pyaf.std:START_FORECASTING '['4359']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4359']' 3.148853302001953 Split Transformation ... ForecastMAPE TestMAPE -0 None Anscombe_4359 ... 0.3105 0.2269 -1 None _4359 ... 0.3172 0.2438 -2 None Anscombe_4359 ... 0.3172 0.2438 -3 None Diff_4359 ... 0.3172 0.2438 -4 None Anscombe_4359 ... 0.3525 0.2526 +0 None Anscombe_4359 ... 0.3068 0.2143 +1 None Anscombe_4359 ... 0.3105 0.2269 +2 None Anscombe_4359 ... 0.3105 0.2269 +3 None _4359 ... 0.3172 0.2438 +4 None _4359 ... 0.3172 0.2438 [5 rows x 8 columns] -Forecast Columns Index(['Date', '4359', 'row_number', 'Date_Normalized', '_4359', - '_4359_Lag1Trend', '_4359_Lag1Trend_residue', - '_4359_Lag1Trend_residue_zeroCycle', - '_4359_Lag1Trend_residue_zeroCycle_residue', - '_4359_Lag1Trend_residue_zeroCycle_residue_NoAR', - '_4359_Lag1Trend_residue_zeroCycle_residue_NoAR_residue', '_4359_Trend', - '_4359_Trend_residue', '_4359_Cycle', '_4359_Cycle_residue', '_4359_AR', - '_4359_AR_residue', '_4359_TransformedForecast', '4359_Forecast', - '_4359_TransformedResidue', '4359_Residue'], +Forecast Columns Index(['Date', '4359', 'row_number', 'Date_Normalized', 'Anscombe_4359', + 'Anscombe_4359_LinearTrend', 'Anscombe_4359_LinearTrend_residue', + 'Anscombe_4359_LinearTrend_residue_zeroCycle', + 'Anscombe_4359_LinearTrend_residue_zeroCycle_residue', + 'Anscombe_4359_LinearTrend_residue_zeroCycle_residue_AR(16)', + 'Anscombe_4359_LinearTrend_residue_zeroCycle_residue_AR(16)_residue', + 'Anscombe_4359_Trend', 'Anscombe_4359_Trend_residue', + 'Anscombe_4359_Cycle', 'Anscombe_4359_Cycle_residue', + 'Anscombe_4359_AR', 'Anscombe_4359_AR_residue', + 'Anscombe_4359_TransformedForecast', '4359_Forecast', + 'Anscombe_4359_TransformedResidue', '4359_Residue'], dtype='object') RangeIndex: 610 entries, 0 to 609 @@ -7790,95 +8311,97 @@ memory usage: 14.4 KB None Forecasts Date 4359 4359_Forecast -550 2017-01-01 NaN 41.0 -551 2017-01-02 NaN 41.0 -552 2017-01-03 NaN 41.0 -553 2017-01-04 NaN 41.0 -554 2017-01-05 NaN 41.0 -555 2017-01-06 NaN 41.0 -556 2017-01-07 NaN 41.0 -557 2017-01-08 NaN 41.0 -558 2017-01-09 NaN 41.0 -559 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+INFO:pyaf.std:START_FORECASTING '['4360']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['4360']' 2.070089817047119 Split Transformation ... ForecastMAPE TestMAPE 0 None _4360 ... 0.1855 0.1502 -1 None Anscombe_4360 ... 0.1869 0.1473 -2 None _4360 ... 0.1959 0.1523 -3 None Anscombe_4360 ... 0.1959 0.1523 -4 None Diff_4360 ... 0.1959 0.1523 +1 None _4360 ... 0.1855 0.1502 +2 None Anscombe_4360 ... 0.1869 0.1473 +3 None Anscombe_4360 ... 0.1869 0.1473 +4 None Anscombe_4360 ... 0.1928 0.1503 [5 rows x 8 columns] Forecast Columns Index(['Date', '4360', 'row_number', 'Date_Normalized', '_4360', '_4360_Lag1Trend', '_4360_Lag1Trend_residue', - '_4360_Lag1Trend_residue_zeroCycle', - '_4360_Lag1Trend_residue_zeroCycle_residue', - '_4360_Lag1Trend_residue_zeroCycle_residue_AR(16)', - '_4360_Lag1Trend_residue_zeroCycle_residue_AR(16)_residue', + '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth', + '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth_residue', + '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR', + '_4360_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR_residue', '_4360_Trend', '_4360_Trend_residue', '_4360_Cycle', 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diff --git a/tests/references/perfs_test_ozone_perf_measure_L1.log b/tests/references/perfs_test_ozone_perf_measure_L1.log index 16cc909dc..27e3936dc 100644 --- a/tests/references/perfs_test_ozone_perf_measure_L1.log +++ b/tests/references/perfs_test_ozone_perf_measure_L1.log @@ -5,83 +5,179 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.5605426228203235 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.5261675661644761 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 1.375 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.6508983876696501 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 1.5916666666666666 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.658300014310987 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 1.6341503267973863 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.49890693458144625 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.2958333333333332 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.5022433204129457 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.46249999999999997 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.5323261827434593 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.4750000000000001 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.4995426348666088 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5161409468920186 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.42992050508902313 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.6360316077716477 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.42992050508902313 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.6360316077716477 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.8748264723979712 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 1.3242319894152537 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.790797232162749 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 1.402278992293177 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.790797232162749 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 1.402278992293177 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.6879144278726216 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 6.995833333333334 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 1.0566145117902794 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 20.10416666666664 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.6603972655612725 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.5223856209150293 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 1.6305711595820502 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 6.416666666666625 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.49755304755854696 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.47500000000000014 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.49755304755854696 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.47500000000000014 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 3.1385447415183445 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.825195127492685 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.756691711124745 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6948994436442139 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.756691711124745 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6948994436442139 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 6.347161603813181 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 12.248525337291364 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 6.603538240739032 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 23.068989212426406 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 3.7499569653097917 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 4.373040035575822 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 6262.314593549406 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.136674096905536 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 145.7754226418056 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 149999998.72500002 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 145.7754226418056 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 149999998.72500002 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 1816543.239794039 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 1.2749999999886539 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 1001.1714595941761 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 1.273892372787632 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 823.8472230204334 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.4749999999999999 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 79021.4163449064 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 30.918610996415797 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 21755.620663238235 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 149999998.72500002 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 21755.620663238235 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 149999998.72500002 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 150000001.0416667 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 144862.3412062537 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8918017.740977407 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 149999998.72500002 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 129372055.57368635 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 149999998.72500002 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.8175661883510942 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.6499999999999884 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.6422936469990683 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 3.854166666666679 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.7838374201027061 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 2.475 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.9411250517766688 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.44166666666666293 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.9411250517766688 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.44166666666666293 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.8126886027042626 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.4750000000000076 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.8405936049178894 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.3592585858770498 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.724449354540699 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2191569803939653 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.724449354540699 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.2191569803939653 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.8297978245418371 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.671979209978542 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.7053445083391655 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 2.648158256631882 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.7053445083391655 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.648158256631882 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.5605426228203237 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.7333333333333332 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.61204078008256 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.6120407800825598 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 1.1461538461538463 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.6086269012321174 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.6086269012321177 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 1.2105245517010224 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.6373389130755879 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.6373389130755884 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.658974358974359 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.6612524595546271 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.6612524595546275 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.7948717948717949 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.6129709676792842 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.6129709676792846 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.7999999999999999 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.6323256152404273 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.5551666232747684 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.5265316758013037 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.5265316758013029 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 0.8979604351062586 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.5265316758013037 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.5265316758013029 INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.8979604351062586 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.5854931516610287 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.5497940931077173 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.5234616533490389 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.9531820454348846 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.5234616533490389 -INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.9531820454348846 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.7007866782266745 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.5854931516610291 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.5497940931077184 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.52346165334904 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.9531820454348853 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.52346165334904 +INFO:pyaf.std:collectPerformanceIndices : L1 None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.9531820454348853 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.7007866782266792 INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 5.523076923076923 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.6951969992917088 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.6951969992917069 INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 16.846153846153836 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 1.038525959861724 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 1.038525959861723 INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 1.0532260767554873 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 1.2219989703345728 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 1.2219989703345762 INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 5.651282051282014 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.5628756708886004 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.5628756708885972 INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.7999999999999999 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.5628756708886004 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.5628756708885972 INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.7999999999999999 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 2.074309665032368 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 5.436377212875031 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 1.0773790807543189 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.9170607242312269 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 1.0773790807543189 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.9170607242312269 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 1.4562733363378548 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 8.421536555114356 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 1.9287783768151672 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 17.753964437744504 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 1.1510533705482664 -INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 1.6666173293759767 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 48579.42959963605 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 2.0743096650323647 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 5.436377212875025 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 1.0773790807543242 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 0.9170607242312266 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 1.0773790807543242 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.9170607242312266 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 1.4562733363378633 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 8.42153655511437 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 1.9287783768151858 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 17.75396443774452 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 1.1510533705482722 +INFO:pyaf.std:collectPerformanceIndices : L1 None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 1.6666173293759852 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 48579.42959963651 INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.762322758966405 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 46553.871137539776 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 46553.87113753933 INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 46553.871137539776 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 46553.87113753933 INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 136658.13563484067 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 136658.1356348429 INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 1.9820512814054727 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 2102.2598701796096 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 2102.259870179624 INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.9753594417107565 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 4307.447872763609 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 4307.447872763839 INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.7999999999999998 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 432512.13457099174 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.4498065631715833 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 381244.459691654 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 432512.1345709996 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.449806563171583 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 381244.4596916433 INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 381244.459691654 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 381244.4596916433 INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 41150179.46784002 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 913.8998075263399 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 12687651.905730065 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 10290421.248161048 -INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 63498159.08889887 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 41150179.467839904 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 913.899807526342 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 12687651.905729825 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 10290421.24816105 +INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 63498159.08889845 INFO:pyaf.std:collectPerformanceIndices : L1 None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 1.0702947520150587 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 1.070294752015082 INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 3.8320512820512787 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.9470908687670389 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.9470908687670418 INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 4.005128205128212 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.9780554284937057 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.9780554284937554 INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 3.1820512820512823 INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.8498661927869472 INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.7628205128205187 @@ -89,19 +185,19 @@ INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_Lag1T INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.7628205128205187 INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 0.9064310107041359 INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.8000000000000077 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.9230256030959577 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.7713810103146841 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 1.1741715821619572 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.9864983552558724 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 1.1741715821619572 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.9864983552558724 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.8931955390296981 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 1.7808079756061148 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 1.18946033342649 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 1.7556142987693555 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 1.18946033342649 -INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 1.7556142987693555 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 5.759371280670166 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.923025603095914 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.7713810103147178 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 1.1741715821618726 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.9864983552558783 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 1.1741715821618726 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.9864983552558783 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.8931955390297447 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 1.780807975606071 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 1.189460333426548 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 1.755614298769329 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 1.189460333426548 +INFO:pyaf.std:collectPerformanceIndices : L1 None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 1.755614298769329 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.346686840057373 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -113,41 +209,44 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51)' INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.164 MAPE_Forecast=0.1657 MAPE_Test=0.343 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1541 SMAPE_Forecast=0.1624 SMAPE_Test=0.2829 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6966 MASE_Forecast=0.6743 MASE_Test=1.6728 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.612253593001835 L1_Forecast=0.5234616533490389 L1_Test=0.7907972321627473 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8075172004744579 L2_Forecast=0.6961889847046421 L2_Test=0.9040021463083746 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.612253593001835 L1_Forecast=0.52346165334904 L1_Test=0.790797232162749 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8075172004744577 L2_Forecast=0.6961889847046427 L2_Test=0.9040021463083768 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.161127322430003, array([-2.3971979 , -0.44987431, 1.1799632 ])) +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.161127322430005, array([-2.3971979 , -0.44987431, 1.1799632 ])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_PolyTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag1 0.4298034154687585 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag10 0.18759477698423593 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag7 -0.1712780359497549 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag30 0.16118420849024673 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag12 0.1408376722792285 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag1 0.42980341546875883 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag10 0.18759477698423566 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag7 -0.17127803594975502 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag30 0.16118420849024662 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag12 0.1408376722792287 INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag48 0.1268329129615784 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag32 -0.1260528502207304 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag36 0.12175439034016733 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag22 0.11587981866061661 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag39 -0.11437523085285092 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag32 -0.12605285022073032 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag36 0.12175439034016722 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag22 0.11587981866061664 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag39 -0.11437523085285083 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.4651572704315186 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.36261987686157227 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... None None -2 None _Ozone ... None None -1 None _Ozone ... None None -3 None _Ozone ... None None - -[4 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 23.088494300842285 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.4826099872589111 Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... None None 1 None _Ozone ... None None @@ -197,31 +296,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "PolyTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5234616533490389", - "MAPE": "0.1657", - "MASE": "0.6743", - "RMSE": "0.6961889847046421" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.52346165334904", + "MAPE": "0.1657", + "MASE": "0.6743", + "RMSE": "0.6961889847046427" + } } } diff --git a/tests/references/perfs_test_ozone_perf_measure_L2.log b/tests/references/perfs_test_ozone_perf_measure_L2.log index 70aca9a00..c4cfca400 100644 --- a/tests/references/perfs_test_ozone_perf_measure_L2.log +++ b/tests/references/perfs_test_ozone_perf_measure_L2.log @@ -5,83 +5,179 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.6960254985946378 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.6440929767832049 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 1.5303866613811468 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.809814658369991 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 1.758195097251724 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.8090340517856743 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 1.7867071082222747 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.5854632805592169 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.39712508524812845 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.5667100522815951 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.5643949562732348 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.6174583977530741 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.5737304826019501 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.6012349281893705 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5961504841809397 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.5519432695595543 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.739349297072831 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.5519432695595543 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.739349297072831 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 1.001916858868194 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 1.4932919781088982 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.9040021463083768 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 1.582117704495611 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.9040021463083768 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 1.582117704495611 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.8474751805774461 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 7.014493923299099 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 1.2152125512751635 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 20.12129365456735 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.8243084760949634 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.6863570832326995 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 1.7647743292937443 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 6.437746759024682 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.5501555212590609 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.5737304826019501 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.5501555212590609 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.5737304826019501 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 3.162550320393547 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.8367533836843 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.9107119929571179 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.8437491246684338 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.9107119929571179 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.8437491246684338 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 6.497676355127097 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 12.284325795175185 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 6.714647195214352 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 23.09588512345918 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 3.847734914920028 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 4.464878559052141 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 9784.889886362613 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.3121792655859033 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 223.23519509400634 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 149999998.725 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 223.23519509400634 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 149999998.725 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 2534419.4431996937 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 1.4654350889627958 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 1469.9209715773138 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 1.4642045657932787 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 1268.464173985049 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.5737304826019501 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 130230.89605266292 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 35.26751411130984 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 30353.019649823073 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 149999998.725 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 30353.019649823073 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 149999998.725 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 150000001.04166672 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 194039.57940350502 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 13385075.481868764 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 149999998.725 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 136994816.29992777 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 149999998.725 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 1.001551819825926 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 7.536605115479147 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.8372405520141378 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 5.121950312136979 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.9012925155323974 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 2.5782746168707473 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 1.1642464369933578 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5458174297937527 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 1.1642464369933578 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.5458174297937527 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.8916974916055468 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.5737304826019518 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.9479650679543811 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.5855387471750457 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.8094877711263742 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.4129426744487286 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.8094877711263742 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.4129426744487286 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.9882372210949184 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.8845211800911046 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.8105844693722842 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 2.744418528222996 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.8105844693722842 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.744418528222996 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.6960254985946376 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.9097759656598473 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.7747504222056346 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.7747504222056345 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 1.3790557899500917 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.7617249259910056 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.7617249259910058 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 1.426654943722196 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.8042916961844139 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.8042916961844141 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.8588901604351443 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.869814035636587 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.8698140356365872 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.9848857801796105 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.8430031293616601 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.8430031293616607 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.9658263367813662 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.7565740356816676 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.662953224721834 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.7315688649507152 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_LinearTrend_Cycle_None_AR 62 0.731568864950715 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_LinearTrend_Cycle_None_NoAR 24 1.1010807964604632 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.7315688649507152 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.731568864950715 INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 1.1010807964604632 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.7099281215012695 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.7284840017128614 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.6961889847046421 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 1.1759775793470628 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.6961889847046421 -INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 1.1759775793470628 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.8398335462401598 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.7099281215012702 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.7284840017128632 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.6961889847046427 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 1.175977579347064 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.6961889847046427 +INFO:pyaf.std:collectPerformanceIndices : L2 None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 1.175977579347064 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.8398335462401647 INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 5.579725202313581 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.864209716842245 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.8642097168422438 INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 16.927098589736705 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 1.1734557209179244 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 1.1734557209179235 INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 1.2727967070003514 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 1.4090960675703095 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 1.409096067570313 INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 5.727844719281826 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.8261753731796114 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_Lag1Trend_Cycle_None_AR 110 0.8261753731796115 INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_Lag1Trend_Cycle_None_NoAR 72 0.9658263367813663 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.8261753731796114 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.8261753731796115 INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.9658263367813663 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 2.210512888531719 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 5.466921853264854 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 1.207589712132219 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 1.1180053929094167 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 1.207589712132219 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 1.1180053929094167 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 1.9302725728382455 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 8.542001756508121 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 2.3706978943635577 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 17.886144259293616 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 1.4459132184945247 -INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 1.9909753046415244 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 76320.99566412526 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 2.2105128885317167 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 5.466921853264847 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_Cycle_None_AR 94 1.2075897121322243 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_Cycle_None_NoAR 56 1.1180053929094165 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 1.2075897121322243 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 1.1180053929094165 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 1.9302725728382566 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 8.542001756508137 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 2.3706978943635812 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 17.886144259293626 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 1.4459132184945318 +INFO:pyaf.std:collectPerformanceIndices : L2 None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 1.9909753046415344 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 76320.99566412593 INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0321935479641593 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 63368.414081809424 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 63368.41408180898 INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 63368.414081809424 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 63368.41408180898 INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 342559.42941343866 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 342559.4294134442 INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.259282844472537 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 5065.199738872224 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 5065.199738872256 INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 2.2523518368743276 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 11358.33441336752 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 11358.3344133681 INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.9658263367813654 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 916798.6087364714 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 3.531365735219621 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 629556.888582446 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 916798.6087364876 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 3.53136573521962 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 629556.8885824276 INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 629556.888582446 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 629556.8885824276 INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 71088723.21584608 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 2023.0110611931511 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 29509169.381356277 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 26389914.73209321 -INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 92961811.74453643 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 71088723.2158459 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 2023.0110611931552 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 29509169.38135582 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 26389914.73209322 +INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 92961811.74453597 INFO:pyaf.std:collectPerformanceIndices : L2 None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 149999998.01794872 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 1.434311608286531 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 1.434311608286513 INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 8.092347603976828 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 1.1321273465161876 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 1.1321273465161723 INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 5.572712822775661 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 1.2057677805950133 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 1.205767780595026 INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 3.361737951013144 INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 1.105384520850317 INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.9164801675082231 @@ -89,19 +185,19 @@ INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_Lag1T INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.9164801675082231 INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 1.1632988446952974 INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.9658263367813694 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 1.139252716752329 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 1.0338537964924692 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 1.388131630702993 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 1.2002716680094039 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 1.388131630702993 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 1.2002716680094039 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 1.1902513408687048 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 2.0573298713351065 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 1.3915867702930702 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 2.008140577518323 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 1.3915867702930702 -INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 2.008140577518323 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 5.090008497238159 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 1.1392527167523134 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 1.0338537964925314 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 1.3881316307028648 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 1.2002716680093997 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 1.3881316307028648 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 1.2002716680093997 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 1.1902513408687667 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 2.057329871335058 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 1.39158677029313 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 2.008140577518298 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 1.39158677029313 +INFO:pyaf.std:collectPerformanceIndices : L2 None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 2.008140577518298 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.723332166671753 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -128,13 +224,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.112995862960815 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.16011786460876465 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... None None - -[1 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.242292642593384 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.6341040134429932 Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... None None 1 None _Ozone ... None None @@ -184,31 +286,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5551666232747684", - "MAPE": "0.1765", - "MASE": "0.7151", - "RMSE": "0.662953224721834" + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5551666232747684", + "MAPE": "0.1765", + "MASE": "0.7151", + "RMSE": "0.662953224721834" + } } } diff --git a/tests/references/perfs_test_ozone_perf_measure_MAPE.log b/tests/references/perfs_test_ozone_perf_measure_MAPE.log index 03f25eac2..0774cc0fd 100644 --- a/tests/references/perfs_test_ozone_perf_measure_MAPE.log +++ b/tests/references/perfs_test_ozone_perf_measure_MAPE.log @@ -5,6 +5,102 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.4619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.9761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 360.8514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 3289621.0774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 @@ -101,7 +197,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Pol INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 5.48717737197876 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 8.584742546081543 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -113,8 +209,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6135978624272591 L1_Forecast=0.5265316758013037 L1_Test=0.42992050508902385 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507152 L2_Test=0.551943269559555 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -126,28 +222,31 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033754 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533328 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225498 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.1612820579538937 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046368 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481863 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102252 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045825 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947768 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.285993337631226 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3378636837005615 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.174 0.9094 -2 None _Ozone ... 0.343 1.6728 -1 None _Ozone ... 0.174 0.9094 -3 None _Ozone ... 0.343 1.6728 - -[4 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.522131204605103 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.287003755569458 Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.1740 0.9094 1 None _Ozone ... 0.1740 0.9094 @@ -197,31 +296,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013037", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507152" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/perfs_test_ozone_perf_measure_MASE.log b/tests/references/perfs_test_ozone_perf_measure_MASE.log index fea392763..7cd66560b 100644 --- a/tests/references/perfs_test_ozone_perf_measure_MASE.log +++ b/tests/references/perfs_test_ozone_perf_measure_MASE.log @@ -5,6 +5,102 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 1.113 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 2.9087 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 1.3769 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 3.367 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 1.3926 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 3.4569 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 1.0554 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.6258 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 1.0624 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.9784 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 1.1261 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 1.0048 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 1.0567 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 1.0918 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.9094 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 1.3455 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.9094 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 1.3455 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 1.8506 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 2.8013 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 1.6728 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 2.9664 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 1.6728 +INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 2.9664 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 1.4552 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 14.7989 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 2.2351 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 42.528 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 1.397 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 3.2204 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 3.4493 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 13.5737 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 1.0525 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 1.0048 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 1.0525 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 1.0048 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 6.6392 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 12.3225 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 1.6007 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.47 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 1.6007 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.47 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 13.4267 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 25.9103 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 13.969 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 48.7998 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 7.9326 +INFO:pyaf.std:collectPerformanceIndices : MASE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 9.2507 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 13247.2039 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 2.4045 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 308.3711 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 317307689.5435 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 308.3711 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 317307689.5435 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 3842687.6218 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.6971 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 2117.8627 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 2.6948 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 1742.7537 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 1.0048 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 167160.6884 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 65.4048 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 46021.5052 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 317307689.5435 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 46021.5052 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 317307689.5435 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 317307694.4441 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 306439.5679 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 18865037.525 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 317307689.5435 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 273671655.9634 +INFO:pyaf.std:collectPerformanceIndices : MASE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 317307689.5435 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 1.7295 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 7.7212 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 1.3587 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 8.153 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 1.6581 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 5.2356 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 1.9908 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.9343 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 1.9908 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.9343 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 1.7191 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 1.0048 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.7782 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.8754 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 1.5325 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 2.579 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 1.5325 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 2.579 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.7553 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.6523 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 1.4921 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 5.6019 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.4921 +INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 5.6019 INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.7221 INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.9446 INFO:pyaf.std:collectPerformanceIndices : MASE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.7884 @@ -101,7 +197,7 @@ INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone Integration_Pol INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 2.2615 INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 1.5322 INFO:pyaf.std:collectPerformanceIndices : MASE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 2.2615 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 6.028483867645264 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.845917701721191 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -113,41 +209,44 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51)' INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.164 MAPE_Forecast=0.1657 MAPE_Test=0.343 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1541 SMAPE_Forecast=0.1624 SMAPE_Test=0.2829 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6966 MASE_Forecast=0.6743 MASE_Test=1.6728 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.612253593001835 L1_Forecast=0.5234616533490389 L1_Test=0.7907972321627473 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8075172004744579 L2_Forecast=0.6961889847046421 L2_Test=0.9040021463083746 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.612253593001835 L1_Forecast=0.52346165334904 L1_Test=0.790797232162749 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8075172004744577 L2_Forecast=0.6961889847046427 L2_Test=0.9040021463083768 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.161127322430003, array([-2.3971979 , -0.44987431, 1.1799632 ])) +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.161127322430005, array([-2.3971979 , -0.44987431, 1.1799632 ])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_PolyTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag1 0.4298034154687585 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag10 0.18759477698423593 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag7 -0.1712780359497549 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag30 0.16118420849024673 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag12 0.1408376722792285 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag1 0.42980341546875883 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag10 0.18759477698423566 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag7 -0.17127803594975502 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag30 0.16118420849024662 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag12 0.1408376722792287 INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag48 0.1268329129615784 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag32 -0.1260528502207304 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag36 0.12175439034016733 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag22 0.11587981866061661 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag39 -0.11437523085285092 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag32 -0.12605285022073032 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag36 0.12175439034016722 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag22 0.11587981866061664 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_PolyTrend_residue_zeroCycle_residue_Lag39 -0.11437523085285083 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.4948694705963135 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3718283176422119 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.343 1.6728 -2 None _Ozone ... 0.174 0.9094 -1 None _Ozone ... 0.343 1.6728 -3 None _Ozone ... 0.174 0.9094 - -[4 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.631534576416016 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.0602772235870361 Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.3430 1.6728 1 None _Ozone ... 0.3430 1.6728 @@ -197,31 +296,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "PolyTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5234616533490389", - "MAPE": "0.1657", - "MASE": "0.6743", - "RMSE": "0.6961889847046421" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.52346165334904", + "MAPE": "0.1657", + "MASE": "0.6743", + "RMSE": "0.6961889847046427" + } } } diff --git a/tests/references/real-life_test_sof_example.log b/tests/references/real-life_test_sof_example.log index 62f5e49f8..fa848c2c1 100644 --- a/tests/references/real-life_test_sof_example.log +++ b/tests/references/real-life_test_sof_example.log @@ -5,6 +5,70 @@ INFO:pyaf.std:START_TRAINING 'Used' 2 2011-11-03 600 3 2011-11-04 599 4 2011-11-05 678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(15) 18 0.088 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.1133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_ConstantTrend_residue_zeroCycle_residue_AR(15) 10 0.088 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.1133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(15) 50 0.0732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_Lag1Trend_residue_zeroCycle_residue_AR(15) 42 0.0732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.0801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_LinearTrend_residue_bestCycle_byMAPE_residue_AR(15) 34 0.0859 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.1121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_LinearTrend_residue_zeroCycle_residue_AR(15) 26 0.0859 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.1121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_PolyTrend_residue_bestCycle_byMAPE_residue_AR(15) 34 0.1159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.1359 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_PolyTrend_residue_zeroCycle_residue_AR(15) 26 0.1178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.1258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(15) 50 0.155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_ConstantTrend_residue_zeroCycle_residue_AR(15) 42 0.155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.1107 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(15) 82 0.1099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_Lag1Trend_residue_zeroCycle_residue_AR(15) 74 0.1099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_LinearTrend_residue_bestCycle_byMAPE_residue_AR(15) 66 0.2383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1512 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_LinearTrend_residue_zeroCycle_residue_AR(15) 58 0.2383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.1512 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_PolyTrend_residue_bestCycle_byMAPE_residue_AR(15) 66 0.2439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.1544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_PolyTrend_residue_zeroCycle_residue_AR(15) 58 0.2439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Used_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.1544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(15) 50 5566848.3661 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_ConstantTrend_residue_zeroCycle_residue_AR(15) 42 5566848.3661 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_ConstantTrend_residue_zeroCycle_residue_NoAR 32 5566848.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(15) 82 5566848.3647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_Lag1Trend_residue_zeroCycle_residue_AR(15) 74 5566848.3647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_LinearTrend_residue_bestCycle_byMAPE_residue_AR(15) 66 5566848.4024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 5566848.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_LinearTrend_residue_zeroCycle_residue_AR(15) 58 5566848.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_LinearTrend_residue_zeroCycle_residue_NoAR 48 5566848.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_PolyTrend_residue_bestCycle_byMAPE_residue_AR(15) 66 5566848.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 5566848.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_PolyTrend_residue_zeroCycle_residue_AR(15) 58 5566848.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Used_PolyTrend_residue_zeroCycle_residue_NoAR 48 5566848.0574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(15) 50 0.1012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_ConstantTrend_residue_zeroCycle_residue_AR(15) 42 0.1012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(15) 82 0.1145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0814 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_Lag1Trend_residue_zeroCycle_residue_AR(15) 74 0.1084 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_LinearTrend_residue_bestCycle_byMAPE_residue_AR(15) 66 0.0846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.275 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_LinearTrend_residue_zeroCycle_residue_AR(15) 58 0.0846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.275 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_PolyTrend_residue_bestCycle_byMAPE_residue_AR(15) 66 0.113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_PolyTrend_residue_zeroCycle_residue_AR(15) 58 0.113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.145 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used NoTransf_ConstantTrend_Cycle_None_AR 18 0.0219 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.0408 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Used NoTransf_ConstantTrend_NoCycle_AR 10 0.0219 @@ -69,7 +133,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used Integration_Poly INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used Integration_PolyTrend_Cycle_None_NoAR 56 0.7699 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used Integration_PolyTrend_NoCycle_AR 58 0.0504 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Used Integration_PolyTrend_NoCycle_NoAR 48 0.7699 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Used' 3.21167254447937 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Used']' 4.62622857093811 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2011-11-01T00:00:00.000000 TimeMax=2011-12-10T00:00:00.000000 TimeDelta= Horizon=10 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Used' Length=61 Min=550 Max=800 Mean=627.8852459016393 StdDev=51.32445758360245 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Used' Min=550 Max=800 Mean=627.8852459016393 StdDev=51.32445758360245 @@ -81,8 +145,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Used_ConstantTrend_residue_zeroCycle_residue_AR(1 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.036 MAPE_Forecast=0.0219 MAPE_Test=0.088 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.036 SMAPE_Forecast=0.0218 SMAPE_Test=0.0952 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.623 MASE_Forecast=0.5042 MASE_Test=1.1484 -INFO:pyaf.std:MODEL_L1 L1_Fit=22.187872017704517 L1_Forecast=13.612426292653016 L1_Test=64.81889200924046 -INFO:pyaf.std:MODEL_L2 L2_Fit=29.11620775569817 L2_Forecast=15.415689563294164 L2_Test=89.82108749258755 +INFO:pyaf.std:MODEL_L1 L1_Fit=22.187872017704517 L1_Forecast=13.612426292653005 L1_Test=64.81889200924047 +INFO:pyaf.std:MODEL_L2 L2_Fit=29.116207755698166 L2_Forecast=15.415689563294148 L2_Test=89.82108749258757 INFO:pyaf.std:MODEL_COMPLEXITY 10 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -94,29 +158,31 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Used_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Used_ConstantTrend_residue_zeroCycle_residue_Lag14 0.4531256368468811 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Used_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.3213959938690225 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Used_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.1982512449820899 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Used_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.13203337020403427 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Used_ConstantTrend_residue_zeroCycle_residue_Lag15 0.13084648185091305 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Used_ConstantTrend_residue_zeroCycle_residue_Lag8 0.05650614907392988 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Used_ConstantTrend_residue_zeroCycle_residue_Lag11 0.04528419544498935 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Used_ConstantTrend_residue_zeroCycle_residue_Lag13 0.03361954959624684 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Used_ConstantTrend_residue_zeroCycle_residue_Lag14 0.45312563684688073 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Used_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.32139599386902246 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Used_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.19825124498209007 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Used_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.13203337020403386 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Used_ConstantTrend_residue_zeroCycle_residue_Lag15 0.13084648185091302 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Used_ConstantTrend_residue_zeroCycle_residue_Lag8 0.05650614907392956 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Used_ConstantTrend_residue_zeroCycle_residue_Lag11 0.04528419544498993 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Used_ConstantTrend_residue_zeroCycle_residue_Lag13 0.0336195495962473 INFO:pyaf.std:AR_MODEL_COEFF 9 _Used_ConstantTrend_residue_zeroCycle_residue_Lag1 -0.018068921193853664 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Used_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.015196122159948602 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Used_ConstantTrend_residue_zeroCycle_residue_Lag12 -0.01519612215994897 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.416978597640991 -INFO:pyaf.std:START_FORECASTING 'Used' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Used' 0.16632843017578125 - Split Transformation ... TestMAPE TestMASE -0 None _Used ... 0.0880 1.1484 -1 None _Used ... 0.0880 1.1484 -2 None _Used ... 0.0859 1.1196 -4 None _Used ... 0.1178 1.5322 -3 None _Used ... 0.0859 1.1196 - -[5 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.59394407272339 +INFO:pyaf.std:START_FORECASTING '['Used']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Used']' 1.0603752136230469 Split Transformation ... TestMAPE TestMASE 0 None _Used ... 0.0880 1.1484 1 None _Used ... 0.0880 1.1484 @@ -178,31 +244,33 @@ Forecasts { - "Dataset": { - "Signal": "Used", - "Time": { - "Horizon": 10, - "TimeMinMax": [ - "2011-11-01 00:00:00", - "2011-12-31 00:00:00" - ], - "TimeVariable": "Date" + "Used": { + "Dataset": { + "Signal": "Used", + "Time": { + "Horizon": 10, + "TimeMinMax": [ + "2011-11-01 00:00:00", + "2011-12-31 00:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 61 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Used_ConstantTrend_residue_zeroCycle_residue_AR(15)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 61 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Used_ConstantTrend_residue_zeroCycle_residue_AR(15)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "10", - "MAE": "13.612426292653016", - "MAPE": "0.0219", - "MASE": "0.5042", - "RMSE": "15.415689563294164" + "Model_Performance": { + "COMPLEXITY": "10", + "MAE": "13.612426292653005", + "MAPE": "0.0219", + "MASE": "0.5042", + "RMSE": "15.415689563294148" + } } } diff --git a/tests/references/real-life_test_sof_example2.log b/tests/references/real-life_test_sof_example2.log index d2e9688d2..d89eea884 100644 --- a/tests/references/real-life_test_sof_example2.log +++ b/tests/references/real-life_test_sof_example2.log @@ -5,6 +5,198 @@ INFO:pyaf.std:START_TRAINING 'dengue_index' 2 2010-01-03 0.174940 3 2010-01-04 0.176245 4 2010-01-05 0.176658 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 68 0.0163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 4 0.0075 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(64) 100 0.0061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 36 0.006 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 100 0.0046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 0.0041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.0048 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.0044 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(64) 100 0.0064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 0.0067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 0.0071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 36 0.0073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 84 0.0061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 0.7886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 84 0.0378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 0.7182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 84 0.0676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 0.6269 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 84 0.0913 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 0.6276 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 84 0.0175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 0.0086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.816 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.816 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 84 0.06 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 20 2.1069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 84 0.0715 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 20 1.9997 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 84 0.0378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 20 1.9756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 84 0.0875 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 20 1.9793 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 84 0.1573 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 20 1.4629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 2.2164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index_PolyTrend_residue_zeroCycle_residue_NoAR 16 2.2164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 100 0.981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR 36 3.4342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 100 0.8781 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 36 1.7436 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 0.9951 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 0.5537 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 100 0.7685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 1.52 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 100 0.9025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR 36 1.3813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.9766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.1576 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.9766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.1576 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(64) 132 0.0166 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 3.0229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 132 0.0348 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 1.5295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 0.0126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 0.4024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(64) 132 0.0101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 1.4114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 132 0.0542 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.2182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0251 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0251 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 0.8916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 3.3438 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 0.7888 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 1.6957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.9052 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.4295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 0.6803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 1.4259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.8017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 1.3096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.0552 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.0552 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 1.251 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 3.9596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 1.1548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 2.129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 1.2647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 1.0872 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 1.0525 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 1.8152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 1.2078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 1.841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 1.2458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.4624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_zeroCycle_residue_AR(64) 112 1.2458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_dengue_index_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.4624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 100 52955622.8966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR 36 6.644 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 52955622.8966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 6.2535 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 52955622.8966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_ConstantTrend_residue_zeroCycle_residue_NoAR 32 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(64) 132 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR 68 0.0992 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 132 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 68 2.114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(64) 132 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR 68 7.5638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(64) 132 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR 68 1.1659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 132 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 2.6933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 52955622.8966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 52955622.8562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 52955622.8966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 52955622.8966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 52955622.8562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 52955622.8562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 52955622.8562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_zeroCycle_residue_AR(64) 112 52955622.8966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_LinearTrend_residue_zeroCycle_residue_NoAR 48 52955622.8242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 52955622.8562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 52955622.8562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_zeroCycle_residue_AR(64) 112 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_dengue_index_PolyTrend_residue_zeroCycle_residue_NoAR 48 52955622.8647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 100 29.8169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 36 25.2452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(64) 132 0.0134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR 68 0.0077 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.0069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 0.3305 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 0.2124 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 0.8846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 0.6661 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.0483 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.6455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 0.5614 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 1.109 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.1994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 1.4262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.7248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.7248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(64) 116 0.3875 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR 52 1.0006 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64) 116 0.7243 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR 52 0.6277 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64) 116 0.0684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR 52 0.5954 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(64) 116 0.5404 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR 52 1.0232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(64) 116 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR 52 1.4259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0118 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0118 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.6062 INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index NoTransf_ConstantTrend_Seasonal_WeekOfYear_AR 68 0.0258 INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index NoTransf_ConstantTrend_Seasonal_WeekOfYear_NoAR 4 0.0311 INFO:pyaf.std:collectPerformanceIndices : MAPE None _dengue_index NoTransf_ConstantTrend_Cycle_None_AR 72 0.0428 @@ -197,7 +389,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index Integrat INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index Integration_PolyTrend_Cycle_None_NoAR 56 0.4279 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index Integration_PolyTrend_NoCycle_AR 112 0.0232 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_dengue_index Integration_PolyTrend_NoCycle_NoAR 48 0.4279 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'dengue_index' 18.68084216117859 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['dengue_index']' 18.450538158416748 INFO:pyaf.std:TIME_DETAIL TimeVariable='date' TimeMin=2010-01-01T00:00:00.000000 TimeMax=2014-06-11T00:00:00.000000 TimeDelta= Horizon=5 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='dengue_index' Length=2034 Min=0.109196345 Max=0.377576611 Mean=0.2029481254252704 StdDev=0.08800258476886157 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_dengue_index' Min=0.109196345 Max=0.377576611 Mean=0.2029481254252704 StdDev=0.08800258476886157 @@ -224,17 +416,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.339584112167358 -INFO:pyaf.std:START_FORECASTING 'dengue_index' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'dengue_index' 0.11529874801635742 - Split Transformation ... TestMAPE TestMASE -12 None _dengue_index ... 0.0069 0.8936 -2 None _dengue_index ... 0.0073 0.9460 -3 None _dengue_index ... 0.0044 0.5759 -6 None _dengue_index ... 0.0041 0.5281 -10 None _dengue_index ... 0.0060 0.7836 - -[5 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.271881580352783 +INFO:pyaf.std:START_FORECASTING '['dengue_index']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['dengue_index']' 0.42999267578125 Split Transformation ... TestMAPE TestMASE 0 None _dengue_index ... 0.0048 0.6216 1 None _dengue_index ... 0.0071 0.9291 @@ -289,31 +483,33 @@ Forecasts { - "Dataset": { - "Signal": "dengue_index", - "Time": { - "Horizon": 5, - "TimeMinMax": [ - "2010-01-01 00:00:00", - "2015-07-27 00:00:00" - ], - "TimeVariable": "date" + "dengue_index": { + "Dataset": { + "Signal": "dengue_index", + "Time": { + "Horizon": 5, + "TimeMinMax": [ + "2010-01-01 00:00:00", + "2015-07-27 00:00:00" + ], + "TimeVariable": "date" + }, + "Training_Signal_Length": 2034 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_dengue_index_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 2034 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_dengue_index_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "0.002337476751231526", - "MAPE": "0.0138", - "MASE": "0.9993", - "RMSE": "0.008197099249173069" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "0.002337476751231526", + "MAPE": "0.0138", + "MASE": "0.9993", + "RMSE": "0.008197099249173069" + } } } diff --git a/tests/references/sampling_test_ozone_with_no_sampling.log b/tests/references/sampling_test_ozone_with_no_sampling.log index e42c1e90e..220bb80a2 100644 --- a/tests/references/sampling_test_ozone_with_no_sampling.log +++ b/tests/references/sampling_test_ozone_with_no_sampling.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.9677512645721436 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 9.431546211242676 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,8 +17,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6135978624272591 L1_Forecast=0.5265316758013037 L1_Test=0.42992050508902385 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507152 L2_Test=0.551943269559555 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -30,19 +30,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033754 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533328 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225498 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.1612820579538937 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046368 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481863 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102252 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045825 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947768 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.33420825004577637 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 2.1129841804504395 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1595 0.1740 1 None _Ozone ... 0.1595 0.1740 @@ -92,31 +92,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013037", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507152" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/sampling_test_ozone_with_sampling.log b/tests/references/sampling_test_ozone_with_sampling.log index 597602b22..8fb115c06 100644 --- a/tests/references/sampling_test_ozone_with_sampling.log +++ b/tests/references/sampling_test_ozone_with_sampling.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.411252021789551 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.924264192581177 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -31,8 +31,8 @@ INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfY INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.16320538520812988 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4891202449798584 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1758 0.2259 1 None _Ozone ... 0.1765 0.2209 @@ -82,31 +82,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5551666232747684", - "MAPE": "0.1765", - "MASE": "0.7151", - "RMSE": "0.662953224721834" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5551666232747684", + "MAPE": "0.1765", + "MASE": "0.7151", + "RMSE": "0.662953224721834" + } } } diff --git a/tests/references/sampling_test_ozone_with_sampling_2.log b/tests/references/sampling_test_ozone_with_sampling_2.log index 8be581569..e9f24bb50 100644 --- a/tests/references/sampling_test_ozone_with_sampling_2.log +++ b/tests/references/sampling_test_ozone_with_sampling_2.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.342358350753784 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.761334180831909 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,8 +17,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1808 MAPE_Forecast=0.159 MAPE_Test=0.2261 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1821 SMAPE_Forecast=0.1637 SMAPE_Test=0.2308 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7927 MASE_Forecast=0.6285 MASE_Test=1.1047 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6967844263001123 L1_Forecast=0.4879277331703874 L1_Test=0.5222402194480161 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.9346346715447164 L2_Forecast=0.6257488589750227 L2_Test=0.6349819486074739 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6967844263001123 L1_Forecast=0.4879277331703874 L1_Test=0.522240219448016 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9346346715447161 L2_Forecast=0.6257488589750227 L2_Test=0.6349819486074736 INFO:pyaf.std:MODEL_COMPLEXITY 24 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -30,19 +30,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear -0.009150326797386477 {1: -1.909150326797386, 2: -1.6091503267973861, 3: -1.409150326797386, 4: -0.10915032679738612, 5: -0.2091503267973862, 6: 0.4908496732026135, 7: 1.3908496732026139, 8: 0.9908496732026135, 9: 0.9908496732026135, 10: 0.7908496732026138, 11: -0.9591503267973862, 12: -1.559150326797386} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.37779934321591213 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag12 0.22876793385895608 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 0.19317371254917867 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.18093591455376062 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.1515472829636597 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag15 0.14168455498022015 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag16 0.1164632719429092 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag14 -0.11616806286775284 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag19 -0.10701284610741732 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 -0.097193749705864 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag1 0.377799343215912 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag12 0.22876793385895577 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 0.19317371254917845 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.1809359145537607 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.1515472829636596 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag15 0.1416845549802201 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag16 0.11646327194290905 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag14 -0.11616806286775261 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag19 -0.10701284610741714 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 -0.09719374970586374 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.24687480926513672 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.8449368476867676 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1589 0.2126 1 None _Ozone ... 0.1590 0.2261 @@ -92,31 +92,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51)", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "24", - "MAE": "0.4879277331703874", - "MAPE": "0.159", - "MASE": "0.6285", - "RMSE": "0.6257488589750227" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51)", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "24", + "MAE": "0.4879277331703874", + "MAPE": "0.159", + "MASE": "0.6285", + "RMSE": "0.6257488589750227" + } } } diff --git a/tests/references/sampling_test_ozone_with_sampling_3.log b/tests/references/sampling_test_ozone_with_sampling_3.log index d56e1e820..f0bcba0ba 100644 --- a/tests/references/sampling_test_ozone_with_sampling_3.log +++ b/tests/references/sampling_test_ozone_with_sampling_3.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.703483581542969 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.461336612701416 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,8 +17,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1998 MAPE_Forecast=0.1721 MAPE_Test=0.2478 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1951 SMAPE_Forecast=0.1598 SMAPE_Test=0.2123 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8453 MASE_Forecast=0.6626 MASE_Test=1.2051 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7430178106930672 L1_Forecast=0.5144076012041215 L1_Test=0.5696838972994179 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.9699258263118073 L2_Forecast=0.6378119821973828 L2_Test=0.6752880873244894 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7430178106930675 L1_Forecast=0.5144076012041214 L1_Test=0.5696838972994185 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9699258263118077 L2_Forecast=0.6378119821973826 L2_Test=0.6752880873244899 INFO:pyaf.std:MODEL_COMPLEXITY 25 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -30,19 +30,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6566689722163859 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag22 0.24736665940262326 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.24241592191181877 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.22215288569514757 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag40 -0.17430706419605915 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag41 0.1546333859247021 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.14312411246491438 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag42 0.14207394641535187 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.14154527370028006 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag24 0.12927596029451405 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6566689722163861 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag22 0.2473666594026237 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.24241592191181904 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.22215288569514768 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag40 -0.17430706419605912 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag41 0.15463338592470205 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.14312411246491472 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag42 0.14207394641535215 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.14154527370028047 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag24 0.12927596029451394 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.2643616199493408 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.2091708183288574 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1651 0.1554 1 None _Ozone ... 0.1651 0.1554 @@ -92,31 +92,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "25", - "MAE": "0.5144076012041215", - "MAPE": "0.1721", - "MASE": "0.6626", - "RMSE": "0.6378119821973828" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "25", + "MAE": "0.5144076012041214", + "MAPE": "0.1721", + "MASE": "0.6626", + "RMSE": "0.6378119821973826" + } } } diff --git a/tests/references/svr_test_air_passengers_svr.log b/tests/references/svr_test_air_passengers_svr.log index fe178fd3d..bd26e5a3d 100644 --- a/tests/references/svr_test_air_passengers_svr.log +++ b/tests/references/svr_test_air_passengers_svr.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 2.6540980339050293 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.734963655471802 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -26,9 +26,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.120751142501831 -INFO:pyaf.std:START_FORECASTING 'AirPassengers' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'AirPassengers' 0.2642087936401367 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.40579652786255 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.8490159511566162 Split Transformation ... ForecastMAPE TestMAPE 0 None _AirPassengers ... 0.0904 0.1013 1 None _AirPassengers ... 0.0907 0.0984 @@ -97,31 +107,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "SVR", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_SVR(33)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "56", - "MAE": "34.2172068845683", - "MAPE": "0.0907", - "MASE": "0.9237", - "RMSE": "42.45442249078107" + "Model": { + "AR_Model": "SVR", + "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_SVR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "56", + "MAE": "34.2172068845683", + "MAPE": "0.0907", + "MASE": "0.9237", + "RMSE": "42.45442249078107" + } } } diff --git a/tests/references/svr_test_air_passengers_svr_only.log b/tests/references/svr_test_air_passengers_svr_only.log index 1857a869e..c2813782c 100644 --- a/tests/references/svr_test_air_passengers_svr_only.log +++ b/tests/references/svr_test_air_passengers_svr_only.log @@ -1,4 +1,36 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(33) 32 0.5102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_SVR(33) 24 0.5102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(33) 64 0.1013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_SVR(33) 56 0.0984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(33) 48 0.1305 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_SVR(33) 40 0.1305 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(33) 48 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_SVR(33) 40 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(33) 64 0.3765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_SVR(33) 56 0.3765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(33) 96 0.6878 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_SVR(33) 88 0.3284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(33) 80 0.4063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_SVR(33) 72 0.4063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(33) 80 0.5564 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_SVR(33) 72 0.7103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(33) 64 0.957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_SVR(33) 56 0.2663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(33) 96 471.4719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_SVR(33) 88 471.4719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(33) 80 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_SVR(33) 72 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(33) 80 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_SVR(33) 72 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(33) 64 1.0001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_SVR(33) 56 1.0001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(33) 96 0.0932 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_SVR(33) 88 0.0999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(33) 80 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_SVR(33) 72 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(33) 80 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_SVR(33) 72 0.2703 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_SVR 32 0.431 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_SVR 24 0.431 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_SVR 64 0.0904 @@ -31,7 +63,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integra INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_SVR 72 0.4864 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_SVR 80 0.2113 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_SVR 72 0.2113 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 3.036264181137085 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.736163377761841 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -58,16 +90,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.138685464859009 -INFO:pyaf.std:START_FORECASTING 'AirPassengers' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'AirPassengers' 0.25855135917663574 - Split Transformation ... TestMAPE TestMASE -1 None _AirPassengers ... 0.0984 0.9427 -0 None _AirPassengers ... 0.1013 0.9716 -2 None CumSum_AirPassengers ... 0.0999 0.9588 -3 None CumSum_AirPassengers ... 0.0932 0.8903 - -[4 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 26.05820941925049 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.6249771118164062 Split Transformation ... TestMAPE TestMASE 0 None _AirPassengers ... 0.1013 0.9716 1 None _AirPassengers ... 0.0984 0.9427 @@ -136,31 +171,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "SVR", + "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_SVR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "SVR", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_SVR(33)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "56", - "MAE": "34.2172068845683", - "MAPE": "0.0907", - "MASE": "0.9237", - "RMSE": "42.45442249078107" + "Model_Performance": { + "COMPLEXITY": "56", + "MAE": "34.2172068845683", + "MAPE": "0.0907", + "MASE": "0.9237", + "RMSE": "42.45442249078107" + } } } diff --git a/tests/references/svr_test_ozone_svr.log b/tests/references/svr_test_ozone_svr.log index e730af6e9..e6bb81962 100644 --- a/tests/references/svr_test_ozone_svr.log +++ b/tests/references/svr_test_ozone_svr.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.865353107452393 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 9.173337697982788 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,8 +17,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6135978624272591 L1_Forecast=0.5265316758013037 L1_Test=0.42992050508902385 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507152 L2_Test=0.551943269559555 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -30,21 +30,31 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033754 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533328 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225498 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.1612820579538937 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046368 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481863 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102252 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045825 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947768 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.327384948730469 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.33736443519592285 +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.71915054321289 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.1890971660614014 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1595 0.1740 1 None _Ozone ... 0.1595 0.1740 @@ -94,31 +104,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013037", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507152" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/svr_test_ozone_svr_only.log b/tests/references/svr_test_ozone_svr_only.log index 795950493..7c5bef4c4 100644 --- a/tests/references/svr_test_ozone_svr_only.log +++ b/tests/references/svr_test_ozone_svr_only.log @@ -5,6 +5,54 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 42 0.5203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 46 0.4442 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 38 0.417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 78 0.2431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 70 0.2475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 58 0.2247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 62 0.2062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 54 0.2062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 58 0.3931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 62 0.4099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 54 0.4099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 0.9543 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 78 1.2139 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 70 2.4717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 1.0249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 110 2.8234 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 102 2.8234 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 1.799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 1.9046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 86 1.9046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 1.1919 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 3.9727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 86 4.3211 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 473.0097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 78 3293.1119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 70 3293.1119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.4635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 102 0.4634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 2756.6141 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 21963.1328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 86 21963.1328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 2054099.7495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 5500927.6236 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 86 7377037.7134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 1.633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 78 1.7156 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 70 1.1841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.2581 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.2581 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 102 0.2217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.4864 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.5152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 86 0.5152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 1.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 1.2452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 86 1.2452 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_SVR 42 0.2478 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_SVR 46 0.1716 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_SVR 38 0.1691 @@ -53,7 +101,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lin INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_SVR 90 0.6068 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_SVR 94 0.694 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_SVR 86 0.694 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.677865266799927 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 8.666393518447876 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -80,16 +128,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.296887397766113 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3379037380218506 - Split Transformation ... TestMAPE TestMASE -2 None _Ozone ... 0.4170 1.9098 -3 None _Ozone ... 0.4442 2.0205 -0 None _Ozone ... 0.4099 1.9519 -1 None _Ozone ... 0.4099 1.9519 - -[4 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 23.756245136260986 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.4069302082061768 Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.4099 1.9519 1 None _Ozone ... 0.4099 1.9519 @@ -139,31 +190,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "SVR", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "SVR", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "38", - "MAE": "0.47525631539058155", - "MAPE": "0.1691", - "MASE": "0.6122", - "RMSE": "0.6236798279968584" + "Model_Performance": { + "COMPLEXITY": "38", + "MAE": "0.47525631539058155", + "MAPE": "0.1691", + "MASE": "0.6122", + "RMSE": "0.6236798279968584" + } } } diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_1.log b/tests/references/temporal_hierarchy_test_temporal_demo_1.log index b8cdeaf43..b824e4187 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_1.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_1.log @@ -1,26 +1,17 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.373321533203125 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.7030074596405029 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-01 00:00:00'), 2: Timestamp('2001-02-08 00:00:00'), 3: Timestamp('2001-02-15 00:00:00'), 4: Timestamp('2001-02-22 00:00:00')}, 'Signal': {0: 5.185958905669871, 1: 52.68214704590134, 2: 97.81781280163595, 3: 61.48599412944952, 4: 47.28876337670961}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA Q {'TH_Q_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-04-26 00:00:00'), 2: Timestamp('2001-07-27 00:00:00'), 3: Timestamp('2001-10-27 00:00:00'), 4: Timestamp('2002-01-25 00:00:00')}, 'Signal': {0: 666.5596094225025, 1: 1077.4457537430067, 2: 1362.2354461702034, 3: 1551.8657635147804, 4: 1719.6917811853032}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, 'Q': 4} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_Q')] +INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_Q']' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:START_TRAINING 'Signal_Q' -INFO:pyaf.std:START_TRAINING 'Signal_W' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_Q' 5.3046019077301025 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_W' 11.972093343734741 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 36.68292188644409 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 73.10181832313538 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_Q')] -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 10.538168907165527 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 11.0652494430542 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 22.81254816055298 +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_Q']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 36.014233350753784 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -29,13 +20,13 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_D_Forecast', - 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', - 'Date', 'TH_W_start', 'Signal_W', 'Signal_W_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'TH_W_start', 'TH_Q_start', 'Signal_D', + 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', + 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', - 'TH_Q_start', 'Signal_Q', 'Signal_Q_Forecast', - 'Signal_Q_Forecast_Lower_Bound', 'Signal_Q_Forecast_Upper_Bound', - 'Signal_D_BU_Forecast', 'Signal_W_BU_Forecast', 'Signal_Q_BU_Forecast', + 'Signal_Q', 'Signal_Q_Forecast', 'Signal_Q_Forecast_Lower_Bound', + 'Signal_Q_Forecast_Upper_Bound', 'Signal_D_BU_Forecast', + 'Signal_W_BU_Forecast', 'Signal_Q_BU_Forecast', 'Signal_Q_AHP_TD_Forecast', 'Signal_W_AHP_TD_Forecast', 'Signal_D_AHP_TD_Forecast', 'Signal_Q_PHA_TD_Forecast', 'Signal_W_PHA_TD_Forecast', 'Signal_D_PHA_TD_Forecast', @@ -43,42 +34,49 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_ 'Signal_D_OC_Forecast', 'Signal_W_OC_Forecast', 'Signal_Q_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_D']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_Q']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_Q']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.9928, 'RMSE': 46.583764952054906, 'MAE': 42.018977664445686, 'SMAPE': 1.9815, 'ErrorMean': -42.0060529036014, 'ErrorStdDev': 20.137990877149086, 'R2': -4.455651136949602, 'Pearson': 0.03738550688143808} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.9917, 'RMSE': 83.30279481877753, 'MAE': 82.67856316960288, 'SMAPE': 1.9814, 'ErrorMean': -82.67856316960288, 'ErrorStdDev': 10.178939867654922, 'R2': -115.12547964092661, 'Pearson': -0.023439686321373686} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 2628, 'MAPE': 0.0667, 'RMSE': 1.359004241502849, 'MAE': 1.0288483172385678, 'SMAPE': 0.043, 'ErrorMean': 1.1274593638202503e-15, 'ErrorStdDev': 1.359004241502849, 'R2': 0.9953567823217537, 'Pearson': 0.9976756899523935} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 657, 'MAPE': 0.0119, 'RMSE': 1.2140180766446607, 'MAE': 0.9828321842412838, 'SMAPE': 0.0119, 'ErrorMean': -0.099861635734604, 'ErrorStdDev': 1.209903940041692, 'R2': 0.9753362742238273, 'Pearson': 0.987676902148217} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 2628, 'MAPE': 0.9011, 'RMSE': 43.793976817124474, 'MAE': 37.24734616379794, 'SMAPE': 1.7358, 'ErrorMean': -36.29450488659023, 'ErrorStdDev': 24.507576797719377, 'R2': -3.82176664278317, 'Pearson': 0.1023848127010454} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 657, 'MAPE': 0.8635, 'RMSE': 77.93534431038971, 'MAE': 72.32360615464947, 'SMAPE': 1.7202, 'ErrorMean': -71.57363564466736, 'ErrorStdDev': 30.840437308562855, 'R2': -100.642957466567, 'Pearson': -0.12772738373583722} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 2628, 'MAPE': 1.3132, 'RMSE': 152.89748402376605, 'MAE': 48.48349913765221, 'SMAPE': 1.0096, 'ErrorMean': -0.03898100867287557, 'ErrorStdDev': 152.897479054688, 'R2': -57.77303228838987, 'Pearson': 0.10427148253978075} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 657, 'MAPE': 1.1376, 'RMSE': 266.48845888942617, 'MAE': 93.5058900562005, 'SMAPE': 0.9995, 'ErrorMean': -1.1808363038732015, 'ErrorStdDev': 266.4858426762758, 'R2': -1187.4069605794614, 'Pearson': -0.045803994533019504} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.9926, 'RMSE': 46.5700525634989, 'MAE': 41.96217303455629, 'SMAPE': 1.9801, 'ErrorMean': -41.88699343129133, 'ErrorStdDev': 20.35312204685088, 'R2': -4.45243976072174, 'Pearson': 0.037385506881438074} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.9901, 'RMSE': 83.28087724448281, 'MAE': 82.5381706505936, 'SMAPE': 1.9794, 'ErrorMean': -82.45033369183778, 'ErrorStdDev': 11.732305345293907, 'R2': -115.06438075452827, 'Pearson': -0.023439686321373696} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.0003, 'RMSE': 8.498930562362506, 'MAE': 0.6435651170497352, 'SMAPE': 0.0003, 'ErrorMean': -0.0036501151029767094, 'ErrorStdDev': 8.498929778537956, 'R2': 0.9996275458905743, 'Pearson': 0.9998137636220039} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.0001, 'RMSE': 11.914572720607884, 'MAE': 1.0852550651437607, 'SMAPE': 0.0001, 'ErrorMean': 0.06395954121396204, 'ErrorStdDev': 11.914401046286013, 'R2': 0.9997669222648048, 'Pearson': 0.9998837759323713} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 2628, 'MAPE': 416634752161.1399, 'RMSE': 450.6794352442406, 'MAE': 83.16230485505324, 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'MAE': 88.68407421986662, 'SMAPE': 0.3075, 'ErrorMean': -78.36592981051007, 'ErrorStdDev': 212.9369913160132, 'R2': -0.22576121275689243, 'Pearson': -0.04222821419904044} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 2628, 'MAPE': 367041329317.9014, 'RMSE': 115.11675494162623, 'MAE': 73.43394219668387, 'SMAPE': 1.934, 'ErrorMean': -0.025676333128462073, 'ErrorStdDev': 115.11675207812435, 'R2': 0.00785032796280194, 'Pearson': 0.10888231818003787} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 657, 'MAPE': 717212043217.6122, 'RMSE': 206.0167745544909, 'MAE': 143.72107742096168, 'SMAPE': 1.9307, 'ErrorMean': -0.2786687774638947, 'ErrorStdDev': 206.01658608361703, 'R2': -0.01051782748666552, 'Pearson': -0.12225344774301827} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 2628, 'MAPE': 0.0071, 'RMSE': 2.177566452719836, 'MAE': 0.6085949655154175, 'SMAPE': 0.0035, 'ErrorMean': 8.651966341066668e-17, 'ErrorStdDev': 2.177566452719836, 'R2': 0.9996449880677393, 'Pearson': 0.99983802455392} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 657, 'MAPE': 0.002, 'RMSE': 3.363940743428132, 'MAE': 1.1924454407452545, 'SMAPE': 0.0021, 'ErrorMean': -1.1924454407452545, 'ErrorStdDev': 3.1455001503960647, 'R2': 0.9997305764918528, 'Pearson': 0.9999843789350714} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 2628, 'MAPE': 232429887128.9266, 'RMSE': 163.12431674249856, 'MAE': 51.8170485045932, 'SMAPE': 1.8436, 'ErrorMean': -0.06465734180133773, 'ErrorStdDev': 163.12430392843106, 'R2': -0.99222106145332, 'Pearson': 0.2918171517123125} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 657, 'MAPE': 508794634061.2192, 'RMSE': 299.67213482798223, 'MAE': 103.11857025782844, 'SMAPE': 1.8441, 'ErrorMean': -1.3596434456024917, 'ErrorStdDev': 299.6690503906955, 'R2': -1.138117342813016, 'Pearson': 0.21062698475013486} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 25570622339.7917, 'RMSE': 125.44085602970705, 'MAE': 44.30724091491697, 'SMAPE': 0.2999, 'ErrorMean': -39.11162140279899, 'ErrorStdDev': 119.18762281675815, 'R2': -0.17808908956975977, 'Pearson': 0.03972284251296412} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 657, 'MAPE': 62652937212.2081, 'RMSE': 229.50641140850678, 'MAE': 89.79029573639485, 'SMAPE': 0.3075, 'ErrorMean': -77.25970829398184, 'ErrorStdDev': 216.1113841331354, 'R2': -0.25408928559465616, 'Pearson': -0.04222821419904043} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 60.59128022193909 +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 376, 'MAPE': 0.9953, 'RMSE': 43.61739434056328, 'MAE': 38.82508906089763, 'SMAPE': 1.9751, 'ErrorMean': -38.79130836674138, 'ErrorStdDev': 19.94270503859951, 'R2': -3.809039955319877, 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+INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 376, 'MAPE': 0.0359, 'RMSE': 5.616083488396741, 'MAE': 3.6072026732867726, 'SMAPE': 0.0261, 'ErrorMean': -0.5507998231415778, 'ErrorStdDev': 5.589008257595413, 'R2': 0.9982639981166463, 'Pearson': 0.9991516950575161} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 94, 'MAPE': 0.0059, 'RMSE': 4.199829627924871, 'MAE': 3.4311219086102214, 'SMAPE': 0.0059, 'ErrorMean': -0.4594524671204664, 'ErrorStdDev': 4.174622418141847, 'R2': 0.9883602842550797, 'Pearson': 0.9942334089102576} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 376, 'MAPE': 0.8153, 'RMSE': 259.6549080593885, 'MAE': 199.75087969813913, 'SMAPE': 0.9173, 'ErrorMean': -163.08916864238967, 'ErrorStdDev': 202.04602037868423, 'R2': -2.710873530959564, 'Pearson': 0.34470726146365005} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 94, 'MAPE': 0.6209, 'RMSE': 363.2995151004631, 'MAE': 362.49301943996755, 'SMAPE': 0.9005, 'ErrorMean': -362.49301943996755, 'ErrorStdDev': 24.19397713330535, 'R2': -86.09809673662467, 'Pearson': 0.9914232675117872} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 376, 'MAPE': 1.0192, 'RMSE': 322.1917471430979, 'MAE': 291.8070999296352, 'SMAPE': 1.9737, 'ErrorMean': -291.2375015888688, 'ErrorStdDev': 137.79782144647865, 'R2': -4.713627261850848, 'Pearson': 0.09778886526916761} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 94, 'MAPE': 1.0, 'RMSE': 585.0828979545455, 'MAE': 583.7864502550929, 'SMAPE': 2.0, 'ErrorMean': -583.7864502550929, 'ErrorStdDev': 38.92785606024334, 'R2': -224.89875435276088, 'Pearson': 0.0} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 109.90229535102844 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-05T00:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_D' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_D' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -90,35 +88,53 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_D_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0667 MAPE_Forecast=0.0119 MAPE_Test=0.0107 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.043 SMAPE_Forecast=0.0119 SMAPE_Test=0.0107 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4456 MASE_Forecast=0.4238 MASE_Test=0.4377 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.0288483172385678 L1_Forecast=0.9828321842412838 L1_Test=1.023698313669492 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.359004241502849 L2_Forecast=1.2140180766446607 L2_Test=1.2923917049609932 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.0288483172385674 L1_Forecast=0.9828321842412836 L1_Test=1.0236983136694886 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.359004241502849 L2_Forecast=1.2140180766446595 L2_Test=1.2923917049609883 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 42.354333411171915 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_D_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag21 0.3938827990494035 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag42 0.33146029445342495 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.25093217429860903 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag63 0.24260881398354753 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15543183068103217 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.11348082264365836 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.10107189648913975 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag3 0.09930723701673971 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.0783585462253969 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.07656432642980247 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag21 0.39388279904940376 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag42 0.33146029445342484 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.25093217429861164 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag63 0.24260881398354692 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15543183068103128 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.11348082264365861 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.10107189648913845 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag3 0.09930723701673916 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.07835854622539506 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.07656432642980306 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_W_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-03T00:00:00.000000 TimeDelta= Horizon=52 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_W' Length=522 Min=5.185958905669871 Max=727.5038824023378 Mean=385.34514038141367 StdDev=185.43607844052733 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_W' Min=5.185958905669871 Max=727.5038824023378 Mean=385.34514038141367 StdDev=185.43607844052733 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_W_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [LinearTrend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_W_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_LinearTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0495 MAPE_Forecast=0.0142 MAPE_Test=0.0176 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0245 SMAPE_Forecast=0.0143 SMAPE_Test=0.0176 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1327 MASE_Forecast=0.257 MASE_Test=0.3593 -INFO:pyaf.std:MODEL_L1 L1_Fit=4.253690344081162 L1_Forecast=8.33443249542162 L1_Test=11.750818189231296 -INFO:pyaf.std:MODEL_L2 L2_Fit=5.756919745613754 L2_Forecast=8.893385028377653 L2_Test=16.642863200847113 -INFO:pyaf.std:MODEL_COMPLEXITY 24 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [Lag1Trend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_W_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0359 MAPE_Forecast=0.0059 MAPE_Test=0.0083 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0261 SMAPE_Forecast=0.0059 SMAPE_Test=0.0081 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1125 MASE_Forecast=0.1058 MASE_Test=0.1667 +INFO:pyaf.std:MODEL_L1 L1_Fit=3.6072026732867726 L1_Forecast=3.4311219086102214 L1_Test=5.451214365608601 +INFO:pyaf.std:MODEL_L2 L2_Fit=5.616083488396741 L2_Forecast=4.199829627924871 L2_Test=14.817188328676421 +INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 5.185958905669871 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_W_Lag1Trend_residue_bestCycle_byMAPE 12 -12.931251865057625 {0: -33.870587229317834, 1: -12.858763848798418, 2: 49.74977134955286, 3: -32.28703382500029, 4: -12.408777313072562, 5: 49.99369185479328, 6: -34.80354276657749, 7: -12.94358249459772, 8: 48.89042849147563, 9: -33.88141364580514, 10: -14.104361860924314, 11: 49.20838455450192} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_Q_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2007-10-27T00:00:00.000000 TimeDelta= Horizon=4 @@ -132,20 +148,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_Q_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0322 MAPE_Forecast=0.0121 MAPE_Test=0.0087 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0304 SMAPE_Forecast=0.012 SMAPE_Test=0.0087 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2752 MASE_Forecast=0.4113 MASE_Test=0.2701 -INFO:pyaf.std:MODEL_L1 L1_Fit=60.060593754145756 L1_Forecast=90.3256350362592 L1_Test=76.07178415772069 -INFO:pyaf.std:MODEL_L2 L2_Fit=82.3175924405223 L2_Forecast=108.02649757719166 L2_Test=90.65303156034186 +INFO:pyaf.std:MODEL_L1 L1_Fit=60.06059375414562 L1_Forecast=90.32563503625931 L1_Test=76.07178415771978 +INFO:pyaf.std:MODEL_L2 L2_Fit=82.31759244052236 L2_Forecast=108.02649757719168 L2_Test=90.65303156034095 INFO:pyaf.std:MODEL_COMPLEXITY 7 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3720.654312352265 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_Q_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8636016521663791 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag3 0.2743247507645108 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.21383199534542124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag6 0.1536123953333636 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.09731926497993784 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag4 0.08513831131586441 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06579488508226555 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8636016521663812 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag3 0.27432475076451124 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.2138319953454227 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag6 0.15361239533336257 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.09731926497994144 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag4 0.08513831131586579 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06579488508226333 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_Q')] +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_Q']' RangeIndex: 3650 entries, 0 to 3649 Data columns (total 2 columns): @@ -155,62 +180,56 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 10.126453638076782 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 10.242431879043579 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 22.09840989112854 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 42.27106428146362 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 22.66479182243347 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 42.38111615180969 Int64Index: 4015 entries, 0 to 4014 -Data columns (total 31 columns): +Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 TH_D_start 4015 non-null datetime64[ns] - 1 Signal_D 3650 non-null float64 - 2 Signal_D_Forecast 4015 non-null float64 - 3 Signal_D_Forecast_Lower_Bound 365 non-null float64 - 4 Signal_D_Forecast_Upper_Bound 365 non-null float64 - 5 Date 4015 non-null datetime64[ns] - 6 TH_W_start 574 non-null datetime64[ns] - 7 Signal_W 3651 non-null float64 - 8 Signal_W_Forecast 4015 non-null float64 + 1 TH_W_start 574 non-null datetime64[ns] + 2 TH_Q_start 44 non-null datetime64[ns] + 3 Signal_D 3650 non-null float64 + 4 Signal_D_Forecast 4015 non-null float64 + 5 Signal_D_Forecast_Lower_Bound 365 non-null float64 + 6 Signal_D_Forecast_Upper_Bound 365 non-null float64 + 7 Signal_W 522 non-null float64 + 8 Signal_W_Forecast 574 non-null float64 9 Signal_W_Forecast_Lower_Bound 52 non-null float64 10 Signal_W_Forecast_Upper_Bound 52 non-null float64 - 11 TH_Q_start 44 non-null datetime64[ns] - 12 Signal_Q 3651 non-null float64 - 13 Signal_Q_Forecast 4015 non-null float64 - 14 Signal_Q_Forecast_Lower_Bound 4 non-null float64 - 15 Signal_Q_Forecast_Upper_Bound 4 non-null float64 - 16 Signal_D_BU_Forecast 4015 non-null float64 - 17 Signal_W_BU_Forecast 4015 non-null float64 - 18 Signal_Q_BU_Forecast 4015 non-null float64 - 19 Signal_Q_AHP_TD_Forecast 4015 non-null float64 - 20 Signal_W_AHP_TD_Forecast 4015 non-null float64 - 21 Signal_D_AHP_TD_Forecast 4015 non-null float64 - 22 Signal_Q_PHA_TD_Forecast 4015 non-null float64 - 23 Signal_W_PHA_TD_Forecast 4015 non-null float64 - 24 Signal_D_PHA_TD_Forecast 4015 non-null float64 - 25 Signal_W_MO_Forecast 4015 non-null float64 - 26 Signal_D_MO_Forecast 4015 non-null float64 - 27 Signal_Q_MO_Forecast 4015 non-null float64 - 28 Signal_D_OC_Forecast 4015 non-null float64 - 29 Signal_W_OC_Forecast 4015 non-null float64 - 30 Signal_Q_OC_Forecast 4015 non-null float64 -dtypes: datetime64[ns](4), float64(27) + 11 Signal_Q 3651 non-null float64 + 12 Signal_Q_Forecast 4015 non-null float64 + 13 Signal_Q_Forecast_Lower_Bound 4 non-null float64 + 14 Signal_Q_Forecast_Upper_Bound 4 non-null float64 + 15 Signal_D_BU_Forecast 4015 non-null float64 + 16 Signal_W_BU_Forecast 4015 non-null float64 + 17 Signal_Q_BU_Forecast 574 non-null float64 + 18 Signal_Q_AHP_TD_Forecast 4015 non-null float64 + 19 Signal_W_AHP_TD_Forecast 4015 non-null float64 + 20 Signal_D_AHP_TD_Forecast 4015 non-null float64 + 21 Signal_Q_PHA_TD_Forecast 4015 non-null float64 + 22 Signal_W_PHA_TD_Forecast 4015 non-null float64 + 23 Signal_D_PHA_TD_Forecast 4015 non-null float64 + 24 Signal_W_MO_Forecast 574 non-null float64 + 25 Signal_D_MO_Forecast 574 non-null float64 + 26 Signal_Q_MO_Forecast 574 non-null float64 + 27 Signal_D_OC_Forecast 574 non-null float64 + 28 Signal_W_OC_Forecast 574 non-null float64 + 29 Signal_Q_OC_Forecast 574 non-null float64 +dtypes: datetime64[ns](3), float64(27) memory usage: 1.1 MB - TH_D_start Signal_D ... Signal_W_OC_Forecast Signal_Q_OC_Forecast -4010 2012-01-18 NaN ... 37.110516 37.110516 -4011 2012-01-19 NaN ... 280.785751 280.785751 -4012 2012-01-20 NaN ... 31.770307 31.770307 -4013 2012-01-21 NaN ... 31.941882 31.941882 -4014 2012-01-22 NaN ... 32.276643 32.276643 + TH_D_start TH_W_start ... Signal_W_OC_Forecast Signal_Q_OC_Forecast +4010 2012-01-18 NaT ... NaN NaN +4011 2012-01-19 2012-01-19 ... 244.897663 244.897663 +4012 2012-01-20 NaT ... NaN NaN +4013 2012-01-21 NaT ... NaN NaN +4014 2012-01-22 NaT ... NaN NaN -[5 rows x 31 columns] +[5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W.log index d46298871..33b6c4549 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W.log @@ -1,26 +1,17 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3759322166442871 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.1210517883300781 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, '2W': 1209600.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-01 00:00:00'), 2: Timestamp('2001-02-08 00:00:00'), 3: Timestamp('2001-02-15 00:00:00'), 4: Timestamp('2001-02-22 00:00:00')}, 'Signal': {0: 5.185958905669871, 1: 52.68214704590134, 2: 97.81781280163595, 3: 61.48599412944952, 4: 47.28876337670961}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 2W {'TH_2W_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-08 00:00:00'), 2: Timestamp('2001-02-22 00:00:00'), 3: Timestamp('2001-03-08 00:00:00'), 4: Timestamp('2001-03-22 00:00:00')}, 'Signal': {0: 5.185958905669871, 1: 150.4999598475373, 2: 108.77475750615913, 3: 164.1990188624757, 4: 167.4702093923979}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, '2W': 26} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_2W')] +INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_2W']' -INFO:pyaf.std:START_TRAINING 'Signal_W' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:START_TRAINING 'Signal_2W' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_W' 10.445603609085083 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_2W' 11.321505069732666 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 33.663509368896484 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W']' 90.66763114929199 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_2W')] -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_2W' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 9.237563371658325 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2W' 10.329071521759033 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 24.416369199752808 +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_2W']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W']' 53.65689539909363 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -29,13 +20,13 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_D_Forecast', - 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', - 'Date', 'TH_W_start', 'Signal_W', 'Signal_W_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'TH_2W_start', 'TH_W_start', 'Signal_D', + 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', + 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', - 'TH_2W_start', 'Signal_2W', 'Signal_2W_Forecast', - 'Signal_2W_Forecast_Lower_Bound', 'Signal_2W_Forecast_Upper_Bound', - 'Signal_D_BU_Forecast', 'Signal_W_BU_Forecast', 'Signal_2W_BU_Forecast', + 'Signal_2W', 'Signal_2W_Forecast', 'Signal_2W_Forecast_Lower_Bound', + 'Signal_2W_Forecast_Upper_Bound', 'Signal_D_BU_Forecast', + 'Signal_W_BU_Forecast', 'Signal_2W_BU_Forecast', 'Signal_2W_AHP_TD_Forecast', 'Signal_W_AHP_TD_Forecast', 'Signal_D_AHP_TD_Forecast', 'Signal_2W_PHA_TD_Forecast', 'Signal_W_PHA_TD_Forecast', 'Signal_D_PHA_TD_Forecast', @@ -44,42 +35,39 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_ 'Signal_2W_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_D']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_2W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_2W']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.0048, 'RMSE': 3.5824530968251445, 'MAE': 0.483440625004177, 'SMAPE': 0.0022, 'ErrorMean': 8.111218444750002e-17, 'ErrorStdDev': 3.5824530968251445, 'R2': 0.9995454435468185, 'Pearson': 0.9997730199974042} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.0004, 'RMSE': 2.0773650668534405, 'MAE': 0.4860562414019848, 'SMAPE': 0.0004, 'ErrorMean': 0.0588053098653743, 'ErrorStdDev': 2.076532580171724, 'R2': 0.9999523317446014, 'Pearson': 0.999976434363682} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_BU_Forecast', 'Length': 2628, 'MAPE': 212398800678.1426, 'RMSE': 122.94845447196349, 'MAE': 42.32713675676068, 'SMAPE': 0.1906, 'ErrorMean': 0.19567377857932547, 'ErrorStdDev': 122.94829876341052, 'R2': 0.4646066790722765, 'Pearson': 0.6816252704283031} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_BU_Forecast', 'Length': 657, 'MAPE': 412518096714.8225, 'RMSE': 221.33855137118843, 'MAE': 83.52681135826171, 'SMAPE': 0.1917, 'ErrorMean': -1.0231920153044487, 'ErrorStdDev': 221.33618637989596, 'R2': 0.4588499092919823, 'Pearson': 0.6774200126432232} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_MO_Forecast', 'Length': 2628, 'MAPE': 212398800678.1426, 'RMSE': 122.94845447196349, 'MAE': 42.32713675676068, 'SMAPE': 0.1906, 'ErrorMean': 0.19567377857932547, 'ErrorStdDev': 122.94829876341052, 'R2': 0.4646066790722765, 'Pearson': 0.6816252704283031} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_MO_Forecast', 'Length': 657, 'MAPE': 412518096714.8225, 'RMSE': 221.33855137118843, 'MAE': 83.52681135826171, 'SMAPE': 0.1917, 'ErrorMean': -1.0231920153044487, 'ErrorStdDev': 221.33618637989596, 'R2': 0.4588499092919823, 'Pearson': 0.6774200126432232} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_OC_Forecast', 'Length': 2628, 'MAPE': 202576520566.171, 'RMSE': 90.31429125571442, 'MAE': 40.41934229122609, 'SMAPE': 1.9022, 'ErrorMean': 0.12189040801006078, 'ErrorStdDev': 90.31420900251769, 'R2': 0.7111051780893203, 'Pearson': 0.9423808009436656} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_OC_Forecast', 'Length': 657, 'MAPE': 395846877200.7134, 'RMSE': 162.8386515623789, 'MAE': 79.52730945928988, 'SMAPE': 1.902, 'ErrorMean': -0.3579340191540575, 'ErrorStdDev': 162.83825817630137, 'R2': 0.7071005177821943, 'Pearson': 0.9470260155136175} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.0048, 'RMSE': 3.5824530968251445, 'MAE': 0.483440625004177, 'SMAPE': 0.0022, 'ErrorMean': 8.111218444750002e-17, 'ErrorStdDev': 3.5824530968251445, 'R2': 0.9995454435468185, 'Pearson': 0.9997730199974042} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.0004, 'RMSE': 2.0773650668534405, 'MAE': 0.4860562414019848, 'SMAPE': 0.0004, 'ErrorMean': 0.0588053098653743, 'ErrorStdDev': 2.076532580171724, 'R2': 0.9999523317446014, 'Pearson': 0.999976434363682} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.9509, 'RMSE': 45.34698890654646, 'MAE': 39.970242641672705, 'SMAPE': 1.8725, 'ErrorMean': -39.60219926266229, 'ErrorStdDev': 22.09106643987197, 'R2': -4.169806988339572, 'Pearson': 0.06870311119128965} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.9345, 'RMSE': 80.89212091429927, 'MAE': 78.08629136862852, 'SMAPE': 1.8632, 'ErrorMean': -77.90417387172116, 'ErrorStdDev': 21.782445211184424, 'R2': -108.5016897494444, 'Pearson': -0.09503136193957647} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 2628, 'MAPE': 0.0667, 'RMSE': 1.359004241502849, 'MAE': 1.0288483172385678, 'SMAPE': 0.043, 'ErrorMean': 1.1274593638202503e-15, 'ErrorStdDev': 1.359004241502849, 'R2': 0.9953567823217537, 'Pearson': 0.9976756899523935} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 657, 'MAPE': 0.0119, 'RMSE': 1.2140180766446607, 'MAE': 0.9828321842412838, 'SMAPE': 0.0119, 'ErrorMean': -0.099861635734604, 'ErrorStdDev': 1.209903940041692, 'R2': 0.9753362742238273, 'Pearson': 0.987676902148217} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 2628, 'MAPE': 0.9011, 'RMSE': 43.793976817124474, 'MAE': 37.24734616379794, 'SMAPE': 1.7358, 'ErrorMean': -36.29450488659023, 'ErrorStdDev': 24.507576797719377, 'R2': -3.82176664278317, 'Pearson': 0.1023848127010454} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 657, 'MAPE': 0.8635, 'RMSE': 77.93534431038971, 'MAE': 72.32360615464947, 'SMAPE': 1.7202, 'ErrorMean': -71.57363564466736, 'ErrorStdDev': 30.840437308562855, 'R2': -100.642957466567, 'Pearson': -0.12772738373583722} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 2628, 'MAPE': 1.3359, 'RMSE': 86.64974935575928, 'MAE': 48.94233108166097, 'SMAPE': 1.04, 'ErrorMean': -0.04810703744080176, 'ErrorStdDev': 86.64973600149544, 'R2': -17.87609009305134, 'Pearson': 0.16414479749750327} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 657, 'MAPE': 1.1659, 'RMSE': 154.8526905169701, 'MAE': 95.48844565429856, 'SMAPE': 1.0336, 'ErrorMean': -0.34838030286557636, 'ErrorStdDev': 154.85229863166094, 'R2': -400.278496128835, 'Pearson': -0.10151421295652757} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.9525, 'RMSE': 45.35310218575732, 'MAE': 39.93968398595214, 'SMAPE': 1.8718, 'ErrorMean': -39.32441914888108, 'ErrorStdDev': 22.594112872048363, 'R2': -4.171200977429726, 'Pearson': 0.06870311119128965} 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-INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.0784, 'RMSE': 87.3115575769596, 'MAE': 22.41935586039547, 'SMAPE': 0.1492, 'ErrorMean': -21.190004872150187, 'ErrorStdDev': 84.70119113703764, 'R2': 0.4292530656436431, 'Pearson': 0.6820839239897298} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.0735, 'RMSE': 156.86376569092553, 'MAE': 43.0089645812401, 'SMAPE': 0.145, 'ErrorMean': -41.62426414468244, 'ErrorStdDev': 151.24041001382957, 'R2': 0.41415316797048873, 'Pearson': 0.6775375250308577} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 58.89303660392761 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 188, 'MAPE': 0.0858, 'RMSE': 9.305079767439297, 'MAE': 5.230081736365968, 'SMAPE': 0.0239, 'ErrorMean': 7.233929767259743e-14, 'ErrorStdDev': 9.305079767439295, 'R2': 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+INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 5.185958905669871 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_W_Lag1Trend_residue_bestCycle_byMAPE 12 -12.931251865057625 {0: -33.870587229317834, 1: -12.858763848798418, 2: 49.74977134955286, 3: -32.28703382500029, 4: -12.408777313072562, 5: 49.99369185479328, 6: -34.80354276657749, 7: -12.94358249459772, 8: 48.89042849147563, 9: -33.88141364580514, 10: -14.104361860924314, 11: 49.20838455450192} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_2W_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-03-27T00:00:00.000000 TimeDelta= Horizon=26 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_2W' Length=261 Min=5.185958905669871 Max=1416.2759918556064 Mean=768.281349103739 StdDev=370.0800641476012 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_2W' Min=5.185958905669871 Max=1416.2759918556064 Mean=768.281349103739 StdDev=370.0800641476012 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_2W_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [Lag1Trend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_2W_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_2W_Lag1Trend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_2W_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.067 MAPE_Forecast=0.0059 MAPE_Test=0.0045 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0307 SMAPE_Forecast=0.0059 SMAPE_Test=0.0045 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2135 MASE_Forecast=0.2086 MASE_Test=0.1761 -INFO:pyaf.std:MODEL_L1 L1_Fit=6.757882779313707 L1_Forecast=6.794445757470298 L1_Test=5.996079560219116 -INFO:pyaf.std:MODEL_L2 L2_Fit=13.394122572149948 L2_Forecast=7.766879727524188 L2_Test=7.53267466562269 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_2W_ConstantTrend_residue_zeroCycle_residue_AR(64)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_2W_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_2W_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_2W_ConstantTrend_residue_zeroCycle_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0858 MAPE_Forecast=0.0047 MAPE_Test=0.0051 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0239 SMAPE_Forecast=0.0047 SMAPE_Test=0.0052 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1652 MASE_Forecast=0.1681 MASE_Test=0.2033 +INFO:pyaf.std:MODEL_L1 L1_Fit=5.230081736365968 L1_Forecast=5.476652137145135 L1_Test=6.9216977079398525 +INFO:pyaf.std:MODEL_L2 L2_Fit=9.305079767439297 L2_Forecast=6.665620872737207 L2_Test=8.155409085933165 +INFO:pyaf.std:MODEL_COMPLEXITY 47 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 589.6831644569942 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_2W_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag2 0.37019707660509715 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.3473768256515174 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag12 0.28584191726761304 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag6 0.2610151596511644 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag3 0.2195263261523463 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.19600777140990627 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag9 0.1954425931230418 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.19400765811166498 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag1 0.18856531420749578 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag15 0.16844711662165468 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_2W')] +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_2W']' RangeIndex: 3650 entries, 0 to 3649 Data columns (total 2 columns): @@ -149,62 +174,56 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_2W' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 9.15406322479248 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2W' 10.236759662628174 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 23.925971746444702 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W']' 44.5496187210083 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 24.23978281021118 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 44.74139928817749 Int64Index: 4015 entries, 0 to 4014 -Data columns (total 31 columns): +Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 TH_D_start 4015 non-null datetime64[ns] - 1 Signal_D 3650 non-null float64 - 2 Signal_D_Forecast 4015 non-null float64 - 3 Signal_D_Forecast_Lower_Bound 365 non-null float64 - 4 Signal_D_Forecast_Upper_Bound 365 non-null float64 - 5 Date 4015 non-null datetime64[ns] - 6 TH_W_start 574 non-null datetime64[ns] - 7 Signal_W 3651 non-null float64 - 8 Signal_W_Forecast 4015 non-null float64 + 1 TH_2W_start 287 non-null datetime64[ns] + 2 TH_W_start 574 non-null datetime64[ns] + 3 Signal_D 3650 non-null float64 + 4 Signal_D_Forecast 4015 non-null float64 + 5 Signal_D_Forecast_Lower_Bound 365 non-null float64 + 6 Signal_D_Forecast_Upper_Bound 365 non-null float64 + 7 Signal_W 522 non-null float64 + 8 Signal_W_Forecast 574 non-null float64 9 Signal_W_Forecast_Lower_Bound 52 non-null float64 10 Signal_W_Forecast_Upper_Bound 52 non-null float64 - 11 TH_2W_start 287 non-null datetime64[ns] - 12 Signal_2W 3651 non-null float64 - 13 Signal_2W_Forecast 4015 non-null float64 - 14 Signal_2W_Forecast_Lower_Bound 26 non-null float64 - 15 Signal_2W_Forecast_Upper_Bound 26 non-null float64 - 16 Signal_D_BU_Forecast 4015 non-null float64 - 17 Signal_W_BU_Forecast 4015 non-null float64 - 18 Signal_2W_BU_Forecast 4015 non-null float64 - 19 Signal_2W_AHP_TD_Forecast 4015 non-null float64 - 20 Signal_W_AHP_TD_Forecast 4015 non-null float64 - 21 Signal_D_AHP_TD_Forecast 4015 non-null float64 - 22 Signal_2W_PHA_TD_Forecast 4015 non-null float64 - 23 Signal_W_PHA_TD_Forecast 4015 non-null float64 - 24 Signal_D_PHA_TD_Forecast 4015 non-null float64 - 25 Signal_W_MO_Forecast 4015 non-null float64 - 26 Signal_D_MO_Forecast 4015 non-null float64 - 27 Signal_2W_MO_Forecast 4015 non-null float64 - 28 Signal_D_OC_Forecast 4015 non-null float64 - 29 Signal_W_OC_Forecast 4015 non-null float64 - 30 Signal_2W_OC_Forecast 4015 non-null float64 -dtypes: datetime64[ns](4), float64(27) + 11 Signal_2W 3651 non-null float64 + 12 Signal_2W_Forecast 4015 non-null float64 + 13 Signal_2W_Forecast_Lower_Bound 26 non-null float64 + 14 Signal_2W_Forecast_Upper_Bound 26 non-null float64 + 15 Signal_D_BU_Forecast 4015 non-null float64 + 16 Signal_W_BU_Forecast 4015 non-null float64 + 17 Signal_2W_BU_Forecast 574 non-null float64 + 18 Signal_2W_AHP_TD_Forecast 4015 non-null float64 + 19 Signal_W_AHP_TD_Forecast 4015 non-null float64 + 20 Signal_D_AHP_TD_Forecast 4015 non-null float64 + 21 Signal_2W_PHA_TD_Forecast 4015 non-null float64 + 22 Signal_W_PHA_TD_Forecast 4015 non-null float64 + 23 Signal_D_PHA_TD_Forecast 4015 non-null float64 + 24 Signal_W_MO_Forecast 574 non-null float64 + 25 Signal_D_MO_Forecast 574 non-null float64 + 26 Signal_2W_MO_Forecast 574 non-null float64 + 27 Signal_D_OC_Forecast 574 non-null float64 + 28 Signal_W_OC_Forecast 574 non-null float64 + 29 Signal_2W_OC_Forecast 574 non-null float64 +dtypes: datetime64[ns](3), float64(27) memory usage: 1.1 MB - TH_D_start Signal_D ... Signal_W_OC_Forecast Signal_2W_OC_Forecast -4010 2012-01-18 NaN ... 37.110516 37.110516 -4011 2012-01-19 NaN ... 280.785751 280.785751 -4012 2012-01-20 NaN ... 31.770307 31.770307 -4013 2012-01-21 NaN ... 31.941882 31.941882 -4014 2012-01-22 NaN ... 32.276643 32.276643 + TH_D_start TH_2W_start ... Signal_W_OC_Forecast Signal_2W_OC_Forecast +4010 2012-01-18 NaT ... NaN NaN +4011 2012-01-19 NaT ... 244.897663 244.897663 +4012 2012-01-20 NaT ... NaN NaN +4013 2012-01-21 NaT ... NaN NaN +4014 2012-01-22 NaT ... NaN NaN -[5 rows x 31 columns] +[5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W_Q.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W_Q.log index a40146f92..8b4a6671b 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W_Q.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W_Q.log @@ -1,31 +1,18 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3584778308868408 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.913963794708252 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, '2W': 1209600.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-01 00:00:00'), 2: Timestamp('2001-02-08 00:00:00'), 3: Timestamp('2001-02-15 00:00:00'), 4: Timestamp('2001-02-22 00:00:00')}, 'Signal': {0: 5.185958905669871, 1: 52.68214704590134, 2: 97.81781280163595, 3: 61.48599412944952, 4: 47.28876337670961}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 2W {'TH_2W_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-08 00:00:00'), 2: Timestamp('2001-02-22 00:00:00'), 3: Timestamp('2001-03-08 00:00:00'), 4: Timestamp('2001-03-22 00:00:00')}, 'Signal': {0: 5.185958905669871, 1: 150.4999598475373, 2: 108.77475750615913, 3: 164.1990188624757, 4: 167.4702093923979}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA Q {'TH_Q_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-04-26 00:00:00'), 2: Timestamp('2001-07-27 00:00:00'), 3: Timestamp('2001-10-27 00:00:00'), 4: Timestamp('2002-01-25 00:00:00')}, 'Signal': {0: 666.5596094225025, 1: 1077.4457537430067, 2: 1362.2354461702034, 3: 1551.8657635147804, 4: 1719.6917811853032}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, '2W': 26, 'Q': 4} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_2W'), (3, 'Signal_Q')] +INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:START_TRAINING 'Signal_W' -INFO:pyaf.std:START_TRAINING 'Signal_2W' -INFO:pyaf.std:START_TRAINING 'Signal_Q' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_Q' 7.812517166137695 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_2W' 10.278740644454956 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_W' 12.924316167831421 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 35.66199278831482 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' 81.93476343154907 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_2W'), (3, 'Signal_Q')] -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_2W' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2W' 10.922086000442505 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 11.428718328475952 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 12.920408248901367 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 25.173038005828857 +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' 47.417360067367554 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -34,13 +21,12 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_D_Forecast', - 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', - 'Date', 'TH_W_start', 'Signal_W', 'Signal_W_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_Q_start', 'TH_W_start', 'TH_D_start', 'TH_2W_start', 'Signal_D', + 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', + 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', - 'TH_2W_start', 'Signal_2W', 'Signal_2W_Forecast', - 'Signal_2W_Forecast_Lower_Bound', 'Signal_2W_Forecast_Upper_Bound', - 'TH_Q_start', 'Signal_Q', 'Signal_Q_Forecast', + 'Signal_2W', 'Signal_2W_Forecast', 'Signal_2W_Forecast_Lower_Bound', + 'Signal_2W_Forecast_Upper_Bound', 'Signal_Q', 'Signal_Q_Forecast', 'Signal_Q_Forecast_Lower_Bound', 'Signal_Q_Forecast_Upper_Bound', 'Signal_D_BU_Forecast', 'Signal_W_BU_Forecast', 'Signal_2W_BU_Forecast', 'Signal_Q_BU_Forecast', 'Signal_Q_AHP_TD_Forecast', @@ -53,54 +39,60 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_ 'Signal_Q_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_D']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_2W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_2W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (3, ['Signal_Q']) -INFO:pyaf.hierarchical:MODEL_LEVEL (3, ['Signal_Q']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 39361348573.8146, 'RMSE': 177.21165370576531, 'MAE': 45.720710175962026, 'SMAPE': 0.1616, 'ErrorMean': -37.77890216577285, 'ErrorStdDev': 173.1378778900823, 'R2': -0.11227290555363512, 'Pearson': 0.023856724462791197} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 657, 'MAPE': 84449379455.7797, 'RMSE': 322.78975945369183, 'MAE': 91.80068653531835, 'SMAPE': 0.1644, 'ErrorMean': -74.91081064417672, 'ErrorStdDev': 313.97706804288197, 'R2': -0.15091461643655202, 'Pearson': -0.0287047768691906} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_BU_Forecast', 'Length': 2628, 'MAPE': 212398800678.1426, 'RMSE': 122.94845447196349, 'MAE': 42.32713675676068, 'SMAPE': 0.1906, 'ErrorMean': 0.19567377857932547, 'ErrorStdDev': 122.94829876341052, 'R2': 0.4646066790722765, 'Pearson': 0.6816252704283031} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_BU_Forecast', 'Length': 657, 'MAPE': 412518096714.8225, 'RMSE': 221.33855137118843, 'MAE': 83.52681135826171, 'SMAPE': 0.1917, 'ErrorMean': -1.0231920153044487, 'ErrorStdDev': 221.33618637989596, 'R2': 0.4588499092919823, 'Pearson': 0.6774200126432232} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_MO_Forecast', 'Length': 2628, 'MAPE': 0.0048, 'RMSE': 3.5824530968251445, 'MAE': 0.483440625004177, 'SMAPE': 0.0022, 'ErrorMean': 8.111218444750002e-17, 'ErrorStdDev': 3.5824530968251445, 'R2': 0.9995454435468185, 'Pearson': 0.9997730199974042} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_MO_Forecast', 'Length': 657, 'MAPE': 0.0004, 'RMSE': 2.0773650668534405, 'MAE': 0.4860562414019848, 'SMAPE': 0.0004, 'ErrorMean': 0.0588053098653743, 'ErrorStdDev': 2.076532580171724, 'R2': 0.9999523317446014, 'Pearson': 0.999976434363682} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_OC_Forecast', 'Length': 2628, 'MAPE': 246219926348.833, 'RMSE': 153.23463229271093, 'MAE': 50.6408930371739, 'SMAPE': 1.9197, 'ErrorMean': 0.09826232758349106, 'ErrorStdDev': 153.23460078715024, 'R2': 0.1683498647205115, 'Pearson': 0.4960507142263714} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_OC_Forecast', 'Length': 657, 'MAPE': 499178122642.028, 'RMSE': 279.07970484642146, 'MAE': 100.71371571605181, 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'ErrorMean': -21.190004872150187, 'ErrorStdDev': 84.70119113703764, 'R2': 0.4292530656436431, 'Pearson': 0.6820839239897298} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 657, 'MAPE': 0.0735, 'RMSE': 156.86376569092553, 'MAE': 43.0089645812401, 'SMAPE': 0.145, 'ErrorMean': -41.62426414468244, 'ErrorStdDev': 151.24041001382957, 'R2': 0.41415316797048873, 'Pearson': 0.6775375250308577} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 2628, 'MAPE': 174322415346.6956, 'RMSE': 126.1184463061527, 'MAE': 38.89207809624948, 'SMAPE': 1.8122, 'ErrorMean': -0.09741145099583325, 'ErrorStdDev': 126.11840868678584, 'R2': -0.1908507640499415, 'Pearson': 0.4785557589304099} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 657, 'MAPE': 381595975545.9143, 'RMSE': 231.56955491397034, 'MAE': 77.36653972226519, 'SMAPE': 1.8117, 'ErrorMean': -1.0473446130957265, 'ErrorStdDev': 231.56718643261127, 'R2': -0.27673785291159114, 'Pearson': 0.4170519592426979} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 25570622339.7917, 'RMSE': 125.44085602970705, 'MAE': 44.30724091491697, 'SMAPE': 0.2999, 'ErrorMean': -39.11162140279899, 'ErrorStdDev': 119.18762281675815, 'R2': -0.17808908956975977, 'Pearson': 0.03972284251296412} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 657, 'MAPE': 62652937212.2081, 'RMSE': 229.50641140850678, 'MAE': 89.79029573639485, 'SMAPE': 0.3075, 'ErrorMean': -77.25970829398184, 'ErrorStdDev': 216.1113841331354, 'R2': -0.25408928559465616, 'Pearson': -0.04222821419904043} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 61.926453828811646 +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 188, 'MAPE': 1.0812, 'RMSE': 642.4460201500355, 'MAE': 584.0950209428194, 'SMAPE': 1.9818, 'ErrorMean': -583.1229643448361, 'ErrorStdDev': 269.6377148329045, 'R2': -4.746411697968978, 'Pearson': 0.08239281150044506} 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[Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0359 MAPE_Forecast=0.0059 MAPE_Test=0.0083 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0261 SMAPE_Forecast=0.0059 SMAPE_Test=0.0081 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1125 MASE_Forecast=0.1058 MASE_Test=0.1667 +INFO:pyaf.std:MODEL_L1 L1_Fit=3.6072026732867726 L1_Forecast=3.4311219086102214 L1_Test=5.451214365608601 +INFO:pyaf.std:MODEL_L2 L2_Fit=5.616083488396741 L2_Forecast=4.199829627924871 L2_Test=14.817188328676421 +INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 5.185958905669871 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_W_Lag1Trend_residue_bestCycle_byMAPE 12 -12.931251865057625 {0: -33.870587229317834, 1: -12.858763848798418, 2: 49.74977134955286, 3: -32.28703382500029, 4: -12.408777313072562, 5: 49.99369185479328, 6: -34.80354276657749, 7: -12.94358249459772, 8: 48.89042849147563, 9: -33.88141364580514, 10: -14.104361860924314, 11: 49.20838455450192} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_2W_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-03-27T00:00:00.000000 TimeDelta= Horizon=26 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_2W' Length=261 Min=5.185958905669871 Max=1416.2759918556064 Mean=768.281349103739 StdDev=370.0800641476012 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_2W' Min=5.185958905669871 Max=1416.2759918556064 Mean=768.281349103739 StdDev=370.0800641476012 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_2W_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [Lag1Trend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_2W_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_2W_Lag1Trend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_2W_Lag1Trend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.067 MAPE_Forecast=0.0059 MAPE_Test=0.0045 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0307 SMAPE_Forecast=0.0059 SMAPE_Test=0.0045 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2135 MASE_Forecast=0.2086 MASE_Test=0.1761 -INFO:pyaf.std:MODEL_L1 L1_Fit=6.757882779313707 L1_Forecast=6.794445757470298 L1_Test=5.996079560219116 -INFO:pyaf.std:MODEL_L2 L2_Fit=13.394122572149948 L2_Forecast=7.766879727524188 L2_Test=7.53267466562269 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_2W_ConstantTrend_residue_zeroCycle_residue_AR(64)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_2W_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_2W_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_2W_ConstantTrend_residue_zeroCycle_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0858 MAPE_Forecast=0.0047 MAPE_Test=0.0051 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0239 SMAPE_Forecast=0.0047 SMAPE_Test=0.0052 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1652 MASE_Forecast=0.1681 MASE_Test=0.2033 +INFO:pyaf.std:MODEL_L1 L1_Fit=5.230081736365968 L1_Forecast=5.476652137145135 L1_Test=6.9216977079398525 +INFO:pyaf.std:MODEL_L2 L2_Fit=9.305079767439297 L2_Forecast=6.665620872737207 L2_Test=8.155409085933165 +INFO:pyaf.std:MODEL_COMPLEXITY 47 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 589.6831644569942 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_2W_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag2 0.37019707660509715 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.3473768256515174 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag12 0.28584191726761304 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag6 0.2610151596511644 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag3 0.2195263261523463 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.19600777140990627 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag9 0.1954425931230418 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.19400765811166498 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag1 0.18856531420749578 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_2W_ConstantTrend_residue_zeroCycle_residue_Lag15 0.16844711662165468 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_Q_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2007-10-27T00:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_Q' Length=40 Min=666.5596094225025 Max=9129.148140134008 Mean=4973.439747231688 StdDev=2417.7870366430593 @@ -170,20 +199,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_Q_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0322 MAPE_Forecast=0.0121 MAPE_Test=0.0087 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0304 SMAPE_Forecast=0.012 SMAPE_Test=0.0087 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2752 MASE_Forecast=0.4113 MASE_Test=0.2701 -INFO:pyaf.std:MODEL_L1 L1_Fit=60.060593754145756 L1_Forecast=90.3256350362592 L1_Test=76.07178415772069 -INFO:pyaf.std:MODEL_L2 L2_Fit=82.3175924405223 L2_Forecast=108.02649757719166 L2_Test=90.65303156034186 +INFO:pyaf.std:MODEL_L1 L1_Fit=60.06059375414562 L1_Forecast=90.32563503625931 L1_Test=76.07178415771978 +INFO:pyaf.std:MODEL_L2 L2_Fit=82.31759244052236 L2_Forecast=108.02649757719168 L2_Test=90.65303156034095 INFO:pyaf.std:MODEL_COMPLEXITY 7 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3720.654312352265 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_Q_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8636016521663791 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag3 0.2743247507645108 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.21383199534542124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag6 0.1536123953333636 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.09731926497993784 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag4 0.08513831131586441 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06579488508226555 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8636016521663812 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag3 0.27432475076451124 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.2138319953454227 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag6 0.15361239533336257 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.09731926497994144 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag4 0.08513831131586579 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06579488508226333 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_2W'), (3, 'Signal_Q')] +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' RangeIndex: 3650 entries, 0 to 3649 Data columns (total 2 columns): @@ -193,74 +231,66 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_2W' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 8.830187797546387 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2W' 10.24690580368042 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 10.25973105430603 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 22.70530080795288 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' 45.58655595779419 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 23.18331241607666 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 45.82208704948425 Int64Index: 4015 entries, 0 to 4014 -Data columns (total 41 columns): +Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_D_start 4015 non-null datetime64[ns] - 1 Signal_D 3650 non-null float64 - 2 Signal_D_Forecast 4015 non-null float64 - 3 Signal_D_Forecast_Lower_Bound 365 non-null float64 - 4 Signal_D_Forecast_Upper_Bound 365 non-null float64 - 5 Date 4015 non-null datetime64[ns] - 6 TH_W_start 574 non-null datetime64[ns] - 7 Signal_W 3651 non-null float64 - 8 Signal_W_Forecast 4015 non-null float64 - 9 Signal_W_Forecast_Lower_Bound 52 non-null float64 - 10 Signal_W_Forecast_Upper_Bound 52 non-null float64 - 11 TH_2W_start 287 non-null datetime64[ns] - 12 Signal_2W 3651 non-null float64 - 13 Signal_2W_Forecast 4015 non-null float64 + 0 TH_Q_start 44 non-null datetime64[ns] + 1 TH_W_start 574 non-null datetime64[ns] + 2 TH_D_start 4015 non-null datetime64[ns] + 3 TH_2W_start 287 non-null datetime64[ns] + 4 Signal_D 3650 non-null float64 + 5 Signal_D_Forecast 4015 non-null float64 + 6 Signal_D_Forecast_Lower_Bound 365 non-null float64 + 7 Signal_D_Forecast_Upper_Bound 365 non-null float64 + 8 Signal_W 522 non-null float64 + 9 Signal_W_Forecast 574 non-null float64 + 10 Signal_W_Forecast_Lower_Bound 52 non-null float64 + 11 Signal_W_Forecast_Upper_Bound 52 non-null float64 + 12 Signal_2W 261 non-null float64 + 13 Signal_2W_Forecast 287 non-null float64 14 Signal_2W_Forecast_Lower_Bound 26 non-null float64 15 Signal_2W_Forecast_Upper_Bound 26 non-null float64 - 16 TH_Q_start 44 non-null datetime64[ns] - 17 Signal_Q 3651 non-null float64 - 18 Signal_Q_Forecast 4015 non-null float64 - 19 Signal_Q_Forecast_Lower_Bound 4 non-null float64 - 20 Signal_Q_Forecast_Upper_Bound 4 non-null float64 - 21 Signal_D_BU_Forecast 4015 non-null float64 - 22 Signal_W_BU_Forecast 4015 non-null float64 - 23 Signal_2W_BU_Forecast 4015 non-null float64 - 24 Signal_Q_BU_Forecast 4015 non-null float64 - 25 Signal_Q_AHP_TD_Forecast 4015 non-null float64 - 26 Signal_2W_AHP_TD_Forecast 4015 non-null float64 - 27 Signal_W_AHP_TD_Forecast 4015 non-null float64 - 28 Signal_D_AHP_TD_Forecast 4015 non-null float64 - 29 Signal_Q_PHA_TD_Forecast 4015 non-null float64 - 30 Signal_2W_PHA_TD_Forecast 4015 non-null float64 - 31 Signal_W_PHA_TD_Forecast 4015 non-null float64 - 32 Signal_D_PHA_TD_Forecast 4015 non-null float64 - 33 Signal_2W_MO_Forecast 4015 non-null float64 - 34 Signal_W_MO_Forecast 4015 non-null float64 - 35 Signal_D_MO_Forecast 4015 non-null float64 - 36 Signal_Q_MO_Forecast 4015 non-null float64 - 37 Signal_D_OC_Forecast 4015 non-null float64 - 38 Signal_W_OC_Forecast 4015 non-null float64 - 39 Signal_2W_OC_Forecast 4015 non-null float64 - 40 Signal_Q_OC_Forecast 4015 non-null float64 -dtypes: datetime64[ns](5), float64(36) + 16 Signal_Q 3651 non-null float64 + 17 Signal_Q_Forecast 4015 non-null float64 + 18 Signal_Q_Forecast_Lower_Bound 4 non-null float64 + 19 Signal_Q_Forecast_Upper_Bound 4 non-null float64 + 20 Signal_D_BU_Forecast 4015 non-null float64 + 21 Signal_W_BU_Forecast 4015 non-null float64 + 22 Signal_2W_BU_Forecast 574 non-null float64 + 23 Signal_Q_BU_Forecast 287 non-null float64 + 24 Signal_Q_AHP_TD_Forecast 4015 non-null float64 + 25 Signal_2W_AHP_TD_Forecast 4015 non-null float64 + 26 Signal_W_AHP_TD_Forecast 4015 non-null float64 + 27 Signal_D_AHP_TD_Forecast 4015 non-null float64 + 28 Signal_Q_PHA_TD_Forecast 4015 non-null float64 + 29 Signal_2W_PHA_TD_Forecast 4015 non-null float64 + 30 Signal_W_PHA_TD_Forecast 4015 non-null float64 + 31 Signal_D_PHA_TD_Forecast 4015 non-null float64 + 32 Signal_2W_MO_Forecast 287 non-null float64 + 33 Signal_W_MO_Forecast 287 non-null float64 + 34 Signal_D_MO_Forecast 287 non-null float64 + 35 Signal_Q_MO_Forecast 287 non-null float64 + 36 Signal_D_OC_Forecast 287 non-null float64 + 37 Signal_W_OC_Forecast 287 non-null float64 + 38 Signal_2W_OC_Forecast 287 non-null float64 + 39 Signal_Q_OC_Forecast 287 non-null float64 +dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_D_start Signal_D ... Signal_2W_OC_Forecast Signal_Q_OC_Forecast -4010 2012-01-18 NaN ... 27.832887 27.832887 -4011 2012-01-19 NaN ... 210.589313 210.589313 -4012 2012-01-20 NaN ... 23.827730 23.827730 -4013 2012-01-21 NaN ... 23.956411 23.956411 -4014 2012-01-22 NaN ... 24.207482 24.207482 + TH_Q_start TH_W_start ... Signal_2W_OC_Forecast Signal_Q_OC_Forecast +4010 NaT NaT ... NaN NaN +4011 NaT 2012-01-19 ... NaN NaN +4012 NaT NaT ... NaN NaN +4013 NaT NaT ... NaN NaN +4014 NaT NaT ... NaN NaN -[5 rows x 41 columns] +[5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M.log index 15bd21598..4f234dea8 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M.log @@ -1,26 +1,17 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3515353202819824 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.7596802711486816 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, 'M': 2419200.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-01 00:00:00'), 2: Timestamp('2001-02-08 00:00:00'), 3: Timestamp('2001-02-15 00:00:00'), 4: Timestamp('2001-02-22 00:00:00')}, 'Signal': {0: 5.185958905669871, 1: 52.68214704590134, 2: 97.81781280163595, 3: 61.48599412944952, 4: 47.28876337670961}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA M {'TH_M_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-22 00:00:00'), 2: Timestamp('2001-03-25 00:00:00'), 3: Timestamp('2001-04-24 00:00:00'), 4: Timestamp('2001-05-25 00:00:00')}, 'Signal': {0: 23.123051573831955, 1: 276.6131975356409, 2: 366.8233603130296, 3: 318.36900358848874, 4: 420.71153575897733}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, 'M': 13} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_M')] +INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_M']' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:START_TRAINING 'Signal_W' -INFO:pyaf.std:START_TRAINING 'Signal_M' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_M' 8.391285419464111 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_W' 11.709087371826172 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 36.70508670806885 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M']' 71.53393411636353 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_M')] -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 11.104697704315186 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 13.933985233306885 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 26.39169931411743 +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_M']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M']' 40.859419107437134 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -29,13 +20,13 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_D_Forecast', - 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', - 'Date', 'TH_W_start', 'Signal_W', 'Signal_W_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'TH_W_start', 'TH_D_start', 'Signal_D', + 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', + 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', - 'TH_M_start', 'Signal_M', 'Signal_M_Forecast', - 'Signal_M_Forecast_Lower_Bound', 'Signal_M_Forecast_Upper_Bound', - 'Signal_D_BU_Forecast', 'Signal_W_BU_Forecast', 'Signal_M_BU_Forecast', + 'Signal_M', 'Signal_M_Forecast', 'Signal_M_Forecast_Lower_Bound', + 'Signal_M_Forecast_Upper_Bound', 'Signal_D_BU_Forecast', + 'Signal_W_BU_Forecast', 'Signal_M_BU_Forecast', 'Signal_M_AHP_TD_Forecast', 'Signal_W_AHP_TD_Forecast', 'Signal_D_AHP_TD_Forecast', 'Signal_M_PHA_TD_Forecast', 'Signal_W_PHA_TD_Forecast', 'Signal_D_PHA_TD_Forecast', @@ -43,42 +34,39 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_ 'Signal_D_OC_Forecast', 'Signal_W_OC_Forecast', 'Signal_M_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_D']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_M']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.9741, 'RMSE': 46.05967770448167, 'MAE': 41.17726431651522, 'SMAPE': 1.9406, 'ErrorMean': -41.12116355151006, 'ErrorStdDev': 20.749549836338293, 'R2': -4.333584860367554, 'Pearson': 0.0729223149927666} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.9718, 'RMSE': 82.37276884676692, 'MAE': 80.99433577791926, 'SMAPE': 1.9401, 'ErrorMean': -80.99433577791926, 'ErrorStdDev': 15.006352634020088, 'R2': -112.54701066740974, 'Pearson': 0.013362955505336746} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 2628, 'MAPE': 0.0667, 'RMSE': 1.359004241502849, 'MAE': 1.0288483172385678, 'SMAPE': 0.043, 'ErrorMean': 1.1274593638202503e-15, 'ErrorStdDev': 1.359004241502849, 'R2': 0.9953567823217537, 'Pearson': 0.9976756899523935} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 657, 'MAPE': 0.0119, 'RMSE': 1.2140180766446607, 'MAE': 0.9828321842412838, 'SMAPE': 0.0119, 'ErrorMean': -0.099861635734604, 'ErrorStdDev': 1.209903940041692, 'R2': 0.9753362742238273, 'Pearson': 0.987676902148217} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 2628, 'MAPE': 0.9011, 'RMSE': 43.793976817124474, 'MAE': 37.24734616379794, 'SMAPE': 1.7358, 'ErrorMean': -36.29450488659023, 'ErrorStdDev': 24.507576797719377, 'R2': -3.82176664278317, 'Pearson': 0.1023848127010454} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 657, 'MAPE': 0.8635, 'RMSE': 77.93534431038971, 'MAE': 72.32360615464947, 'SMAPE': 1.7202, 'ErrorMean': -71.57363564466736, 'ErrorStdDev': 30.840437308562855, 'R2': -100.642957466567, 'Pearson': -0.12772738373583722} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 2628, 'MAPE': 1.2437, 'RMSE': 92.49791925171509, 'MAE': 47.36441630637928, 'SMAPE': 1.0217, 'ErrorMean': -0.034334353739705746, 'ErrorStdDev': 92.49791287942101, 'R2': -20.510046369935996, 'Pearson': 0.17743095610280793} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 657, 'MAPE': 1.1041, 'RMSE': 162.908976216052, 'MAE': 91.46422736787422, 'SMAPE': 1.0114, 'ErrorMean': -1.309460463801916, 'ErrorStdDev': 162.90371341702416, 'R2': -443.1180303424707, 'Pearson': -0.025260358263321934} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.9738, 'RMSE': 46.04955549558528, 'MAE': 41.12472495365293, 'SMAPE': 1.9395, 'ErrorMean': -40.95185046951648, 'ErrorStdDev': 21.059617861284938, 'R2': -4.3312408696767415, 'Pearson': 0.0729223149927666} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.9698, 'RMSE': 82.35310798965382, 'MAE': 80.8208467903004, 'SMAPE': 1.9378, 'ErrorMean': -80.67142769777071, 'ErrorStdDev': 16.557631133677848, 'R2': -112.4928139914079, 'Pearson': 0.01336295550533674} 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StdDev=185.43607844052733 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_W_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [LinearTrend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_W_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_LinearTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0495 MAPE_Forecast=0.0142 MAPE_Test=0.0176 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0245 SMAPE_Forecast=0.0143 SMAPE_Test=0.0176 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1327 MASE_Forecast=0.257 MASE_Test=0.3593 -INFO:pyaf.std:MODEL_L1 L1_Fit=4.253690344081162 L1_Forecast=8.33443249542162 L1_Test=11.750818189231296 -INFO:pyaf.std:MODEL_L2 L2_Fit=5.756919745613754 L2_Forecast=8.893385028377653 L2_Test=16.642863200847113 -INFO:pyaf.std:MODEL_COMPLEXITY 24 +INFO:pyaf.std:BEST_DECOMPOSITION 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_Signal_M_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag12 0.475162457077253 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.43707787702718565 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.2479040029978476 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag7 0.24725965032977787 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.2391853915085793 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag20 0.22450680632229486 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag15 0.14827461924959437 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.1393545210944173 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.1384975030632493 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.13021893244270838 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag12 0.475162457077252 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.4370778770271848 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.2479040029978487 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag7 0.24725965032977837 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.23918539150857476 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag20 0.2245068063222918 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag15 0.14827461924959295 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.139354521094421 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.13849750306324848 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.13021893244270827 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_M')] +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_M']' RangeIndex: 3650 entries, 0 to 3649 Data columns (total 2 columns): @@ -158,62 +173,56 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 10.368027210235596 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 13.03253960609436 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 22.55849862098694 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M']' 41.31870365142822 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 23.04889154434204 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 41.49259328842163 Int64Index: 4015 entries, 0 to 4014 -Data columns (total 31 columns): +Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_D_start 4015 non-null datetime64[ns] - 1 Signal_D 3650 non-null float64 - 2 Signal_D_Forecast 4015 non-null float64 - 3 Signal_D_Forecast_Lower_Bound 365 non-null float64 - 4 Signal_D_Forecast_Upper_Bound 365 non-null float64 - 5 Date 4015 non-null datetime64[ns] - 6 TH_W_start 574 non-null datetime64[ns] - 7 Signal_W 3651 non-null float64 - 8 Signal_W_Forecast 4015 non-null float64 + 0 TH_M_start 133 non-null datetime64[ns] + 1 TH_W_start 574 non-null datetime64[ns] + 2 TH_D_start 4015 non-null datetime64[ns] + 3 Signal_D 3650 non-null float64 + 4 Signal_D_Forecast 4015 non-null float64 + 5 Signal_D_Forecast_Lower_Bound 365 non-null float64 + 6 Signal_D_Forecast_Upper_Bound 365 non-null float64 + 7 Signal_W 522 non-null float64 + 8 Signal_W_Forecast 574 non-null float64 9 Signal_W_Forecast_Lower_Bound 52 non-null float64 10 Signal_W_Forecast_Upper_Bound 52 non-null float64 - 11 TH_M_start 133 non-null datetime64[ns] - 12 Signal_M 3651 non-null float64 - 13 Signal_M_Forecast 4015 non-null float64 - 14 Signal_M_Forecast_Lower_Bound 13 non-null float64 - 15 Signal_M_Forecast_Upper_Bound 13 non-null float64 - 16 Signal_D_BU_Forecast 4015 non-null float64 - 17 Signal_W_BU_Forecast 4015 non-null float64 - 18 Signal_M_BU_Forecast 4015 non-null float64 - 19 Signal_M_AHP_TD_Forecast 4015 non-null float64 - 20 Signal_W_AHP_TD_Forecast 4015 non-null float64 - 21 Signal_D_AHP_TD_Forecast 4015 non-null float64 - 22 Signal_M_PHA_TD_Forecast 4015 non-null float64 - 23 Signal_W_PHA_TD_Forecast 4015 non-null float64 - 24 Signal_D_PHA_TD_Forecast 4015 non-null float64 - 25 Signal_W_MO_Forecast 4015 non-null float64 - 26 Signal_D_MO_Forecast 4015 non-null float64 - 27 Signal_M_MO_Forecast 4015 non-null float64 - 28 Signal_D_OC_Forecast 4015 non-null float64 - 29 Signal_W_OC_Forecast 4015 non-null float64 - 30 Signal_M_OC_Forecast 4015 non-null float64 -dtypes: datetime64[ns](4), float64(27) + 11 Signal_M 3651 non-null float64 + 12 Signal_M_Forecast 4015 non-null float64 + 13 Signal_M_Forecast_Lower_Bound 13 non-null float64 + 14 Signal_M_Forecast_Upper_Bound 13 non-null float64 + 15 Signal_D_BU_Forecast 4015 non-null float64 + 16 Signal_W_BU_Forecast 4015 non-null float64 + 17 Signal_M_BU_Forecast 574 non-null float64 + 18 Signal_M_AHP_TD_Forecast 4015 non-null float64 + 19 Signal_W_AHP_TD_Forecast 4015 non-null float64 + 20 Signal_D_AHP_TD_Forecast 4015 non-null float64 + 21 Signal_M_PHA_TD_Forecast 4015 non-null float64 + 22 Signal_W_PHA_TD_Forecast 4015 non-null float64 + 23 Signal_D_PHA_TD_Forecast 4015 non-null float64 + 24 Signal_W_MO_Forecast 574 non-null float64 + 25 Signal_D_MO_Forecast 574 non-null float64 + 26 Signal_M_MO_Forecast 574 non-null float64 + 27 Signal_D_OC_Forecast 574 non-null float64 + 28 Signal_W_OC_Forecast 574 non-null float64 + 29 Signal_M_OC_Forecast 574 non-null float64 +dtypes: datetime64[ns](3), float64(27) memory usage: 1.1 MB - TH_D_start Signal_D ... Signal_W_OC_Forecast Signal_M_OC_Forecast -4010 2012-01-18 NaN ... 37.110516 37.110516 -4011 2012-01-19 NaN ... 1384.592503 1384.592503 -4012 2012-01-20 NaN ... 31.770307 31.770307 -4013 2012-01-21 NaN ... 31.941882 31.941882 -4014 2012-01-22 NaN ... 32.276643 32.276643 + TH_M_start TH_W_start ... Signal_W_OC_Forecast Signal_M_OC_Forecast +4010 NaT NaT ... NaN NaN +4011 2012-01-19 2012-01-19 ... 1348.704416 1348.704416 +4012 NaT NaT ... NaN NaN +4013 NaT NaT ... NaN NaN +4014 NaT NaT ... NaN NaN -[5 rows x 31 columns] +[5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M_Q.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M_Q.log index db5f4e91f..3b59c62c8 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M_Q.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M_Q.log @@ -1,31 +1,18 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3449544906616211 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.454874038696289 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, 'M': 2419200.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-01 00:00:00'), 2: Timestamp('2001-02-08 00:00:00'), 3: Timestamp('2001-02-15 00:00:00'), 4: Timestamp('2001-02-22 00:00:00')}, 'Signal': {0: 5.185958905669871, 1: 52.68214704590134, 2: 97.81781280163595, 3: 61.48599412944952, 4: 47.28876337670961}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA M {'TH_M_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-22 00:00:00'), 2: Timestamp('2001-03-25 00:00:00'), 3: Timestamp('2001-04-24 00:00:00'), 4: Timestamp('2001-05-25 00:00:00')}, 'Signal': {0: 23.123051573831955, 1: 276.6131975356409, 2: 366.8233603130296, 3: 318.36900358848874, 4: 420.71153575897733}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA Q {'TH_Q_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-04-26 00:00:00'), 2: Timestamp('2001-07-27 00:00:00'), 3: Timestamp('2001-10-27 00:00:00'), 4: Timestamp('2002-01-25 00:00:00')}, 'Signal': {0: 666.5596094225025, 1: 1077.4457537430067, 2: 1362.2354461702034, 3: 1551.8657635147804, 4: 1719.6917811853032}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, 'M': 13, 'Q': 4} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_M'), (3, 'Signal_Q')] +INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' -INFO:pyaf.std:START_TRAINING 'Signal_W' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:START_TRAINING 'Signal_Q' -INFO:pyaf.std:START_TRAINING 'Signal_M' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_Q' 5.030999183654785 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_M' 6.745294570922852 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_W' 12.673025131225586 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 36.447611570358276 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' 74.0800268650055 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_M'), (3, 'Signal_Q')] -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 8.404690980911255 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 10.382452964782715 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 14.946375608444214 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 22.704317569732666 +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' 48.47303485870361 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -34,13 +21,12 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_D_Forecast', - 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', - 'Date', 'TH_W_start', 'Signal_W', 'Signal_W_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'TH_D_start', 'TH_W_start', 'TH_Q_start', 'Signal_D', + 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', + 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', - 'TH_M_start', 'Signal_M', 'Signal_M_Forecast', - 'Signal_M_Forecast_Lower_Bound', 'Signal_M_Forecast_Upper_Bound', - 'TH_Q_start', 'Signal_Q', 'Signal_Q_Forecast', + 'Signal_M', 'Signal_M_Forecast', 'Signal_M_Forecast_Lower_Bound', + 'Signal_M_Forecast_Upper_Bound', 'Signal_Q', 'Signal_Q_Forecast', 'Signal_Q_Forecast_Lower_Bound', 'Signal_Q_Forecast_Upper_Bound', 'Signal_D_BU_Forecast', 'Signal_W_BU_Forecast', 'Signal_M_BU_Forecast', 'Signal_Q_BU_Forecast', 'Signal_Q_AHP_TD_Forecast', @@ -53,54 +39,60 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_ 'Signal_Q_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_D']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_M']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (3, ['Signal_Q']) -INFO:pyaf.hierarchical:MODEL_LEVEL (3, ['Signal_Q']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.9927, 'RMSE': 46.581850420626445, 'MAE': 42.012238486646126, 'SMAPE': 1.9813, 'ErrorMean': -41.99827911140823, 'ErrorStdDev': 20.149772710625495, 'R2': -4.4552027060021695, 'Pearson': 0.037385506881438074} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.9916, 'RMSE': 83.29956747601588, 'MAE': 82.6636613016702, 'SMAPE': 1.9812, 'ErrorMean': -82.6636613016702, 'ErrorStdDev': 10.273122305028622, 'R2': -115.11648187639496, 'Pearson': -0.023439686321373707} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 2628, 'MAPE': 0.0667, 'RMSE': 1.359004241502849, 'MAE': 1.0288483172385678, 'SMAPE': 0.043, 'ErrorMean': 1.1274593638202503e-15, 'ErrorStdDev': 1.359004241502849, 'R2': 0.9953567823217537, 'Pearson': 0.9976756899523935} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 657, 'MAPE': 0.0119, 'RMSE': 1.2140180766446607, 'MAE': 0.9828321842412838, 'SMAPE': 0.0119, 'ErrorMean': -0.099861635734604, 'ErrorStdDev': 1.209903940041692, 'R2': 0.9753362742238273, 'Pearson': 0.987676902148217} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 2628, 'MAPE': 0.9738, 'RMSE': 46.04955549558528, 'MAE': 41.12472495365293, 'SMAPE': 1.9395, 'ErrorMean': -40.95185046951648, 'ErrorStdDev': 21.059617861284938, 'R2': -4.3312408696767415, 'Pearson': 0.0729223149927666} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 657, 'MAPE': 0.9698, 'RMSE': 82.35310798965382, 'MAE': 80.8208467903004, 'SMAPE': 1.9378, 'ErrorMean': -80.67142769777071, 'ErrorStdDev': 16.557631133677848, 'R2': -112.4928139914079, 'Pearson': 0.01336295550533674} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 2628, 'MAPE': 1.3937, 'RMSE': 135.2452303132069, 'MAE': 52.821999714723994, 'SMAPE': 1.1592, 'ErrorMean': -0.06140560509154885, 'ErrorStdDev': 135.24521637316434, 'R2': -44.985544745178565, 'Pearson': 0.12196534180596942} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 657, 'MAPE': 1.2292, 'RMSE': 234.83534328580853, 'MAE': 101.6349749914196, 'SMAPE': 1.147, 'ErrorMean': -1.589347592068692, 'ErrorStdDev': 234.8299649329172, 'R2': -921.858881027255, 'Pearson': -0.03265250765073472} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.9926, 'RMSE': 46.5700525634989, 'MAE': 41.96217303455629, 'SMAPE': 1.9801, 'ErrorMean': -41.88699343129133, 'ErrorStdDev': 20.35312204685088, 'R2': -4.45243976072174, 'Pearson': 0.03738550688143808} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.9901, 'RMSE': 83.28087724448281, 'MAE': 82.5381706505936, 'SMAPE': 1.9794, 'ErrorMean': -82.45033369183778, 'ErrorStdDev': 11.73230534529391, 'R2': -115.06438075452827, 'Pearson': -0.023439686321373696} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 99330828774.8022, 'RMSE': 274.0347070922368, 'MAE': 50.07544608239394, 'SMAPE': 0.0801, 'ErrorMean': -30.023511150912555, 'ErrorStdDev': 272.38503899681973, 'R2': -0.1815277016521586, 'Pearson': 0.08628594816096907} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_AHP_TD_Forecast', 'Length': 657, 'MAPE': 200022972902.1915, 'RMSE': 489.7923607881746, 'MAE': 97.48680985293397, 'SMAPE': 0.0793, 'ErrorMean': -57.482215272501804, 'ErrorStdDev': 486.4075982278845, 'R2': -0.20690786667487715, 'Pearson': 0.06490598457617774} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_BU_Forecast', 'Length': 2628, 'MAPE': 408713939061.3123, 'RMSE': 275.17010874566245, 'MAE': 81.60754187934756, 'SMAPE': 0.3376, 'ErrorMean': 0.16464557717257092, 'ErrorStdDev': 275.1700594885527, 'R2': -0.19133877734286653, 'Pearson': 0.018000095382586415} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_BU_Forecast', 'Length': 657, 'MAPE': 796747874075.08, 'RMSE': 489.36352226423304, 'MAE': 157.8702445959428, 'SMAPE': 0.3375, 'ErrorMean': 1.479330219067034, 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'SMAPE': 0.3318, 'ErrorMean': -32.57160666945013, 'ErrorStdDev': 128.6036607972233, 'R2': -0.31767444455122695, 'Pearson': 0.01820686660478614} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 657, 'MAPE': 160648649742.968, 'RMSE': 238.33424702048342, 'MAE': 96.94848334474199, 'SMAPE': 0.3319, 'ErrorMean': -64.81875339617612, 'ErrorStdDev': 229.35069764660955, 'R2': -0.35242041157324744, 'Pearson': 0.0008208944616908937} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 2628, 'MAPE': 263649627676.6839, 'RMSE': 154.50901739448153, 'MAE': 57.77209966368186, 'SMAPE': 1.8699, 'ErrorMean': -0.08708193822001178, 'ErrorStdDev': 154.50899285460451, 'R2': -0.7873424786886374, 'Pearson': 0.2553444767437738} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 657, 'MAPE': 554496045275.553, 'RMSE': 282.3283528861007, 'MAE': 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Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 14, 'MAPE': 1.0902, 'RMSE': 40.03906232364583, 'MAE': 33.33379098774334, 'SMAPE': 1.814, 'ErrorMean': -32.49105089857087, 'ErrorStdDev': 23.397823045387618, 'R2': -2.1475188587942764, 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200.85526425644963, 'MAE': 188.03390525442592, 'SMAPE': 0.1725, 'ErrorMean': 188.03390525442592, 'ErrorStdDev': 70.61223445195446, 'R2': 0.9938862899242045, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 2, 'MAPE': 0.6803, 'RMSE': 2745.0823978952494, 'MAE': 2159.3074989928186, 'SMAPE': 1.0316, 'ErrorMean': -2159.3074989928186, 'ErrorStdDev': 1694.9538330077635, 'R2': -0.14195547837726274, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 2, 'MAPE': 0.6803, 'RMSE': 2745.0823978952494, 'MAE': 2159.3074989928186, 'SMAPE': 1.0316, 'ErrorMean': -2159.3074989928186, 'ErrorStdDev': 1694.9538330077635, 'R2': -0.14195547837726274, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 2, 'MAPE': 0.6136, 'RMSE': 2641.515672539951, 'MAE': 2053.2071283660816, 'SMAPE': 0.8861, 'ErrorMean': -2053.2071283660816, 'ErrorStdDev': 1661.910207051181, 'R2': -0.057413342019545954, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 2, 'MAPE': 0.2041, 'RMSE': 200.85526425644963, 'MAE': 188.03390525442592, 'SMAPE': 0.1725, 'ErrorMean': 188.03390525442592, 'ErrorStdDev': 70.61223445195446, 'R2': 0.9938862899242045, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 14, 'MAPE': 1.6232, 'RMSE': 296.65803925186356, 'MAE': 254.54983453601494, 'SMAPE': 1.8418, 'ErrorMean': -246.67562823178324, 'ErrorStdDev': 164.79419494999001, 'R2': -2.806003062479135, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 3, 'MAPE': 1.0, 'RMSE': 596.488998895877, 'MAE': 594.2451435619568, 'SMAPE': 2.0, 'ErrorMean': -594.2451435619568, 'ErrorStdDev': 51.68979741530114, 'R2': -132.1665708842945, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 14, 'MAPE': 0.8545, 'RMSE': 274.2750369601739, 'MAE': 241.92123006486722, 'SMAPE': 1.5093, 'ErrorMean': -241.92123006486722, 'ErrorStdDev': 129.23201748563065, 'R2': -2.2533400083904853, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 3, 'MAPE': 0.8629, 'RMSE': 513.943321155761, 'MAE': 512.3625770859363, 'SMAPE': 1.5178, 'ErrorMean': -512.3625770859363, 'ErrorStdDev': 40.27812014570216, 'R2': -97.86004257594809, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 14, 'MAPE': 0.6074, 'RMSE': 18.00695225840996, 'MAE': 13.326464599087375, 'SMAPE': 0.146, 'ErrorMean': 4.640690078869787, 'ErrorStdDev': 17.39868745706224, 'R2': 0.9859771168301523, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 3, 'MAPE': 0.0255, 'RMSE': 18.127529364990764, 'MAE': 15.782125366241493, 'SMAPE': 0.0259, 'ErrorMean': -15.782125366241493, 'ErrorStdDev': 8.91806255880947, 'R2': 0.8770106306753312, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 14, 'MAPE': 4.3075, 'RMSE': 462.2417457971763, 'MAE': 226.4773740579755, 'SMAPE': 0.494, 'ErrorMean': 226.4773740579755, 'ErrorStdDev': 402.9583484647329, 'R2': -8.240494630464802, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 3, 'MAPE': 0.3315, 'RMSE': 196.6528511173861, 'MAE': 196.3725097523904, 'SMAPE': 0.2843, 'ErrorMean': 196.3725097523904, 'ErrorStdDev': 10.49672644895297, 'R2': -13.474075525830203, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 14, 'MAPE': 1.7776, 'RMSE': 296.73424571568427, 'MAE': 254.62599329310325, 'SMAPE': 1.8429, 'ErrorMean': -240.6856939116558, 'ErrorStdDev': 173.5557815998097, 'R2': -2.8079587100582497, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 3, 'MAPE': 1.0, 'RMSE': 596.488998895877, 'MAE': 594.2451435619568, 'SMAPE': 2.0, 'ErrorMean': -594.2451435619568, 'ErrorStdDev': 51.68979741530114, 'R2': -132.1665708842945, 'Pearson': 0.0} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 124.16288590431213 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-05T00:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_D' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_D' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -112,35 +104,53 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_D_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0667 MAPE_Forecast=0.0119 MAPE_Test=0.0107 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.043 SMAPE_Forecast=0.0119 SMAPE_Test=0.0107 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4456 MASE_Forecast=0.4238 MASE_Test=0.4377 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.0288483172385678 L1_Forecast=0.9828321842412838 L1_Test=1.023698313669492 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.359004241502849 L2_Forecast=1.2140180766446607 L2_Test=1.2923917049609932 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.0288483172385674 L1_Forecast=0.9828321842412836 L1_Test=1.0236983136694886 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.359004241502849 L2_Forecast=1.2140180766446595 L2_Test=1.2923917049609883 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 42.354333411171915 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_D_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag21 0.3938827990494035 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag42 0.33146029445342495 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.25093217429860903 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag63 0.24260881398354753 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15543183068103217 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.11348082264365836 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.10107189648913975 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag3 0.09930723701673971 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.0783585462253969 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.07656432642980247 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag21 0.39388279904940376 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag42 0.33146029445342484 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.25093217429861164 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag63 0.24260881398354692 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15543183068103128 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.11348082264365861 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.10107189648913845 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag3 0.09930723701673916 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.07835854622539506 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.07656432642980306 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_W_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-03T00:00:00.000000 TimeDelta= Horizon=52 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_W' Length=522 Min=5.185958905669871 Max=727.5038824023378 Mean=385.34514038141367 StdDev=185.43607844052733 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_W' Min=5.185958905669871 Max=727.5038824023378 Mean=385.34514038141367 StdDev=185.43607844052733 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_W_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [LinearTrend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_W_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_LinearTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0495 MAPE_Forecast=0.0142 MAPE_Test=0.0176 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0245 SMAPE_Forecast=0.0143 SMAPE_Test=0.0176 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1327 MASE_Forecast=0.257 MASE_Test=0.3593 -INFO:pyaf.std:MODEL_L1 L1_Fit=4.253690344081162 L1_Forecast=8.33443249542162 L1_Test=11.750818189231296 -INFO:pyaf.std:MODEL_L2 L2_Fit=5.756919745613754 L2_Forecast=8.893385028377653 L2_Test=16.642863200847113 -INFO:pyaf.std:MODEL_COMPLEXITY 24 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [Lag1Trend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_W_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0359 MAPE_Forecast=0.0059 MAPE_Test=0.0083 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0261 SMAPE_Forecast=0.0059 SMAPE_Test=0.0081 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1125 MASE_Forecast=0.1058 MASE_Test=0.1667 +INFO:pyaf.std:MODEL_L1 L1_Fit=3.6072026732867726 L1_Forecast=3.4311219086102214 L1_Test=5.451214365608601 +INFO:pyaf.std:MODEL_L2 L2_Fit=5.616083488396741 L2_Forecast=4.199829627924871 L2_Test=14.817188328676421 +INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 5.185958905669871 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_W_Lag1Trend_residue_bestCycle_byMAPE 12 -12.931251865057625 {0: -33.870587229317834, 1: -12.858763848798418, 2: 49.74977134955286, 3: -32.28703382500029, 4: -12.408777313072562, 5: 49.99369185479328, 6: -34.80354276657749, 7: -12.94358249459772, 8: 48.89042849147563, 9: -33.88141364580514, 10: -14.104361860924314, 11: 49.20838455450192} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_M_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-01-25T00:00:00.000000 TimeDelta= Horizon=13 @@ 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+INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1252.8364833653602 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_M_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag12 0.475162457077253 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.43707787702718565 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.2479040029978476 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag7 0.24725965032977787 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.2391853915085793 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag20 0.22450680632229486 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag15 0.14827461924959437 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.1393545210944173 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.1384975030632493 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.13021893244270838 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag12 0.475162457077252 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.4370778770271848 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.2479040029978487 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag7 0.24725965032977837 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.23918539150857476 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag20 0.2245068063222918 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag15 0.14827461924959295 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.139354521094421 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag11 -0.13849750306324848 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.13021893244270827 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_Q_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2007-10-27T00:00:00.000000 TimeDelta= Horizon=4 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_Q' Length=40 Min=666.5596094225025 Max=9129.148140134008 Mean=4973.439747231688 StdDev=2417.7870366430593 @@ -180,20 +199,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_Q_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0322 MAPE_Forecast=0.0121 MAPE_Test=0.0087 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0304 SMAPE_Forecast=0.012 SMAPE_Test=0.0087 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2752 MASE_Forecast=0.4113 MASE_Test=0.2701 -INFO:pyaf.std:MODEL_L1 L1_Fit=60.060593754145756 L1_Forecast=90.3256350362592 L1_Test=76.07178415772069 -INFO:pyaf.std:MODEL_L2 L2_Fit=82.3175924405223 L2_Forecast=108.02649757719166 L2_Test=90.65303156034186 +INFO:pyaf.std:MODEL_L1 L1_Fit=60.06059375414562 L1_Forecast=90.32563503625931 L1_Test=76.07178415771978 +INFO:pyaf.std:MODEL_L2 L2_Fit=82.31759244052236 L2_Forecast=108.02649757719168 L2_Test=90.65303156034095 INFO:pyaf.std:MODEL_COMPLEXITY 7 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3720.654312352265 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_Q_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8636016521663791 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag3 0.2743247507645108 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.21383199534542124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag6 0.1536123953333636 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.09731926497993784 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag4 0.08513831131586441 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06579488508226555 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8636016521663812 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag3 0.27432475076451124 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.2138319953454227 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag6 0.15361239533336257 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.09731926497994144 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag4 0.08513831131586579 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06579488508226333 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_M'), (3, 'Signal_Q')] +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' RangeIndex: 3650 entries, 0 to 3649 Data columns (total 2 columns): @@ -203,74 +231,66 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 9.020958662033081 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 10.288617849349976 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 13.261616468429565 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 23.23712968826294 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' 62.65265965461731 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 23.604214906692505 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 62.88531303405762 Int64Index: 4015 entries, 0 to 4014 -Data columns (total 41 columns): +Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_D_start 4015 non-null datetime64[ns] - 1 Signal_D 3650 non-null float64 - 2 Signal_D_Forecast 4015 non-null float64 - 3 Signal_D_Forecast_Lower_Bound 365 non-null float64 - 4 Signal_D_Forecast_Upper_Bound 365 non-null float64 - 5 Date 4015 non-null datetime64[ns] - 6 TH_W_start 574 non-null datetime64[ns] - 7 Signal_W 3651 non-null float64 - 8 Signal_W_Forecast 4015 non-null float64 - 9 Signal_W_Forecast_Lower_Bound 52 non-null float64 - 10 Signal_W_Forecast_Upper_Bound 52 non-null float64 - 11 TH_M_start 133 non-null datetime64[ns] - 12 Signal_M 3651 non-null float64 - 13 Signal_M_Forecast 4015 non-null float64 + 0 TH_M_start 133 non-null datetime64[ns] + 1 TH_D_start 4015 non-null datetime64[ns] + 2 TH_W_start 574 non-null datetime64[ns] + 3 TH_Q_start 44 non-null datetime64[ns] + 4 Signal_D 3650 non-null float64 + 5 Signal_D_Forecast 4015 non-null float64 + 6 Signal_D_Forecast_Lower_Bound 365 non-null float64 + 7 Signal_D_Forecast_Upper_Bound 365 non-null float64 + 8 Signal_W 522 non-null float64 + 9 Signal_W_Forecast 574 non-null float64 + 10 Signal_W_Forecast_Lower_Bound 52 non-null float64 + 11 Signal_W_Forecast_Upper_Bound 52 non-null float64 + 12 Signal_M 120 non-null float64 + 13 Signal_M_Forecast 133 non-null float64 14 Signal_M_Forecast_Lower_Bound 13 non-null float64 15 Signal_M_Forecast_Upper_Bound 13 non-null float64 - 16 TH_Q_start 44 non-null datetime64[ns] - 17 Signal_Q 3651 non-null float64 - 18 Signal_Q_Forecast 4015 non-null float64 - 19 Signal_Q_Forecast_Lower_Bound 4 non-null float64 - 20 Signal_Q_Forecast_Upper_Bound 4 non-null float64 - 21 Signal_D_BU_Forecast 4015 non-null float64 - 22 Signal_W_BU_Forecast 4015 non-null float64 - 23 Signal_M_BU_Forecast 4015 non-null float64 - 24 Signal_Q_BU_Forecast 4015 non-null float64 - 25 Signal_Q_AHP_TD_Forecast 4015 non-null float64 - 26 Signal_M_AHP_TD_Forecast 4015 non-null float64 - 27 Signal_W_AHP_TD_Forecast 4015 non-null float64 - 28 Signal_D_AHP_TD_Forecast 4015 non-null float64 - 29 Signal_Q_PHA_TD_Forecast 4015 non-null float64 - 30 Signal_M_PHA_TD_Forecast 4015 non-null float64 - 31 Signal_W_PHA_TD_Forecast 4015 non-null float64 - 32 Signal_D_PHA_TD_Forecast 4015 non-null float64 - 33 Signal_M_MO_Forecast 4015 non-null float64 - 34 Signal_W_MO_Forecast 4015 non-null float64 - 35 Signal_D_MO_Forecast 4015 non-null float64 - 36 Signal_Q_MO_Forecast 4015 non-null float64 - 37 Signal_D_OC_Forecast 4015 non-null float64 - 38 Signal_W_OC_Forecast 4015 non-null float64 - 39 Signal_M_OC_Forecast 4015 non-null float64 - 40 Signal_Q_OC_Forecast 4015 non-null float64 -dtypes: datetime64[ns](5), float64(36) + 16 Signal_Q 3651 non-null float64 + 17 Signal_Q_Forecast 4015 non-null float64 + 18 Signal_Q_Forecast_Lower_Bound 4 non-null float64 + 19 Signal_Q_Forecast_Upper_Bound 4 non-null float64 + 20 Signal_D_BU_Forecast 4015 non-null float64 + 21 Signal_W_BU_Forecast 4015 non-null float64 + 22 Signal_M_BU_Forecast 574 non-null float64 + 23 Signal_Q_BU_Forecast 133 non-null float64 + 24 Signal_Q_AHP_TD_Forecast 4015 non-null float64 + 25 Signal_M_AHP_TD_Forecast 4015 non-null float64 + 26 Signal_W_AHP_TD_Forecast 4015 non-null float64 + 27 Signal_D_AHP_TD_Forecast 4015 non-null float64 + 28 Signal_Q_PHA_TD_Forecast 4015 non-null float64 + 29 Signal_M_PHA_TD_Forecast 4015 non-null float64 + 30 Signal_W_PHA_TD_Forecast 4015 non-null float64 + 31 Signal_D_PHA_TD_Forecast 4015 non-null float64 + 32 Signal_M_MO_Forecast 133 non-null float64 + 33 Signal_W_MO_Forecast 133 non-null float64 + 34 Signal_D_MO_Forecast 133 non-null float64 + 35 Signal_Q_MO_Forecast 133 non-null float64 + 36 Signal_D_OC_Forecast 21 non-null float64 + 37 Signal_W_OC_Forecast 21 non-null float64 + 38 Signal_M_OC_Forecast 21 non-null float64 + 39 Signal_Q_OC_Forecast 21 non-null float64 +dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_D_start Signal_D ... Signal_M_OC_Forecast Signal_Q_OC_Forecast -4010 2012-01-18 NaN ... 27.832887 27.832887 -4011 2012-01-19 NaN ... 1038.444377 1038.444377 -4012 2012-01-20 NaN ... 23.827730 23.827730 -4013 2012-01-21 NaN ... 23.956411 23.956411 -4014 2012-01-22 NaN ... 24.207482 24.207482 + TH_M_start TH_D_start ... Signal_M_OC_Forecast Signal_Q_OC_Forecast +4010 NaT 2012-01-18 ... NaN NaN +4011 2012-01-19 2012-01-19 ... 1011.528312 1011.528312 +4012 NaT 2012-01-20 ... NaN NaN +4013 NaT 2012-01-21 ... NaN NaN +4014 NaT 2012-01-22 ... NaN NaN -[5 rows x 41 columns] +[5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_Q.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_Q.log index 61b4b94f9..3d4a3d3cb 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_Q.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_Q.log @@ -1,26 +1,17 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.55926513671875 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.042081356048584 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-01 00:00:00'), 2: Timestamp('2001-02-08 00:00:00'), 3: Timestamp('2001-02-15 00:00:00'), 4: Timestamp('2001-02-22 00:00:00')}, 'Signal': {0: 5.185958905669871, 1: 52.68214704590134, 2: 97.81781280163595, 3: 61.48599412944952, 4: 47.28876337670961}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA Q {'TH_Q_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-04-26 00:00:00'), 2: Timestamp('2001-07-27 00:00:00'), 3: Timestamp('2001-10-27 00:00:00'), 4: Timestamp('2002-01-25 00:00:00')}, 'Signal': {0: 666.5596094225025, 1: 1077.4457537430067, 2: 1362.2354461702034, 3: 1551.8657635147804, 4: 1719.6917811853032}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, 'Q': 4} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_Q')] +INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_Q']' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:START_TRAINING 'Signal_W' -INFO:pyaf.std:START_TRAINING 'Signal_Q' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_Q' 6.568089246749878 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_W' 12.639132738113403 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 37.08045744895935 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 76.61522889137268 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_Q')] -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 7.511045217514038 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 8.499413967132568 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 21.30156898498535 +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_Q']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 48.65848731994629 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -29,13 +20,13 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_D_Forecast', - 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', - 'Date', 'TH_W_start', 'Signal_W', 'Signal_W_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'TH_Q_start', 'TH_D_start', 'Signal_D', + 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', + 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', - 'TH_Q_start', 'Signal_Q', 'Signal_Q_Forecast', - 'Signal_Q_Forecast_Lower_Bound', 'Signal_Q_Forecast_Upper_Bound', - 'Signal_D_BU_Forecast', 'Signal_W_BU_Forecast', 'Signal_Q_BU_Forecast', + 'Signal_Q', 'Signal_Q_Forecast', 'Signal_Q_Forecast_Lower_Bound', + 'Signal_Q_Forecast_Upper_Bound', 'Signal_D_BU_Forecast', + 'Signal_W_BU_Forecast', 'Signal_Q_BU_Forecast', 'Signal_Q_AHP_TD_Forecast', 'Signal_W_AHP_TD_Forecast', 'Signal_D_AHP_TD_Forecast', 'Signal_Q_PHA_TD_Forecast', 'Signal_W_PHA_TD_Forecast', 'Signal_D_PHA_TD_Forecast', @@ -43,42 +34,49 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'Signal_D', 'Signal_ 'Signal_D_OC_Forecast', 'Signal_W_OC_Forecast', 'Signal_Q_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_D']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_Q']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_Q']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.9928, 'RMSE': 46.583764952054906, 'MAE': 42.018977664445686, 'SMAPE': 1.9815, 'ErrorMean': -42.0060529036014, 'ErrorStdDev': 20.137990877149086, 'R2': -4.455651136949602, 'Pearson': 0.03738550688143808} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.9917, 'RMSE': 83.30279481877753, 'MAE': 82.67856316960288, 'SMAPE': 1.9814, 'ErrorMean': -82.67856316960288, 'ErrorStdDev': 10.178939867654922, 'R2': -115.12547964092661, 'Pearson': -0.023439686321373686} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 2628, 'MAPE': 0.0667, 'RMSE': 1.359004241502849, 'MAE': 1.0288483172385678, 'SMAPE': 0.043, 'ErrorMean': 1.1274593638202503e-15, 'ErrorStdDev': 1.359004241502849, 'R2': 0.9953567823217537, 'Pearson': 0.9976756899523935} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 657, 'MAPE': 0.0119, 'RMSE': 1.2140180766446607, 'MAE': 0.9828321842412838, 'SMAPE': 0.0119, 'ErrorMean': -0.099861635734604, 'ErrorStdDev': 1.209903940041692, 'R2': 0.9753362742238273, 'Pearson': 0.987676902148217} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 2628, 'MAPE': 0.9011, 'RMSE': 43.793976817124474, 'MAE': 37.24734616379794, 'SMAPE': 1.7358, 'ErrorMean': -36.29450488659023, 'ErrorStdDev': 24.507576797719377, 'R2': -3.82176664278317, 'Pearson': 0.1023848127010454} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 657, 'MAPE': 0.8635, 'RMSE': 77.93534431038971, 'MAE': 72.32360615464947, 'SMAPE': 1.7202, 'ErrorMean': -71.57363564466736, 'ErrorStdDev': 30.840437308562855, 'R2': -100.642957466567, 'Pearson': -0.12772738373583722} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 2628, 'MAPE': 1.3132, 'RMSE': 152.89748402376605, 'MAE': 48.48349913765221, 'SMAPE': 1.0096, 'ErrorMean': -0.03898100867287557, 'ErrorStdDev': 152.897479054688, 'R2': -57.77303228838987, 'Pearson': 0.10427148253978075} 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0.9998137636220039} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.0001, 'RMSE': 11.914572720607884, 'MAE': 1.0852550651437607, 'SMAPE': 0.0001, 'ErrorMean': 0.06395954121396204, 'ErrorStdDev': 11.914401046286013, 'R2': 0.9997669222648048, 'Pearson': 0.9998837759323713} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 21055786502.5835, 'RMSE': 124.34649954443803, 'MAE': 43.940802241869854, 'SMAPE': 0.3001, 'ErrorMean': -39.68869913747901, 'ErrorStdDev': 117.84251825945334, 'R2': -0.1576232586395947, 'Pearson': 0.03972284251296412} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 657, 'MAPE': 51590722046.9258, 'RMSE': 226.8994958693864, 'MAE': 88.68407421986662, 'SMAPE': 0.3075, 'ErrorMean': -78.36592981051007, 'ErrorStdDev': 212.9369913160132, 'R2': -0.22576121275689243, 'Pearson': -0.04222821419904044} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 2628, 'MAPE': 367041329317.9014, 'RMSE': 115.11675494162623, 'MAE': 73.43394219668387, 'SMAPE': 1.934, 'ErrorMean': -0.025676333128462073, 'ErrorStdDev': 115.11675207812435, 'R2': 0.00785032796280194, 'Pearson': 0.10888231818003787} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 657, 'MAPE': 717212043217.6122, 'RMSE': 206.0167745544909, 'MAE': 143.72107742096168, 'SMAPE': 1.9307, 'ErrorMean': -0.2786687774638947, 'ErrorStdDev': 206.01658608361703, 'R2': -0.01051782748666552, 'Pearson': -0.12225344774301827} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 2628, 'MAPE': 0.0071, 'RMSE': 2.177566452719836, 'MAE': 0.6085949655154175, 'SMAPE': 0.0035, 'ErrorMean': 8.651966341066668e-17, 'ErrorStdDev': 2.177566452719836, 'R2': 0.9996449880677393, 'Pearson': 0.99983802455392} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 657, 'MAPE': 0.002, 'RMSE': 3.363940743428132, 'MAE': 1.1924454407452545, 'SMAPE': 0.0021, 'ErrorMean': -1.1924454407452545, 'ErrorStdDev': 3.1455001503960647, 'R2': 0.9997305764918528, 'Pearson': 0.9999843789350714} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 2628, 'MAPE': 232429887128.9266, 'RMSE': 163.12431674249856, 'MAE': 51.8170485045932, 'SMAPE': 1.8436, 'ErrorMean': -0.06465734180133773, 'ErrorStdDev': 163.12430392843106, 'R2': -0.99222106145332, 'Pearson': 0.2918171517123125} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 657, 'MAPE': 508794634061.2192, 'RMSE': 299.67213482798223, 'MAE': 103.11857025782844, 'SMAPE': 1.8441, 'ErrorMean': -1.3596434456024917, 'ErrorStdDev': 299.6690503906955, 'R2': -1.138117342813016, 'Pearson': 0.21062698475013486} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 25570622339.7917, 'RMSE': 125.44085602970705, 'MAE': 44.30724091491697, 'SMAPE': 0.2999, 'ErrorMean': -39.11162140279899, 'ErrorStdDev': 119.18762281675815, 'R2': -0.17808908956975977, 'Pearson': 0.03972284251296412} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 657, 'MAPE': 62652937212.2081, 'RMSE': 229.50641140850678, 'MAE': 89.79029573639485, 'SMAPE': 0.3075, 'ErrorMean': -77.25970829398184, 'ErrorStdDev': 216.1113841331354, 'R2': -0.25408928559465616, 'Pearson': -0.04222821419904043} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 59.23445677757263 +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 376, 'MAPE': 0.9953, 'RMSE': 43.61739434056328, 'MAE': 38.82508906089763, 'SMAPE': 1.9751, 'ErrorMean': -38.79130836674138, 'ErrorStdDev': 19.94270503859951, 'R2': -3.809039955319877, 'Pearson': 0.1086258118356814} 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'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 6, 'MAPE': 0.9445, 'RMSE': 4259.8515095614475, 'MAE': 3727.710051475919, 'SMAPE': 1.7672, 'ErrorMean': -3727.710051475919, 'ErrorStdDev': 2061.6771463151886, 'R2': -2.5434786065381148, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 6, 'MAPE': 0.6327, 'RMSE': 2950.712370559119, 'MAE': 2571.758575708614, 'SMAPE': 0.9282, 'ErrorMean': -2571.758575708614, 'ErrorStdDev': 1446.637937439709, 'R2': -0.7001790037324724, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 6, 'MAPE': 0.0992, 'RMSE': 144.3053863863621, 'MAE': 129.22131548984407, 'SMAPE': 0.0905, 'ErrorMean': -3.8653786535601284, 'ErrorStdDev': 144.25360788549403, 'R2': 0.9959336385665105, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 376, 'MAPE': 1.0139, 'RMSE': 322.3109150783251, 'MAE': 292.4015095712584, 'SMAPE': 1.975, 'ErrorMean': -292.11532453759486, 'ErrorStdDev': 136.20926234629894, 'R2': -4.717854602166412, 'Pearson': 0.09778886526916761} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 94, 'MAPE': 1.0, 'RMSE': 585.0828979545455, 'MAE': 583.7864502550929, 'SMAPE': 2.0, 'ErrorMean': -583.7864502550929, 'ErrorStdDev': 38.92785606024334, 'R2': -224.89875435276088, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 376, 'MAPE': 0.869, 'RMSE': 281.54009776271215, 'MAE': 256.7179222025888, 'SMAPE': 1.5388, 'ErrorMean': -256.7179222025888, 'ErrorStdDev': 115.58864593126381, 'R2': -3.36278283149079, 'Pearson': 0.9779116330152986} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 94, 'MAPE': 0.8621, 'RMSE': 504.32741761109924, 'MAE': 503.23315559768935, 'SMAPE': 1.5153, 'ErrorMean': -503.23315559768935, 'ErrorStdDev': 33.204446411164035, 'R2': -166.8433684134568, 'Pearson': 0.8210731909825577} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 376, 'MAPE': 0.0359, 'RMSE': 5.616083488396741, 'MAE': 3.6072026732867726, 'SMAPE': 0.0261, 'ErrorMean': -0.5507998231415778, 'ErrorStdDev': 5.589008257595413, 'R2': 0.9982639981166463, 'Pearson': 0.9991516950575161} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 94, 'MAPE': 0.0059, 'RMSE': 4.199829627924871, 'MAE': 3.4311219086102214, 'SMAPE': 0.0059, 'ErrorMean': -0.4594524671204664, 'ErrorStdDev': 4.174622418141847, 'R2': 0.9883602842550797, 'Pearson': 0.9942334089102576} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 376, 'MAPE': 0.8153, 'RMSE': 259.6549080593885, 'MAE': 199.75087969813913, 'SMAPE': 0.9173, 'ErrorMean': -163.08916864238967, 'ErrorStdDev': 202.04602037868423, 'R2': -2.710873530959564, 'Pearson': 0.34470726146365005} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 94, 'MAPE': 0.6209, 'RMSE': 363.2995151004631, 'MAE': 362.49301943996755, 'SMAPE': 0.9005, 'ErrorMean': -362.49301943996755, 'ErrorStdDev': 24.19397713330535, 'R2': -86.09809673662467, 'Pearson': 0.9914232675117872} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 376, 'MAPE': 1.0192, 'RMSE': 322.1917471430979, 'MAE': 291.8070999296352, 'SMAPE': 1.9737, 'ErrorMean': -291.2375015888688, 'ErrorStdDev': 137.79782144647865, 'R2': -4.713627261850848, 'Pearson': 0.09778886526916761} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 94, 'MAPE': 1.0, 'RMSE': 585.0828979545455, 'MAE': 583.7864502550929, 'SMAPE': 2.0, 'ErrorMean': -583.7864502550929, 'ErrorStdDev': 38.92785606024334, 'R2': -224.89875435276088, 'Pearson': 0.0} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 126.47476935386658 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-05T00:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_D' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_D' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -90,35 +88,53 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_D_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0667 MAPE_Forecast=0.0119 MAPE_Test=0.0107 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.043 SMAPE_Forecast=0.0119 SMAPE_Test=0.0107 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4456 MASE_Forecast=0.4238 MASE_Test=0.4377 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.0288483172385678 L1_Forecast=0.9828321842412838 L1_Test=1.023698313669492 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.359004241502849 L2_Forecast=1.2140180766446607 L2_Test=1.2923917049609932 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.0288483172385674 L1_Forecast=0.9828321842412836 L1_Test=1.0236983136694886 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.359004241502849 L2_Forecast=1.2140180766446595 L2_Test=1.2923917049609883 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 42.354333411171915 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_D_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag21 0.3938827990494035 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag42 0.33146029445342495 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.25093217429860903 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag63 0.24260881398354753 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15543183068103217 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.11348082264365836 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.10107189648913975 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag3 0.09930723701673971 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.0783585462253969 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.07656432642980247 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag21 0.39388279904940376 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag42 0.33146029445342484 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.25093217429861164 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag63 0.24260881398354692 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15543183068103128 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.11348082264365861 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.10107189648913845 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag3 0.09930723701673916 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.07835854622539506 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.07656432642980306 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_W_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-03T00:00:00.000000 TimeDelta= Horizon=52 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_W' Length=522 Min=5.185958905669871 Max=727.5038824023378 Mean=385.34514038141367 StdDev=185.43607844052733 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_W' Min=5.185958905669871 Max=727.5038824023378 Mean=385.34514038141367 StdDev=185.43607844052733 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_W_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [LinearTrend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_W_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_LinearTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0495 MAPE_Forecast=0.0142 MAPE_Test=0.0176 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0245 SMAPE_Forecast=0.0143 SMAPE_Test=0.0176 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1327 MASE_Forecast=0.257 MASE_Test=0.3593 -INFO:pyaf.std:MODEL_L1 L1_Fit=4.253690344081162 L1_Forecast=8.33443249542162 L1_Test=11.750818189231296 -INFO:pyaf.std:MODEL_L2 L2_Fit=5.756919745613754 L2_Forecast=8.893385028377653 L2_Test=16.642863200847113 -INFO:pyaf.std:MODEL_COMPLEXITY 24 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [Lag1Trend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_W_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0359 MAPE_Forecast=0.0059 MAPE_Test=0.0083 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0261 SMAPE_Forecast=0.0059 SMAPE_Test=0.0081 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1125 MASE_Forecast=0.1058 MASE_Test=0.1667 +INFO:pyaf.std:MODEL_L1 L1_Fit=3.6072026732867726 L1_Forecast=3.4311219086102214 L1_Test=5.451214365608601 +INFO:pyaf.std:MODEL_L2 L2_Fit=5.616083488396741 L2_Forecast=4.199829627924871 L2_Test=14.817188328676421 +INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 5.185958905669871 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_W_Lag1Trend_residue_bestCycle_byMAPE 12 -12.931251865057625 {0: -33.870587229317834, 1: -12.858763848798418, 2: 49.74977134955286, 3: -32.28703382500029, 4: -12.408777313072562, 5: 49.99369185479328, 6: -34.80354276657749, 7: -12.94358249459772, 8: 48.89042849147563, 9: -33.88141364580514, 10: -14.104361860924314, 11: 49.20838455450192} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_Q_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2007-10-27T00:00:00.000000 TimeDelta= Horizon=4 @@ -132,20 +148,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_Q_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0322 MAPE_Forecast=0.0121 MAPE_Test=0.0087 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0304 SMAPE_Forecast=0.012 SMAPE_Test=0.0087 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2752 MASE_Forecast=0.4113 MASE_Test=0.2701 -INFO:pyaf.std:MODEL_L1 L1_Fit=60.060593754145756 L1_Forecast=90.3256350362592 L1_Test=76.07178415772069 -INFO:pyaf.std:MODEL_L2 L2_Fit=82.3175924405223 L2_Forecast=108.02649757719166 L2_Test=90.65303156034186 +INFO:pyaf.std:MODEL_L1 L1_Fit=60.06059375414562 L1_Forecast=90.32563503625931 L1_Test=76.07178415771978 +INFO:pyaf.std:MODEL_L2 L2_Fit=82.31759244052236 L2_Forecast=108.02649757719168 L2_Test=90.65303156034095 INFO:pyaf.std:MODEL_COMPLEXITY 7 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3720.654312352265 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_Q_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8636016521663791 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag3 0.2743247507645108 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.21383199534542124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag6 0.1536123953333636 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.09731926497993784 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag4 0.08513831131586441 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06579488508226555 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag1 0.8636016521663812 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag3 0.27432475076451124 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.2138319953454227 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag6 0.15361239533336257 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.09731926497994144 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag4 0.08513831131586579 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_Q_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.06579488508226333 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_D'), (1, 'Signal_W'), (2, 'Signal_Q')] +INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_Q']' RangeIndex: 3650 entries, 0 to 3649 Data columns (total 2 columns): @@ -155,62 +180,56 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 10.879334449768066 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 10.860366106033325 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 27.230342864990234 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 40.49080967903137 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 27.957782745361328 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 40.701812505722046 Int64Index: 4015 entries, 0 to 4014 -Data columns (total 31 columns): +Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_D_start 4015 non-null datetime64[ns] - 1 Signal_D 3650 non-null float64 - 2 Signal_D_Forecast 4015 non-null float64 - 3 Signal_D_Forecast_Lower_Bound 365 non-null float64 - 4 Signal_D_Forecast_Upper_Bound 365 non-null float64 - 5 Date 4015 non-null datetime64[ns] - 6 TH_W_start 574 non-null datetime64[ns] - 7 Signal_W 3651 non-null float64 - 8 Signal_W_Forecast 4015 non-null float64 + 0 TH_W_start 574 non-null datetime64[ns] + 1 TH_Q_start 44 non-null datetime64[ns] + 2 TH_D_start 4015 non-null datetime64[ns] + 3 Signal_D 3650 non-null float64 + 4 Signal_D_Forecast 4015 non-null float64 + 5 Signal_D_Forecast_Lower_Bound 365 non-null float64 + 6 Signal_D_Forecast_Upper_Bound 365 non-null float64 + 7 Signal_W 522 non-null float64 + 8 Signal_W_Forecast 574 non-null float64 9 Signal_W_Forecast_Lower_Bound 52 non-null float64 10 Signal_W_Forecast_Upper_Bound 52 non-null float64 - 11 TH_Q_start 44 non-null datetime64[ns] - 12 Signal_Q 3651 non-null float64 - 13 Signal_Q_Forecast 4015 non-null float64 - 14 Signal_Q_Forecast_Lower_Bound 4 non-null float64 - 15 Signal_Q_Forecast_Upper_Bound 4 non-null float64 - 16 Signal_D_BU_Forecast 4015 non-null float64 - 17 Signal_W_BU_Forecast 4015 non-null float64 - 18 Signal_Q_BU_Forecast 4015 non-null float64 - 19 Signal_Q_AHP_TD_Forecast 4015 non-null float64 - 20 Signal_W_AHP_TD_Forecast 4015 non-null float64 - 21 Signal_D_AHP_TD_Forecast 4015 non-null float64 - 22 Signal_Q_PHA_TD_Forecast 4015 non-null float64 - 23 Signal_W_PHA_TD_Forecast 4015 non-null float64 - 24 Signal_D_PHA_TD_Forecast 4015 non-null float64 - 25 Signal_W_MO_Forecast 4015 non-null float64 - 26 Signal_D_MO_Forecast 4015 non-null float64 - 27 Signal_Q_MO_Forecast 4015 non-null float64 - 28 Signal_D_OC_Forecast 4015 non-null float64 - 29 Signal_W_OC_Forecast 4015 non-null float64 - 30 Signal_Q_OC_Forecast 4015 non-null float64 -dtypes: datetime64[ns](4), float64(27) + 11 Signal_Q 3651 non-null float64 + 12 Signal_Q_Forecast 4015 non-null float64 + 13 Signal_Q_Forecast_Lower_Bound 4 non-null float64 + 14 Signal_Q_Forecast_Upper_Bound 4 non-null float64 + 15 Signal_D_BU_Forecast 4015 non-null float64 + 16 Signal_W_BU_Forecast 4015 non-null float64 + 17 Signal_Q_BU_Forecast 574 non-null float64 + 18 Signal_Q_AHP_TD_Forecast 4015 non-null float64 + 19 Signal_W_AHP_TD_Forecast 4015 non-null float64 + 20 Signal_D_AHP_TD_Forecast 4015 non-null float64 + 21 Signal_Q_PHA_TD_Forecast 4015 non-null float64 + 22 Signal_W_PHA_TD_Forecast 4015 non-null float64 + 23 Signal_D_PHA_TD_Forecast 4015 non-null float64 + 24 Signal_W_MO_Forecast 574 non-null float64 + 25 Signal_D_MO_Forecast 574 non-null float64 + 26 Signal_Q_MO_Forecast 574 non-null float64 + 27 Signal_D_OC_Forecast 574 non-null float64 + 28 Signal_W_OC_Forecast 574 non-null float64 + 29 Signal_Q_OC_Forecast 574 non-null float64 +dtypes: datetime64[ns](3), float64(27) memory usage: 1.1 MB - TH_D_start Signal_D ... Signal_W_OC_Forecast Signal_Q_OC_Forecast -4010 2012-01-18 NaN ... 37.110516 37.110516 -4011 2012-01-19 NaN ... 280.785751 280.785751 -4012 2012-01-20 NaN ... 31.770307 31.770307 -4013 2012-01-21 NaN ... 31.941882 31.941882 -4014 2012-01-22 NaN ... 32.276643 32.276643 + TH_W_start TH_Q_start ... Signal_W_OC_Forecast Signal_Q_OC_Forecast +4010 NaT NaT ... NaN NaN +4011 2012-01-19 NaT ... 244.897663 244.897663 +4012 NaT NaT ... NaN NaN +4013 NaT NaT ... NaN NaN +4014 NaT NaT ... NaN NaN -[5 rows x 31 columns] +[5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D.log b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D.log index 5ede749b2..0eead17c1 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D.log @@ -1,31 +1,18 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.35790419578552246 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.2391796112060547 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'H': 3600.0, '6H': 21600.0, '12H': 43200.0, 'D': 86400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA H {'TH_H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 01:00:00'), 2: Timestamp('2001-01-25 02:00:00'), 3: Timestamp('2001-01-25 03:00:00'), 4: Timestamp('2001-01-25 04:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 6H {'TH_6H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 06:00:00'), 2: Timestamp('2001-01-25 12:00:00'), 3: Timestamp('2001-01-25 18:00:00'), 4: Timestamp('2001-01-26 00:00:00')}, 'Signal': {0: 16.082802248811667, 1: 51.83274105481723, 2: 87.77037544957828, 3: 58.42664714689759, 4: 29.433643202513927}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 12H {'TH_12H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 12:00:00'), 2: Timestamp('2001-01-26 00:00:00'), 3: Timestamp('2001-01-26 12:00:00'), 4: Timestamp('2001-01-27 00:00:00')}, 'Signal': {0: 67.9155433036289, 1: 146.19702259647588, 2: 99.27213122050448, 3: 127.36564488404751, 4: 155.37956250958317}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 214.11256590010478, 1: 226.63777610455196, 2: 264.8441387439829, 3: 279.33413226235155, 4: 294.2287615834373}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'H': 365, '6H': 60, '12H': 30, 'D': 15} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_H'), (1, 'Signal_6H'), (2, 'Signal_12H'), (3, 'Signal_D')] +INFO:pyaf.std:START_TRAINING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' -INFO:pyaf.std:START_TRAINING 'Signal_H' -INFO:pyaf.std:START_TRAINING 'Signal_6H' -INFO:pyaf.std:START_TRAINING 'Signal_12H' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 9.800216436386108 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_12H' 11.784242391586304 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_6H' 13.192845106124878 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_H' 37.286452770233154 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' 74.07368779182434 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_H'), (1, 'Signal_6H'), (2, 'Signal_12H'), (3, 'Signal_D')] -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:START_FORECASTING 'Signal_6H' -INFO:pyaf.std:START_FORECASTING 'Signal_12H' -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_12H' 11.910550355911255 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_6H' 12.687739849090576 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 17.75829243659973 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 26.443344593048096 +INFO:pyaf.std:START_FORECASTING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' 30.77304744720459 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -34,13 +21,12 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_H_start', 'Signal_H', 'Signal_H_Forecast', - 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', - 'Date', 'TH_6H_start', 'Signal_6H', 'Signal_6H_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_12H_start', 'TH_6H_start', 'TH_D_start', 'TH_H_start', 'Signal_H', + 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', + 'Signal_H_Forecast_Upper_Bound', 'Signal_6H', 'Signal_6H_Forecast', 'Signal_6H_Forecast_Lower_Bound', 'Signal_6H_Forecast_Upper_Bound', - 'TH_12H_start', 'Signal_12H', 'Signal_12H_Forecast', - 'Signal_12H_Forecast_Lower_Bound', 'Signal_12H_Forecast_Upper_Bound', - 'TH_D_start', 'Signal_D', 'Signal_D_Forecast', + 'Signal_12H', 'Signal_12H_Forecast', 'Signal_12H_Forecast_Lower_Bound', + 'Signal_12H_Forecast_Upper_Bound', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', 'Signal_H_BU_Forecast', 'Signal_6H_BU_Forecast', 'Signal_12H_BU_Forecast', 'Signal_D_BU_Forecast', @@ -54,110 +40,123 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_H_start', 'Signal_H', 'Signal_ 'Signal_D_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_H']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_6H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_6H']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_12H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_12H']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (3, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (3, ['Signal_D']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.0449, 'RMSE': 113.53001754308129, 'MAE': 22.245363713525876, 'SMAPE': 0.0862, 'ErrorMean': -21.092186310086294, 'ErrorStdDev': 111.5535053684686, 'R2': 0.46632690152768386, 'Pearson': 0.6966320174183644} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.0438, 'RMSE': 207.25932547618228, 'MAE': 43.833169891277876, 'SMAPE': 0.0864, 'ErrorMean': -42.702948154774774, 'ErrorStdDev': 202.81244097868523, 'R2': 0.44335969731830693, 'Pearson': 0.684076339926267} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_BU_Forecast', 'Length': 2628, 'MAPE': 212164633874.7413, 'RMSE': 114.45796973880991, 'MAE': 42.431001734727424, 'SMAPE': 0.2224, 'ErrorMean': 0.0019250402125487887, 'ErrorStdDev': 114.45796972262151, 'R2': 0.45756715640957535, 'Pearson': 0.6764446979609117} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_BU_Forecast', 'Length': 657, 'MAPE': 414078783995.0982, 'RMSE': 205.69745802934582, 'MAE': 83.88433490086257, 'SMAPE': 0.2243, 'ErrorMean': -1.0685781018514326, 'ErrorStdDev': 205.69468243144917, 'R2': 0.4517175606352366, 'Pearson': 0.6721343424119838} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_MO_Forecast', 'Length': 2628, 'MAPE': 0.0147, 'RMSE': 16.39508200461949, 'MAE': 4.1039056717666895, 'SMAPE': 0.0188, 'ErrorMean': -4.100480186118983, 'ErrorStdDev': 15.874028353932202, 'R2': 0.9888703607413539, 'Pearson': 0.9959963414676455} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_MO_Forecast', 'Length': 657, 'MAPE': 0.001, 'RMSE': 4.096389305649794, 'MAE': 0.9950693639698468, 'SMAPE': 0.001, 'ErrorMean': 0.9605559178813312, 'ErrorStdDev': 3.9821775038369593, 'R2': 0.9997825552462094, 'Pearson': 0.9999681480199881} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_OC_Forecast', 'Length': 2628, 'MAPE': 150339722331.3641, 'RMSE': 76.30966227893418, 'MAE': 31.008290318441794, 'SMAPE': 1.8797, 'ErrorMean': -0.9403458521755284, 'ErrorStdDev': 76.30386822962049, 'R2': 0.7588912539237983, 'Pearson': 0.9004498813832841} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_OC_Forecast', 'Length': 657, 'MAPE': 294435728859.113, 'RMSE': 134.8685927587039, 'MAE': 59.373301588916625, 'SMAPE': 1.8738, 'ErrorMean': -0.4861558170999856, 'ErrorStdDev': 134.8677165419309, 'R2': 0.7642953563053957, 'Pearson': 0.9034550047075101} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.0449, 'RMSE': 113.52983581489895, 'MAE': 22.24643614688311, 'SMAPE': 0.0862, 'ErrorMean': -21.080467314236248, 'ErrorStdDev': 111.55553557744827, 'R2': 0.46632861003379766, 'Pearson': 0.6966320174183644} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.0438, 'RMSE': 207.2592654710996, 'MAE': 43.837167360177915, 'SMAPE': 0.0864, 'ErrorMean': -42.680313331661175, 'ErrorStdDev': 202.81714419035433, 'R2': 0.44336001963183813, 'Pearson': 0.684076339926267} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.1309, 'RMSE': 99.04067858087497, 'MAE': 32.641232514877515, 'SMAPE': 0.2548, 'ErrorMean': -31.769305225310866, 'ErrorStdDev': 93.80707467596045, 'R2': 0.12940353054936649, 'Pearson': 0.4710376068993726} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.128, 'RMSE': 178.6257331470867, 'MAE': 64.08096231743119, 'SMAPE': 0.2543, 'ErrorMean': -63.32542307509875, 'ErrorStdDev': 167.024080104319, 'R2': 0.09273848744006563, 'Pearson': 0.45972821658771823} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_BU_Forecast', 'Length': 2628, 'MAPE': 354600815203.5042, 'RMSE': 104.79763251987087, 'MAE': 70.92016304067288, 'SMAPE': 1.9076, 'ErrorMean': 1.0382359609280002e-15, 'ErrorStdDev': 104.79763251987087, 'R2': 0.025251377035347966, 'Pearson': 0.16108056605682172} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_BU_Forecast', 'Length': 657, 'MAPE': 694785415937.4115, 'RMSE': 187.90476920896094, 'MAE': 139.48077798118828, 'SMAPE': 1.9055, 'ErrorMean': -0.5236947937339587, 'ErrorStdDev': 187.90403943299327, 'R2': -0.00396840408217547, 'Pearson': -0.028173569601943404} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_MO_Forecast', 'Length': 2628, 'MAPE': 0.0982, 'RMSE': 81.57580725051852, 'MAE': 23.55726231357833, 'SMAPE': 0.1854, 'ErrorMean': -23.227406798645447, 'ErrorStdDev': 78.19910422750371, 'R2': 0.4093741547920774, 'Pearson': 0.6764106620858829} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_MO_Forecast', 'Length': 657, 'MAPE': 0.0874, 'RMSE': 145.52501648492037, 'MAE': 43.774833844949036, 'SMAPE': 0.1711, 'ErrorMean': -41.404736056788536, 'ErrorStdDev': 139.51049514285302, 'R2': 0.397829115014108, 'Pearson': 0.6726612434138332} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_OC_Forecast', 'Length': 2628, 'MAPE': 88650203800.9442, 'RMSE': 74.31740049041066, 'MAE': 34.56397199587329, 'SMAPE': 1.7949, 'ErrorMean': -0.9403458521755284, 'ErrorStdDev': 74.31145110499718, 'R2': 0.5098029338410364, 'Pearson': 0.7512885830106135} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_OC_Forecast', 'Length': 657, 'MAPE': 173696353984.4192, 'RMSE': 134.62131726611443, 'MAE': 68.02181147861647, 'SMAPE': 1.791, 'ErrorMean': -0.48615581709998856, 'ErrorStdDev': 134.62043943987607, 'R2': 0.48468579909891574, 'Pearson': 0.7425351127083097} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.131, 'RMSE': 99.04016985764976, 'MAE': 32.64663904962336, 'SMAPE': 0.2548, 'ErrorMean': -31.71740036270408, 'ErrorStdDev': 93.82410010047553, 'R2': 0.1294124741776349, 'Pearson': 0.4710376068993725} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.128, 'RMSE': 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INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_6H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-05-14T12:00:00.000000 TimeDelta= Horizon=60 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_6H' Length=609 Min=16.082802248811667 Max=641.8037849905634 Mean=330.29583461264025 StdDev=160.3213654734028 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_6H' Min=16.082802248811667 Max=641.8037849905634 Mean=330.29583461264025 StdDev=160.3213654734028 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_6H_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [LinearTrend + Cycle + NoAR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_6H_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR' [LinearTrend + Seasonal_FourHourOfWeek + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_6H_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_6H_LinearTrend_residue_bestCycle_byL2' [Cycle] 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27.385398178490007, 33: -0.1368149339458995, 34: -25.6885668598496, 36: 11.057002241266005, 37: 45.6385309988994, 39: -46.65149452446671, 40: -9.051976748634743, 0: 25.692761236148087, 1: -0.6966177462749172, 3: -26.224062956403834, 4: 8.556472955976744, 6: 44.73670026819872, 7: -45.14525768643705, 9: -8.984152428952115, 10: 25.958611938435013, 12: 0.8442607842818006, 13: -25.910544340654354, 15: 9.81908543741784, 16: 43.50388565251524} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_12H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-05-14T12:00:00.000000 TimeDelta= Horizon=30 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_12H' Length=305 Min=67.9155433036289 Max=1250.0727079877868 Mean=659.5087320626162 StdDev=317.9335258931387 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_Signal_12H' Min=-977.5339375310714 Max=104.36583591196654 Mean=0.47391375584554346 StdDev=85.52664847251236 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_Signal_12H_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_Signal_12H_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_Signal_12H_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Signal_12H_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1755 MAPE_Forecast=0.012 MAPE_Test=0.189 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2243 SMAPE_Forecast=0.0119 SMAPE_Test=0.0734 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8603 MASE_Forecast=0.2173 MASE_Test=0.7452 -INFO:pyaf.std:MODEL_L1 L1_Fit=49.02301866092209 L1_Forecast=12.353380957482928 L1_Test=65.16039914721867 -INFO:pyaf.std:MODEL_L2 L2_Fit=56.66500524331911 L2_Forecast=14.57516659887626 L2_Test=191.652775067371 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_12H' Min=67.9155433036289 Max=1250.0727079877868 Mean=659.5087320626162 StdDev=317.9335258931387 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_12H_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR' [Lag1Trend + Seasonal_EightHourOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_12H_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_12H_Lag1Trend_residue_Seasonal_EightHourOfWeek' [Seasonal_EightHourOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_12H_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0206 MAPE_Forecast=0.0056 MAPE_Test=0.162 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0228 SMAPE_Forecast=0.0056 SMAPE_Test=0.0516 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.098 MASE_Forecast=0.1001 MASE_Test=0.4442 +INFO:pyaf.std:MODEL_L1 L1_Fit=5.585735743455776 L1_Forecast=5.689604402673726 L1_Test=38.839917028169296 +INFO:pyaf.std:MODEL_L2 L2_Fit=9.581487284102767 L2_Forecast=6.947529461086647 L2_Test=183.1786298382016 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 67.9155433036289 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_12H_Lag1Trend_residue_Seasonal_EightHourOfWeek 16.36493109696788 {9: -102.96632839786778, 10: 82.71025034670498, 12: -40.36740433703804, 13: 19.59771478801295, 15: 22.082564101786232, 16: -42.34535430542124, 18: 84.02838094077259, 19: -108.09211237141889, 0: 83.13802296515964, 1: -40.67371206816864, 3: 25.158690557474415, 4: 19.020565736829838, 6: -41.42822295471035, 7: 84.58407530005263} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-05-14T00:00:00.000000 TimeDelta= Horizon=15 @@ -171,23 +170,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_D_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0106 MAPE_Forecast=0.002 MAPE_Test=0.7017 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0106 SMAPE_Forecast=0.002 SMAPE_Test=0.114 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2379 MASE_Forecast=0.1609 MASE_Test=0.8635 -INFO:pyaf.std:MODEL_L1 L1_Fit=6.30420309499973 L1_Forecast=4.092386354445962 L1_Test=153.31035026929356 -INFO:pyaf.std:MODEL_L2 L2_Fit=8.359226859017054 L2_Forecast=5.581843926074524 L2_Test=575.7962703396787 +INFO:pyaf.std:MODEL_L1 L1_Fit=6.304203094999719 L1_Forecast=4.092386354445921 L1_Test=153.310350269294 +INFO:pyaf.std:MODEL_L2 L2_Fit=8.359226859017031 L2_Forecast=5.581843926074731 L2_Test=575.7962703396792 INFO:pyaf.std:MODEL_COMPLEXITY 27 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1019.938868453934 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_D_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7994153217889168 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.594150442902771 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.46298265184028364 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.27134883454930847 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.1978830433200269 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag26 0.1955567902238025 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag24 -0.17114317607608545 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.16909974358805152 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag5 0.16433556270903457 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag27 -0.14210284275563106 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7994153217889284 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5941504429027744 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.46298265184029963 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.2713488345493048 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.19788304332001033 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag26 0.1955567902238005 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag24 -0.17114317607609242 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.16909974358804647 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag5 0.16433556270905791 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag27 -0.14210284275562607 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_H'), (1, 'Signal_6H'), (2, 'Signal_12H'), (3, 'Signal_D')] +INFO:pyaf.std:START_FORECASTING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' RangeIndex: 3650 entries, 0 to 3649 Data columns (total 2 columns): @@ -197,74 +205,66 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:START_FORECASTING 'Signal_6H' -INFO:pyaf.std:START_FORECASTING 'Signal_12H' -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_6H' 11.348442316055298 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_12H' 11.220670461654663 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 14.73650312423706 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 23.177273750305176 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' 31.079634428024292 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 23.692601680755615 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 31.28459358215332 Int64Index: 4015 entries, 0 to 4014 -Data columns (total 41 columns): +Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_H_start 4015 non-null datetime64[ns] - 1 Signal_H 3650 non-null float64 - 2 Signal_H_Forecast 4015 non-null float64 - 3 Signal_H_Forecast_Lower_Bound 365 non-null float64 - 4 Signal_H_Forecast_Upper_Bound 365 non-null float64 - 5 Date 4015 non-null datetime64[ns] - 6 TH_6H_start 670 non-null datetime64[ns] - 7 Signal_6H 3651 non-null float64 - 8 Signal_6H_Forecast 4015 non-null float64 - 9 Signal_6H_Forecast_Lower_Bound 60 non-null float64 - 10 Signal_6H_Forecast_Upper_Bound 60 non-null float64 - 11 TH_12H_start 335 non-null datetime64[ns] - 12 Signal_12H 3651 non-null float64 - 13 Signal_12H_Forecast 4015 non-null float64 + 0 TH_12H_start 335 non-null datetime64[ns] + 1 TH_6H_start 670 non-null datetime64[ns] + 2 TH_D_start 168 non-null datetime64[ns] + 3 TH_H_start 4015 non-null datetime64[ns] + 4 Signal_H 3650 non-null float64 + 5 Signal_H_Forecast 4015 non-null float64 + 6 Signal_H_Forecast_Lower_Bound 365 non-null float64 + 7 Signal_H_Forecast_Upper_Bound 365 non-null float64 + 8 Signal_6H 609 non-null float64 + 9 Signal_6H_Forecast 670 non-null float64 + 10 Signal_6H_Forecast_Lower_Bound 60 non-null float64 + 11 Signal_6H_Forecast_Upper_Bound 60 non-null float64 + 12 Signal_12H 305 non-null float64 + 13 Signal_12H_Forecast 335 non-null float64 14 Signal_12H_Forecast_Lower_Bound 30 non-null float64 15 Signal_12H_Forecast_Upper_Bound 30 non-null float64 - 16 TH_D_start 168 non-null datetime64[ns] - 17 Signal_D 3651 non-null float64 - 18 Signal_D_Forecast 4015 non-null float64 - 19 Signal_D_Forecast_Lower_Bound 15 non-null float64 - 20 Signal_D_Forecast_Upper_Bound 15 non-null float64 - 21 Signal_H_BU_Forecast 4015 non-null float64 - 22 Signal_6H_BU_Forecast 4015 non-null float64 - 23 Signal_12H_BU_Forecast 4015 non-null float64 - 24 Signal_D_BU_Forecast 4015 non-null float64 - 25 Signal_D_AHP_TD_Forecast 4015 non-null float64 - 26 Signal_12H_AHP_TD_Forecast 4015 non-null float64 - 27 Signal_6H_AHP_TD_Forecast 4015 non-null float64 - 28 Signal_H_AHP_TD_Forecast 4015 non-null float64 - 29 Signal_D_PHA_TD_Forecast 4015 non-null float64 - 30 Signal_12H_PHA_TD_Forecast 4015 non-null float64 - 31 Signal_6H_PHA_TD_Forecast 4015 non-null float64 - 32 Signal_H_PHA_TD_Forecast 4015 non-null float64 - 33 Signal_12H_MO_Forecast 4015 non-null float64 - 34 Signal_6H_MO_Forecast 4015 non-null float64 - 35 Signal_H_MO_Forecast 4015 non-null float64 - 36 Signal_D_MO_Forecast 4015 non-null float64 - 37 Signal_H_OC_Forecast 4015 non-null float64 - 38 Signal_6H_OC_Forecast 4015 non-null float64 - 39 Signal_12H_OC_Forecast 4015 non-null float64 - 40 Signal_D_OC_Forecast 4015 non-null float64 -dtypes: datetime64[ns](5), float64(36) + 16 Signal_D 3651 non-null float64 + 17 Signal_D_Forecast 4015 non-null float64 + 18 Signal_D_Forecast_Lower_Bound 15 non-null float64 + 19 Signal_D_Forecast_Upper_Bound 15 non-null float64 + 20 Signal_H_BU_Forecast 4015 non-null float64 + 21 Signal_6H_BU_Forecast 4015 non-null float64 + 22 Signal_12H_BU_Forecast 670 non-null float64 + 23 Signal_D_BU_Forecast 335 non-null float64 + 24 Signal_D_AHP_TD_Forecast 4015 non-null float64 + 25 Signal_12H_AHP_TD_Forecast 4015 non-null float64 + 26 Signal_6H_AHP_TD_Forecast 4015 non-null float64 + 27 Signal_H_AHP_TD_Forecast 4015 non-null float64 + 28 Signal_D_PHA_TD_Forecast 4015 non-null float64 + 29 Signal_12H_PHA_TD_Forecast 4015 non-null float64 + 30 Signal_6H_PHA_TD_Forecast 4015 non-null float64 + 31 Signal_H_PHA_TD_Forecast 4015 non-null float64 + 32 Signal_12H_MO_Forecast 335 non-null float64 + 33 Signal_6H_MO_Forecast 335 non-null float64 + 34 Signal_H_MO_Forecast 335 non-null float64 + 35 Signal_D_MO_Forecast 335 non-null float64 + 36 Signal_H_OC_Forecast 335 non-null float64 + 37 Signal_6H_OC_Forecast 335 non-null float64 + 38 Signal_12H_OC_Forecast 335 non-null float64 + 39 Signal_D_OC_Forecast 335 non-null float64 +dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_H_start ... Signal_D_OC_Forecast -4010 2001-07-11 02:00:00 ... 27.832887 -4011 2001-07-11 03:00:00 ... 23.990771 -4012 2001-07-11 04:00:00 ... 23.827730 -4013 2001-07-11 05:00:00 ... 23.956411 -4014 2001-07-11 06:00:00 ... 179.733013 + TH_12H_start ... Signal_D_OC_Forecast +4010 NaT ... NaN +4011 NaT ... NaN +4012 NaT ... NaN +4013 NaT ... NaN +4014 NaT ... NaN -[5 rows x 41 columns] +[5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D_W.log b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D_W.log index 6906ca653..226bf6a3a 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D_W.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D_W.log @@ -1,36 +1,19 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.375624418258667 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.7813715934753418 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'H': 3600.0, '6H': 21600.0, '12H': 43200.0, 'D': 86400.0, 'W': 604800.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA H {'TH_H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 01:00:00'), 2: Timestamp('2001-01-25 02:00:00'), 3: Timestamp('2001-01-25 03:00:00'), 4: Timestamp('2001-01-25 04:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 6H {'TH_6H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 06:00:00'), 2: Timestamp('2001-01-25 12:00:00'), 3: Timestamp('2001-01-25 18:00:00'), 4: Timestamp('2001-01-26 00:00:00')}, 'Signal': {0: 16.082802248811667, 1: 51.83274105481723, 2: 87.77037544957828, 3: 58.42664714689759, 4: 29.433643202513927}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 12H {'TH_12H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 12:00:00'), 2: Timestamp('2001-01-26 00:00:00'), 3: Timestamp('2001-01-26 12:00:00'), 4: Timestamp('2001-01-27 00:00:00')}, 'Signal': {0: 67.9155433036289, 1: 146.19702259647588, 2: 99.27213122050448, 3: 127.36564488404751, 4: 155.37956250958317}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 214.11256590010478, 1: 226.63777610455196, 2: 264.8441387439829, 3: 279.33413226235155, 4: 294.2287615834373}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-02-01 00:00:00'), 2: Timestamp('2001-02-08 00:00:00'), 3: Timestamp('2001-02-15 00:00:00'), 4: Timestamp('2001-02-22 00:00:00')}, 'Signal': {0: 984.9286130109912, 1: 2332.6024363333086, 2: 3081.402510511717, 3: 3788.218373039306, 4: 4465.012563072666}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'H': 365, '6H': 60, '12H': 30, 'D': 15, 'W': 2} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_H'), (1, 'Signal_6H'), (2, 'Signal_12H'), (3, 'Signal_D'), (4, 'Signal_W')] +INFO:pyaf.std:START_TRAINING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' -INFO:pyaf.std:START_TRAINING 'Signal_H' -INFO:pyaf.std:START_TRAINING 'Signal_6H' -INFO:pyaf.std:START_TRAINING 'Signal_12H' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:START_TRAINING 'Signal_W' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_W' 5.3368916511535645 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 8.42037558555603 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_12H' 11.03566312789917 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_6H' 11.826795816421509 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_H' 35.930384159088135 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' 77.14198446273804 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_H'), (1, 'Signal_6H'), (2, 'Signal_12H'), (3, 'Signal_D'), (4, 'Signal_W')] -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:START_FORECASTING 'Signal_6H' -INFO:pyaf.std:START_FORECASTING 'Signal_12H' -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_12H' 10.825392723083496 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_6H' 11.260220050811768 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 13.817128419876099 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 15.578611612319946 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 25.37703514099121 +INFO:pyaf.std:START_FORECASTING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' 26.22898268699646 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -39,146 +22,157 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3, 4] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_H_start', 'Signal_H', 'Signal_H_Forecast', - 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', - 'Date', 'TH_6H_start', 'Signal_6H', 'Signal_6H_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'TH_H_start', 'TH_6H_start', 'TH_W_start', 'TH_12H_start', + 'Signal_H', 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', + 'Signal_H_Forecast_Upper_Bound', 'Signal_6H', 'Signal_6H_Forecast', 'Signal_6H_Forecast_Lower_Bound', 'Signal_6H_Forecast_Upper_Bound', - 'TH_12H_start', 'Signal_12H', 'Signal_12H_Forecast', - 'Signal_12H_Forecast_Lower_Bound', 'Signal_12H_Forecast_Upper_Bound', - 'TH_D_start', 'Signal_D', 'Signal_D_Forecast', + 'Signal_12H', 'Signal_12H_Forecast', 'Signal_12H_Forecast_Lower_Bound', + 'Signal_12H_Forecast_Upper_Bound', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', - 'TH_W_start', 'Signal_W', 'Signal_W_Forecast', - 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', - 'Signal_H_BU_Forecast', 'Signal_6H_BU_Forecast', - 'Signal_12H_BU_Forecast', 'Signal_D_BU_Forecast', - 'Signal_W_BU_Forecast', 'Signal_W_AHP_TD_Forecast', - 'Signal_D_AHP_TD_Forecast', 'Signal_12H_AHP_TD_Forecast', - 'Signal_6H_AHP_TD_Forecast', 'Signal_H_AHP_TD_Forecast', - 'Signal_W_PHA_TD_Forecast', 'Signal_D_PHA_TD_Forecast', - 'Signal_12H_PHA_TD_Forecast', 'Signal_6H_PHA_TD_Forecast', - 'Signal_H_PHA_TD_Forecast', 'Signal_12H_MO_Forecast', - 'Signal_6H_MO_Forecast', 'Signal_H_MO_Forecast', 'Signal_D_MO_Forecast', - 'Signal_W_MO_Forecast', 'Signal_H_OC_Forecast', 'Signal_6H_OC_Forecast', + 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', + 'Signal_W_Forecast_Upper_Bound', 'Signal_H_BU_Forecast', + 'Signal_6H_BU_Forecast', 'Signal_12H_BU_Forecast', + 'Signal_D_BU_Forecast', 'Signal_W_BU_Forecast', + 'Signal_W_AHP_TD_Forecast', 'Signal_D_AHP_TD_Forecast', + 'Signal_12H_AHP_TD_Forecast', 'Signal_6H_AHP_TD_Forecast', + 'Signal_H_AHP_TD_Forecast', 'Signal_W_PHA_TD_Forecast', + 'Signal_D_PHA_TD_Forecast', 'Signal_12H_PHA_TD_Forecast', + 'Signal_6H_PHA_TD_Forecast', 'Signal_H_PHA_TD_Forecast', + 'Signal_12H_MO_Forecast', 'Signal_6H_MO_Forecast', + 'Signal_H_MO_Forecast', 'Signal_D_MO_Forecast', 'Signal_W_MO_Forecast', + 'Signal_H_OC_Forecast', 'Signal_6H_OC_Forecast', 'Signal_12H_OC_Forecast', 'Signal_D_OC_Forecast', 'Signal_W_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_H']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_6H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_6H']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_12H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_12H']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (3, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (3, ['Signal_D']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (4, ['Signal_W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (4, ['Signal_W']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.0782, 'RMSE': 156.40918048743836, 'MAE': 39.994344981383584, 'SMAPE': 0.1554, 'ErrorMean': -39.36327277293088, 'ErrorStdDev': 151.37491369892143, 'R2': -0.012927547790971383, 'Pearson': 0.23033904581505887} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.078, 'RMSE': 280.687286316834, 'MAE': 78.3586771278493, 'SMAPE': 0.1556, 'ErrorMean': -77.71268727122003, 'ErrorStdDev': 269.7148326232613, 'R2': -0.020920607667493485, 'Pearson': 0.2422615442083855} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_BU_Forecast', 'Length': 2628, 'MAPE': 212164633874.7413, 'RMSE': 114.45796973880991, 'MAE': 42.431001734727424, 'SMAPE': 0.2224, 'ErrorMean': 0.0019250402125487887, 'ErrorStdDev': 114.45796972262151, 'R2': 0.45756715640957535, 'Pearson': 0.6764446979609117} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_BU_Forecast', 'Length': 657, 'MAPE': 414078783995.0982, 'RMSE': 205.69745802934582, 'MAE': 83.88433490086257, 'SMAPE': 0.2243, 'ErrorMean': -1.0685781018514326, 'ErrorStdDev': 205.69468243144917, 'R2': 0.4517175606352366, 'Pearson': 0.6721343424119838} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_MO_Forecast', 'Length': 2628, 'MAPE': 0.0147, 'RMSE': 16.39508200461949, 'MAE': 4.1039056717666895, 'SMAPE': 0.0188, 'ErrorMean': -4.100480186118983, 'ErrorStdDev': 15.874028353932202, 'R2': 0.9888703607413539, 'Pearson': 0.9959963414676455} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_MO_Forecast', 'Length': 657, 'MAPE': 0.001, 'RMSE': 4.096389305649794, 'MAE': 0.9950693639698468, 'SMAPE': 0.001, 'ErrorMean': 0.9605559178813312, 'ErrorStdDev': 3.9821775038369593, 'R2': 0.9997825552462094, 'Pearson': 0.9999681480199881} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_OC_Forecast', 'Length': 2628, 'MAPE': 120271777865.1244, 'RMSE': 139.95879253576263, 'MAE': 40.20500618253234, 'SMAPE': 1.9008, 'ErrorMean': -0.8174293193172165, 'ErrorStdDev': 139.9564054174605, 'R2': 0.18893758681060646, 'Pearson': 0.6211176877496704} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_OC_Forecast', 'Length': 657, 'MAPE': 235548583087.322, 'RMSE': 252.46986970276384, 'MAE': 78.19157766786192, 'SMAPE': 1.8953, 'ErrorMean': -0.12011445655981462, 'ErrorStdDev': 252.46984113008008, 'R2': 0.17402748016618297, 'Pearson': 0.6237289516176617} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.0785, 'RMSE': 156.45331601140913, 'MAE': 40.09965015854925, 'SMAPE': 0.1556, 'ErrorMean': -39.25796759576521, 'ErrorStdDev': 151.44785264643306, 'R2': -0.013499283996047495, 'Pearson': 0.23033904581505887} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.0782, 'RMSE': 280.7409372354247, 'MAE': 78.571935707015, 'SMAPE': 0.1558, 'ErrorMean': -77.49942869205432, 'ErrorStdDev': 269.83200772375, 'R2': -0.0213109250477328, 'Pearson': 0.24226154420838555} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.1631, 'RMSE': 112.90334835878996, 'MAE': 41.399600168356905, 'SMAPE': 0.323, 'ErrorMean': -40.865280541258116, 'ErrorStdDev': 105.24825374755892, 'R2': -0.13136654138994897, 'Pearson': 0.1418525787745124} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.1619, 'RMSE': 202.34575341314832, 'MAE': 81.27052074256547, 'SMAPE': 0.3232, 'ErrorMean': -80.75447544527512, 'ErrorStdDev': 185.53306610923315, 'R2': -0.164213491914176, 'Pearson': 0.15443945661751246} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_BU_Forecast', 'Length': 2628, 'MAPE': 354600815203.5042, 'RMSE': 104.79763251987087, 'MAE': 70.92016304067288, 'SMAPE': 1.9076, 'ErrorMean': 1.0382359609280002e-15, 'ErrorStdDev': 104.79763251987087, 'R2': 0.025251377035347966, 'Pearson': 0.16108056605682172} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_BU_Forecast', 'Length': 657, 'MAPE': 694785415937.4115, 'RMSE': 187.90476920896094, 'MAE': 139.48077798118828, 'SMAPE': 1.9055, 'ErrorMean': -0.5236947937339587, 'ErrorStdDev': 187.90403943299327, 'R2': -0.00396840408217547, 'Pearson': -0.028173569601943404} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_MO_Forecast', 'Length': 2628, 'MAPE': 0.0982, 'RMSE': 81.57580725051852, 'MAE': 23.55726231357833, 'SMAPE': 0.1854, 'ErrorMean': -23.227406798645447, 'ErrorStdDev': 78.19910422750371, 'R2': 0.4093741547920774, 'Pearson': 0.6764106620858829} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_MO_Forecast', 'Length': 657, 'MAPE': 0.0874, 'RMSE': 145.52501648492037, 'MAE': 43.774833844949036, 'SMAPE': 0.1711, 'ErrorMean': -41.404736056788536, 'ErrorStdDev': 139.51049514285302, 'R2': 0.397829115014108, 'Pearson': 0.6726612434138332} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_OC_Forecast', 'Length': 2628, 'MAPE': 70920163040.8207, 'RMSE': 147.3724606667857, 'MAE': 40.3032236180747, 'SMAPE': 1.8139, 'ErrorMean': -0.8174293193172165, 'ErrorStdDev': 147.37019363592904, 'R2': -0.9276241426117315, 'Pearson': 0.48195108559128425} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_OC_Forecast', 'Length': 657, 'MAPE': 138957083187.59, 'RMSE': 268.3101765609131, 'MAE': 79.98717114183518, 'SMAPE': 1.8106, 'ErrorMean': -0.12011445655981393, 'ErrorStdDev': 268.31014967508344, 'R2': -1.0470041791331446, 'Pearson': 0.4804180380171492} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6H_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.1634, 'RMSE': 112.93019236919467, 'MAE': 41.458730206146456, 'SMAPE': 0.3232, 'ErrorMean': -40.80615050346855, 'ErrorStdDev': 105.29998304668233, 'R2': -0.13190459497471774, 'Pearson': 0.14185257877451238} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6H_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.1622, 'RMSE': 202.37671867407076, 'MAE': 81.39026782608423, 'SMAPE': 0.3235, 'ErrorMean': -80.63472836175636, 'ErrorStdDev': 185.6189021713838, 'R2': -0.16456984170005873, 'Pearson': 0.1544394566175125} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.036, 'RMSE': 213.02991692953862, 'MAE': 37.02238460205372, 'SMAPE': 0.0718, 'ErrorMean': -36.685861408377235, 'ErrorStdDev': 209.8473089647124, 'R2': 0.1011197703033545, 'Pearson': 0.35808537760176584} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.0351, 'RMSE': 377.01825874272714, 'MAE': 70.61216925661397, 'SMAPE': 0.0701, 'ErrorMean': -70.25902308993086, 'ErrorStdDev': 370.4138727151651, 'R2': 0.1061392502992844, 'Pearson': 0.37088855354088} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 2628, 'MAPE': 190293514061.2312, 'RMSE': 162.21438527510193, 'MAE': 42.491417982329445, 'SMAPE': 0.1185, 'ErrorMean': -4.437651923323303, 'ErrorStdDev': 162.1536741353295, 'R2': 0.4788060537682718, 'Pearson': 0.6933164020926593} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 657, 'MAPE': 431561348174.4422, 'RMSE': 290.89967794787185, 'MAE': 84.00302676818573, 'SMAPE': 0.1123, 'ErrorMean': 2.309242866698612, 'ErrorStdDev': 290.89051209614615, 'R2': 0.46785304468235633, 'Pearson': 0.6843314756521268} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 2628, 'MAPE': 190293514061.2312, 'RMSE': 162.21438527510193, 'MAE': 42.491417982329445, 'SMAPE': 0.1185, 'ErrorMean': -4.437651923323303, 'ErrorStdDev': 162.1536741353295, 'R2': 0.4788060537682718, 'Pearson': 0.6933164020926593} 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+INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_6H_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR' [LinearTrend + Seasonal_FourHourOfWeek + NoAR] INFO:pyaf.std:TREND_DETAIL '_Signal_6H_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_6H_LinearTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_6H_LinearTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0211 MAPE_Forecast=0.0129 MAPE_Test=0.0468 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0205 SMAPE_Forecast=0.013 SMAPE_Test=0.0301 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0833 MASE_Forecast=0.1546 MASE_Test=0.3113 -INFO:pyaf.std:MODEL_L1 L1_Fit=3.4301553636211373 L1_Forecast=6.449946378028857 L1_Test=14.775895663086402 -INFO:pyaf.std:MODEL_L2 L2_Fit=4.216537770280827 L2_Forecast=7.048864426103891 L2_Test=55.88693925485074 -INFO:pyaf.std:MODEL_COMPLEXITY 24 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_6H_LinearTrend_residue_Seasonal_FourHourOfWeek' [Seasonal_FourHourOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_6H_LinearTrend_residue_Seasonal_FourHourOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0209 MAPE_Forecast=0.0129 MAPE_Test=0.0468 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0203 SMAPE_Forecast=0.013 SMAPE_Test=0.0301 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0814 MASE_Forecast=0.1555 MASE_Test=0.3113 +INFO:pyaf.std:MODEL_L1 L1_Fit=3.351992828830046 L1_Forecast=6.489184222212897 L1_Test=14.778241800794428 +INFO:pyaf.std:MODEL_L2 L2_Fit=4.246767470722753 L2_Forecast=7.1288332211292955 L2_Test=55.80621722101477 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (62.69729715449532, array([383.63511514])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_6H_LinearTrend_residue_Seasonal_FourHourOfWeek -0.6986311803276521 {18: -45.11319049002883, 19: -8.044585368700723, 21: 26.126965620336705, 22: -1.3453081475213224, 24: -26.212188940159564, 25: 10.77413656616676, 27: 45.881631895662395, 28: -44.746917040668265, 30: -8.504234112241875, 31: 27.385398178490007, 33: -0.1368149339458995, 34: -25.6885668598496, 36: 11.057002241266005, 37: 45.6385309988994, 39: -46.65149452446671, 40: -9.051976748634743, 0: 25.692761236148087, 1: -0.6966177462749172, 3: -26.224062956403834, 4: 8.556472955976744, 6: 44.73670026819872, 7: -45.14525768643705, 9: -8.984152428952115, 10: 25.958611938435013, 12: 0.8442607842818006, 13: -25.910544340654354, 15: 9.81908543741784, 16: 43.50388565251524} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_12H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-05-14T12:00:00.000000 TimeDelta= Horizon=30 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_12H' Length=305 Min=67.9155433036289 Max=1250.0727079877868 Mean=659.5087320626162 StdDev=317.9335258931387 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_Signal_12H' Min=-977.5339375310714 Max=104.36583591196654 Mean=0.47391375584554346 StdDev=85.52664847251236 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_Signal_12H_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [ConstantTrend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'Diff_Signal_12H_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_Signal_12H_ConstantTrend_residue_bestCycle_byL2' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Signal_12H_ConstantTrend_residue_bestCycle_byL2_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1755 MAPE_Forecast=0.012 MAPE_Test=0.189 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2243 SMAPE_Forecast=0.0119 SMAPE_Test=0.0734 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8603 MASE_Forecast=0.2173 MASE_Test=0.7452 -INFO:pyaf.std:MODEL_L1 L1_Fit=49.02301866092209 L1_Forecast=12.353380957482928 L1_Test=65.16039914721867 -INFO:pyaf.std:MODEL_L2 L2_Fit=56.66500524331911 L2_Forecast=14.57516659887626 L2_Test=191.652775067371 -INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_12H' Min=67.9155433036289 Max=1250.0727079877868 Mean=659.5087320626162 StdDev=317.9335258931387 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_12H_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR' [Lag1Trend + Seasonal_EightHourOfWeek + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_12H_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_12H_Lag1Trend_residue_Seasonal_EightHourOfWeek' [Seasonal_EightHourOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_12H_Lag1Trend_residue_Seasonal_EightHourOfWeek_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0206 MAPE_Forecast=0.0056 MAPE_Test=0.162 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0228 SMAPE_Forecast=0.0056 SMAPE_Test=0.0516 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.098 MASE_Forecast=0.1001 MASE_Test=0.4442 +INFO:pyaf.std:MODEL_L1 L1_Fit=5.585735743455776 L1_Forecast=5.689604402673726 L1_Test=38.839917028169296 +INFO:pyaf.std:MODEL_L2 L2_Fit=9.581487284102767 L2_Forecast=6.947529461086647 L2_Test=183.1786298382016 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 67.9155433036289 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_12H_Lag1Trend_residue_Seasonal_EightHourOfWeek 16.36493109696788 {9: -102.96632839786778, 10: 82.71025034670498, 12: -40.36740433703804, 13: 19.59771478801295, 15: 22.082564101786232, 16: -42.34535430542124, 18: 84.02838094077259, 19: -108.09211237141889, 0: 83.13802296515964, 1: -40.67371206816864, 3: 25.158690557474415, 4: 19.020565736829838, 6: -41.42822295471035, 7: 84.58407530005263} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-05-14T00:00:00.000000 TimeDelta= Horizon=15 @@ -192,44 +186,57 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_D_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0106 MAPE_Forecast=0.002 MAPE_Test=0.7017 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0106 SMAPE_Forecast=0.002 SMAPE_Test=0.114 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2379 MASE_Forecast=0.1609 MASE_Test=0.8635 -INFO:pyaf.std:MODEL_L1 L1_Fit=6.30420309499973 L1_Forecast=4.092386354445962 L1_Test=153.31035026929356 -INFO:pyaf.std:MODEL_L2 L2_Fit=8.359226859017054 L2_Forecast=5.581843926074524 L2_Test=575.7962703396787 +INFO:pyaf.std:MODEL_L1 L1_Fit=6.304203094999719 L1_Forecast=4.092386354445921 L1_Test=153.310350269294 +INFO:pyaf.std:MODEL_L2 L2_Fit=8.359226859017031 L2_Forecast=5.581843926074731 L2_Test=575.7962703396792 INFO:pyaf.std:MODEL_COMPLEXITY 27 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1019.938868453934 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_D_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7994153217889168 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.594150442902771 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.46298265184028364 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.27134883454930847 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.1978830433200269 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag26 0.1955567902238025 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag24 -0.17114317607608545 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.16909974358805152 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag5 0.16433556270903457 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag27 -0.14210284275563106 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7994153217889284 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5941504429027744 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.46298265184029963 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.2713488345493048 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.19788304332001033 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag26 0.1955567902238005 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag24 -0.17114317607609242 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.16909974358804647 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag5 0.16433556270905791 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag27 -0.14210284275562607 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_W_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-06-21T00:00:00.000000 TimeDelta= Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_W' Length=22 Min=984.9286130109912 Max=16445.15453152187 Mean=9023.761586324636 StdDev=4522.866525072544 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_W' Min=984.9286130109912 Max=16445.15453152187 Mean=9023.761586324636 StdDev=4522.866525072544 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_W_LinearTrend_residue_zeroCycle_residue_AR(5)' [LinearTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Signal_W_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_LinearTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_LinearTrend_residue_zeroCycle_residue_AR(5)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0305 MAPE_Forecast=0.0305 MAPE_Test=0.0305 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.028 SMAPE_Forecast=0.028 SMAPE_Test=0.028 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0764 MASE_Forecast=0.0764 MASE_Test=0.0764 -INFO:pyaf.std:MODEL_L1 L1_Fit=56.232307337462046 L1_Forecast=56.232307337462046 L1_Test=56.232307337462046 -INFO:pyaf.std:MODEL_L2 L2_Fit=133.2074961866138 L2_Forecast=133.2074961866138 L2_Test=133.2074961866138 -INFO:pyaf.std:MODEL_COMPLEXITY 21 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_W_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [ConstantTrend + Seasonal_WeekOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_W_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_W_ConstantTrend_residue_Seasonal_WeekOfYear' [Seasonal_WeekOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0 MAPE_Forecast=0.0 MAPE_Test=0.0 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0 SMAPE_Forecast=0.0 SMAPE_Test=0.0 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0 MASE_Forecast=0.0 MASE_Test=0.0 +INFO:pyaf.std:MODEL_L1 L1_Fit=4.650825179520656e-14 L1_Forecast=4.650825179520656e-14 L1_Test=4.650825179520656e-14 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.3923737144427707e-13 L2_Forecast=1.3923737144427707e-13 L2_Test=1.3923737144427707e-13 +INFO:pyaf.std:MODEL_COMPLEXITY 4 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 9023.761586324636 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_W_ConstantTrend_residue_Seasonal_WeekOfYear 23.923739338607447 {4: -8038.832973313644, 5: -6691.159149991327, 6: -5942.3590758129185, 7: -5235.543213285329, 8: -4558.749023251969, 9: -3853.047487716487, 10: -3156.4634189573853, 11: -2432.7118866829915, 12: -1737.9671756549178, 13: -992.8185304609488, 14: -330.3629016064606, 15: 378.2103802836755, 16: 1102.0428352718627, 17: 1791.3498721942306, 18: 2506.6997906815213, 19: 3191.7913693198043, 20: 3909.4510342017784, 21: 4622.7615005948155, 22: 5315.248770340349, 23: 6021.324178581617, 24: 6709.7421600675025, 25: 7421.392945197233} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_W_LinearTrend_residue_zeroCycle_residue_Lag1 0.8104319964046303 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_W_LinearTrend_residue_zeroCycle_residue_Lag5 0.09413720078071863 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_W_LinearTrend_residue_zeroCycle_residue_Lag4 0.05913544728046938 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_W_LinearTrend_residue_zeroCycle_residue_Lag2 0.01651560980835884 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_W_LinearTrend_residue_zeroCycle_residue_Lag3 0.003946398669592021 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_H'), (1, 'Signal_6H'), (2, 'Signal_12H'), (3, 'Signal_D'), (4, 'Signal_W')] +INFO:pyaf.std:START_FORECASTING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' RangeIndex: 3650 entries, 0 to 3649 Data columns (total 2 columns): @@ -239,86 +246,76 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:START_FORECASTING 'Signal_6H' -INFO:pyaf.std:START_FORECASTING 'Signal_12H' -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_6H' 10.597826719284058 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_12H' 11.138136386871338 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 12.045191287994385 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 16.930843353271484 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 25.952411651611328 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' 23.32908606529236 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 26.44603157043457 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 23.583929538726807 Int64Index: 4015 entries, 0 to 4014 -Data columns (total 51 columns): +Data columns (total 50 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_H_start 4015 non-null datetime64[ns] - 1 Signal_H 3650 non-null float64 - 2 Signal_H_Forecast 4015 non-null float64 - 3 Signal_H_Forecast_Lower_Bound 365 non-null float64 - 4 Signal_H_Forecast_Upper_Bound 365 non-null float64 - 5 Date 4015 non-null datetime64[ns] - 6 TH_6H_start 670 non-null datetime64[ns] - 7 Signal_6H 3651 non-null float64 - 8 Signal_6H_Forecast 4015 non-null float64 - 9 Signal_6H_Forecast_Lower_Bound 60 non-null float64 - 10 Signal_6H_Forecast_Upper_Bound 60 non-null float64 - 11 TH_12H_start 335 non-null datetime64[ns] - 12 Signal_12H 3651 non-null float64 - 13 Signal_12H_Forecast 4015 non-null float64 - 14 Signal_12H_Forecast_Lower_Bound 30 non-null float64 - 15 Signal_12H_Forecast_Upper_Bound 30 non-null float64 - 16 TH_D_start 168 non-null datetime64[ns] - 17 Signal_D 3651 non-null float64 - 18 Signal_D_Forecast 4015 non-null float64 + 0 TH_D_start 168 non-null datetime64[ns] + 1 TH_H_start 4015 non-null datetime64[ns] + 2 TH_6H_start 670 non-null datetime64[ns] + 3 TH_W_start 24 non-null datetime64[ns] + 4 TH_12H_start 335 non-null datetime64[ns] + 5 Signal_H 3650 non-null float64 + 6 Signal_H_Forecast 4015 non-null float64 + 7 Signal_H_Forecast_Lower_Bound 365 non-null float64 + 8 Signal_H_Forecast_Upper_Bound 365 non-null float64 + 9 Signal_6H 609 non-null float64 + 10 Signal_6H_Forecast 670 non-null float64 + 11 Signal_6H_Forecast_Lower_Bound 60 non-null float64 + 12 Signal_6H_Forecast_Upper_Bound 60 non-null float64 + 13 Signal_12H 305 non-null float64 + 14 Signal_12H_Forecast 335 non-null float64 + 15 Signal_12H_Forecast_Lower_Bound 30 non-null float64 + 16 Signal_12H_Forecast_Upper_Bound 30 non-null float64 + 17 Signal_D 153 non-null float64 + 18 Signal_D_Forecast 168 non-null float64 19 Signal_D_Forecast_Lower_Bound 15 non-null float64 20 Signal_D_Forecast_Upper_Bound 15 non-null float64 - 21 TH_W_start 24 non-null datetime64[ns] - 22 Signal_W 3651 non-null float64 - 23 Signal_W_Forecast 4015 non-null float64 - 24 Signal_W_Forecast_Lower_Bound 2 non-null float64 - 25 Signal_W_Forecast_Upper_Bound 2 non-null float64 - 26 Signal_H_BU_Forecast 4015 non-null float64 - 27 Signal_6H_BU_Forecast 4015 non-null float64 - 28 Signal_12H_BU_Forecast 4015 non-null float64 - 29 Signal_D_BU_Forecast 4015 non-null float64 - 30 Signal_W_BU_Forecast 4015 non-null float64 - 31 Signal_W_AHP_TD_Forecast 4015 non-null float64 - 32 Signal_D_AHP_TD_Forecast 4015 non-null float64 - 33 Signal_12H_AHP_TD_Forecast 4015 non-null float64 - 34 Signal_6H_AHP_TD_Forecast 4015 non-null float64 - 35 Signal_H_AHP_TD_Forecast 4015 non-null float64 - 36 Signal_W_PHA_TD_Forecast 4015 non-null float64 - 37 Signal_D_PHA_TD_Forecast 4015 non-null float64 - 38 Signal_12H_PHA_TD_Forecast 4015 non-null float64 - 39 Signal_6H_PHA_TD_Forecast 4015 non-null float64 - 40 Signal_H_PHA_TD_Forecast 4015 non-null float64 - 41 Signal_12H_MO_Forecast 4015 non-null float64 - 42 Signal_6H_MO_Forecast 4015 non-null float64 - 43 Signal_H_MO_Forecast 4015 non-null float64 - 44 Signal_D_MO_Forecast 4015 non-null float64 - 45 Signal_W_MO_Forecast 4015 non-null float64 - 46 Signal_H_OC_Forecast 4015 non-null float64 - 47 Signal_6H_OC_Forecast 4015 non-null float64 - 48 Signal_12H_OC_Forecast 4015 non-null float64 - 49 Signal_D_OC_Forecast 4015 non-null float64 - 50 Signal_W_OC_Forecast 4015 non-null float64 -dtypes: datetime64[ns](6), float64(45) + 21 Signal_W 3651 non-null float64 + 22 Signal_W_Forecast 4015 non-null float64 + 23 Signal_W_Forecast_Lower_Bound 2 non-null float64 + 24 Signal_W_Forecast_Upper_Bound 2 non-null float64 + 25 Signal_H_BU_Forecast 4015 non-null float64 + 26 Signal_6H_BU_Forecast 4015 non-null float64 + 27 Signal_12H_BU_Forecast 670 non-null float64 + 28 Signal_D_BU_Forecast 335 non-null float64 + 29 Signal_W_BU_Forecast 168 non-null float64 + 30 Signal_W_AHP_TD_Forecast 4015 non-null float64 + 31 Signal_D_AHP_TD_Forecast 4015 non-null float64 + 32 Signal_12H_AHP_TD_Forecast 4015 non-null float64 + 33 Signal_6H_AHP_TD_Forecast 4015 non-null float64 + 34 Signal_H_AHP_TD_Forecast 4015 non-null float64 + 35 Signal_W_PHA_TD_Forecast 4015 non-null float64 + 36 Signal_D_PHA_TD_Forecast 4015 non-null float64 + 37 Signal_12H_PHA_TD_Forecast 4015 non-null float64 + 38 Signal_6H_PHA_TD_Forecast 4015 non-null float64 + 39 Signal_H_PHA_TD_Forecast 4015 non-null float64 + 40 Signal_12H_MO_Forecast 335 non-null float64 + 41 Signal_6H_MO_Forecast 335 non-null float64 + 42 Signal_H_MO_Forecast 335 non-null float64 + 43 Signal_D_MO_Forecast 335 non-null float64 + 44 Signal_W_MO_Forecast 168 non-null float64 + 45 Signal_H_OC_Forecast 168 non-null float64 + 46 Signal_6H_OC_Forecast 168 non-null float64 + 47 Signal_12H_OC_Forecast 168 non-null float64 + 48 Signal_D_OC_Forecast 168 non-null float64 + 49 Signal_W_OC_Forecast 168 non-null float64 +dtypes: datetime64[ns](5), float64(45) memory usage: 1.7 MB - TH_H_start Signal_H ... Signal_D_OC_Forecast Signal_W_OC_Forecast -4010 2001-07-11 02:00:00 NaN ... 22.266310 22.266310 -4011 2001-07-11 03:00:00 NaN ... 19.192617 19.192617 -4012 2001-07-11 04:00:00 NaN ... 19.062184 19.062184 -4013 2001-07-11 05:00:00 NaN ... 19.165129 19.165129 -4014 2001-07-11 06:00:00 NaN ... 143.786410 143.786410 + TH_D_start TH_H_start ... Signal_D_OC_Forecast Signal_W_OC_Forecast +4010 NaT 2001-07-11 02:00:00 ... NaN NaN +4011 NaT 2001-07-11 03:00:00 ... NaN NaN +4012 NaT 2001-07-11 04:00:00 ... NaN NaN +4013 NaT 2001-07-11 05:00:00 ... NaN NaN +4014 NaT 2001-07-11 06:00:00 ... NaN NaN -[5 rows x 51 columns] +[5 rows x 50 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_D.log b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_D.log index 1ce78b953..e095312c0 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_D.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_D.log @@ -1,21 +1,16 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.5318937301635742 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.0543439388275146 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'H': 3600.0, 'D': 86400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA H {'TH_H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 01:00:00'), 2: Timestamp('2001-01-25 02:00:00'), 3: Timestamp('2001-01-25 03:00:00'), 4: Timestamp('2001-01-25 04:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 214.11256590010478, 1: 226.63777610455196, 2: 264.8441387439829, 3: 279.33413226235155, 4: 294.2287615834373}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'H': 365, 'D': 15} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_H'), (1, 'Signal_D')] +INFO:pyaf.std:START_TRAINING '['Signal_H', 'Signal_D']' -INFO:pyaf.std:START_TRAINING 'Signal_H' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 8.899769067764282 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_H' 36.58265733718872 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_H', 'Signal_D']' 71.48737931251526 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_H'), (1, 'Signal_D')] -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 16.736292362213135 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 25.772560834884644 +INFO:pyaf.std:START_FORECASTING '['Signal_H', 'Signal_D']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_D']' 43.46589779853821 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -24,66 +19,62 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_H_start', 'Signal_H', 'Signal_H_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_H_start', 'TH_D_start', 'Signal_H', 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', - 'Date', 'TH_D_start', 'Signal_D', 'Signal_D_Forecast', - 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', - 'Signal_H_BU_Forecast', 'Signal_D_BU_Forecast', - 'Signal_D_AHP_TD_Forecast', 'Signal_H_AHP_TD_Forecast', - 'Signal_D_PHA_TD_Forecast', 'Signal_H_PHA_TD_Forecast', - 'Signal_D_MO_Forecast', 'Signal_H_MO_Forecast', 'Signal_H_OC_Forecast', - 'Signal_D_OC_Forecast'], + 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', + 'Signal_D_Forecast_Upper_Bound', 'Signal_H_BU_Forecast', + 'Signal_D_BU_Forecast', 'Signal_D_AHP_TD_Forecast', + 'Signal_H_AHP_TD_Forecast', 'Signal_D_PHA_TD_Forecast', + 'Signal_H_PHA_TD_Forecast', 'Signal_D_MO_Forecast', + 'Signal_H_MO_Forecast', 'Signal_H_OC_Forecast', 'Signal_D_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_H']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_D']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.0004, 'RMSE': 1.7102113082139578, 'MAE': 0.2638745587709171, 'SMAPE': 0.0004, 'ErrorMean': 2.703739481583334e-16, 'ErrorStdDev': 1.7102113082139578, 'R2': 0.9999420677788421, 'Pearson': 0.999971033469893} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.0001, 'RMSE': 1.1040414730491197, 'MAE': 0.16153230263712237, 'SMAPE': 0.0001, 'ErrorMean': 0.03578065812139025, 'ErrorStdDev': 1.1034615166451753, 'R2': 0.9999923349343914, 'Pearson': 0.9999962609486089} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 2628, 'MAPE': 406332854306.138, 'RMSE': 224.1616467618108, 'MAE': 81.60374259842389, 'SMAPE': 1.9936, 'ErrorMean': -0.3371717372043185, 'ErrorStdDev': 224.16139318398803, 'R2': 0.004724647075293964, 'Pearson': 0.07096646357513453} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 657, 'MAPE': 798649210520.483, 'RMSE': 398.99122412500236, 'MAE': 158.90484994900538, 'SMAPE': 1.9935, 'ErrorMean': 0.8249921550833206, 'ErrorStdDev': 398.99037120801796, 'R2': -0.0010869214100888147, 'Pearson': -0.01863923757928132} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 2628, 'MAPE': 0.0004, 'RMSE': 1.7102113082139578, 'MAE': 0.2638745587709171, 'SMAPE': 0.0004, 'ErrorMean': 2.703739481583334e-16, 'ErrorStdDev': 1.7102113082139578, 'R2': 0.9999420677788421, 'Pearson': 0.999971033469893} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 657, 'MAPE': 0.0001, 'RMSE': 1.1040414730491197, 'MAE': 0.16153230263712237, 'SMAPE': 0.0001, 'ErrorMean': 0.03578065812139025, 'ErrorStdDev': 1.1034615166451753, 'R2': 0.9999923349343914, 'Pearson': 0.9999962609486089} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 2628, 'MAPE': 203166427153.069, 'RMSE': 112.08961052963545, 'MAE': 40.801871299211946, 'SMAPE': 1.9427, 'ErrorMean': -0.16858586860215932, 'ErrorStdDev': 112.08948375066356, 'R2': 0.751142145391539, 'Pearson': 0.9961490862783878} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 657, 'MAPE': 399324605260.2414, 'RMSE': 199.41383227851557, 'MAE': 79.434534645442, 'SMAPE': 1.9437, 'ErrorMean': 0.4303864066023563, 'ErrorStdDev': 199.41336783562164, 'R2': 0.7499334167457341, 'Pearson': 0.9998111669112487} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.0004, 'RMSE': 1.7102113082139578, 'MAE': 0.2638745587709171, 'SMAPE': 0.0004, 'ErrorMean': 2.703739481583334e-16, 'ErrorStdDev': 1.7102113082139578, 'R2': 0.9999420677788421, 'Pearson': 0.999971033469893} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.0001, 'RMSE': 1.1040414730491197, 'MAE': 0.16153230263712237, 'SMAPE': 0.0001, 'ErrorMean': 0.03578065812139025, 'ErrorStdDev': 1.1034615166451753, 'R2': 0.9999923349343914, 'Pearson': 0.9999962609486089} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_AHP_TD_Forecast', 'Length': 2628, 'MAPE': 0.9677, 'RMSE': 45.8794782904742, 'MAE': 40.82881839829507, 'SMAPE': 1.9235, 'ErrorMean': -40.65831290947223, 'ErrorStdDev': 21.256248953226383, 'R2': -4.291933297019057, 'Pearson': 0.0719294876599415} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_AHP_TD_Forecast', 'Length': 657, 'MAPE': 0.9613, 'RMSE': 82.0245774970677, 'MAE': 80.16925183490899, 'SMAPE': 1.9203, 'ErrorMean': -80.07039167801236, 'ErrorStdDev': 17.79785633446215, 'R2': -111.58910849818754, 'Pearson': -0.023746456783873784} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_BU_Forecast', 'Length': 2628, 'MAPE': 0.0667, 'RMSE': 1.359004241502849, 'MAE': 1.0288483172385678, 'SMAPE': 0.043, 'ErrorMean': 1.1274593638202503e-15, 'ErrorStdDev': 1.359004241502849, 'R2': 0.9953567823217537, 'Pearson': 0.9976756899523935} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_BU_Forecast', 'Length': 657, 'MAPE': 0.0119, 'RMSE': 1.2140180766446607, 'MAE': 0.9828321842412838, 'SMAPE': 0.0119, 'ErrorMean': -0.099861635734604, 'ErrorStdDev': 1.209903940041692, 'R2': 0.9753362742238273, 'Pearson': 0.987676902148217} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_MO_Forecast', 'Length': 2628, 'MAPE': 0.9681, 'RMSE': 45.87926485230503, 'MAE': 40.82669657478764, 'SMAPE': 1.9234, 'ErrorMean': -40.581511236427275, 'ErrorStdDev': 21.402053386431845, 'R2': -4.291884059409169, 'Pearson': 0.0719294876599415} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_MO_Forecast', 'Length': 657, 'MAPE': 0.9613, 'RMSE': 82.02332561368509, 'MAE': 80.16281794732217, 'SMAPE': 1.9202, 'ErrorMean': -79.92205198180929, 'ErrorStdDev': 18.446993027200456, 'R2': -111.5856717878089, 'Pearson': -0.023746456783873784} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_OC_Forecast', 'Length': 2628, 'MAPE': 1.0461, 'RMSE': 112.06229017326302, 'MAE': 40.800367743511266, 'SMAPE': 0.7117, 'ErrorMean': 0.16858586860216124, 'ErrorStdDev': 112.06216336338287, 'R2': -30.571586589529606, 'Pearson': 0.1586709386462811} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_OC_Forecast', 'Length': 657, 'MAPE': 0.9634, 'RMSE': 199.62408033661967, 'MAE': 79.57815565528274, 'SMAPE': 0.7105, 'ErrorMean': -0.494467384215572, 'ErrorStdDev': 199.6234679396366, 'R2': -665.8593318771382, 'Pearson': -0.0048146122391852985} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.9681, 'RMSE': 45.87926485230503, 'MAE': 40.82669657478764, 'SMAPE': 1.9234, 'ErrorMean': -40.581511236427275, 'ErrorStdDev': 21.402053386431845, 'R2': -4.291884059409169, 'Pearson': 0.0719294876599415} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.9613, 'RMSE': 82.02332561368509, 'MAE': 80.16281794732217, 'SMAPE': 1.9202, 'ErrorMean': -79.92205198180929, 'ErrorStdDev': 18.446993027200456, 'R2': -111.5856717878089, 'Pearson': -0.023746456783873784} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 63.113966941833496 +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 110, 'MAPE': 0.0106, 'RMSE': 8.359226859017031, 'MAE': 6.304203094999719, 'SMAPE': 0.0106, 'ErrorMean': -1.1110304595521567e-14, 'ErrorStdDev': 8.359226859017031, 'R2': 0.9996663757221723, 'Pearson': 0.9998331739456228} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_AHP_TD_Forecast', 'Length': 27, 'MAPE': 0.002, 'RMSE': 5.446107261690555, 'MAE': 3.930619364169902, 'SMAPE': 0.002, 'ErrorMean': 0.8706626809545284, 'ErrorStdDev': 5.376060900123032, 'R2': 0.9976162758232858, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 110, 'MAPE': 0.9604, 'RMSE': 1072.3434255557725, 'MAE': 978.6668305947719, 'SMAPE': 1.8478, 'ErrorMean': -978.6668305947719, 'ErrorStdDev': 438.3282525987486, 'R2': -4.490263778354461, 'Pearson': 0.9636068058179725} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_BU_Forecast', 'Length': 27, 'MAPE': 0.959, 'RMSE': 1926.3031163648034, 'MAE': 1923.3482919900246, 'SMAPE': 1.8425, 'ErrorMean': -1923.3482919900246, 'ErrorStdDev': 106.65385044998439, 'R2': -297.2173279886651, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 110, 'MAPE': 0.0106, 'RMSE': 8.359226859017031, 'MAE': 6.304203094999719, 'SMAPE': 0.0106, 'ErrorMean': -1.1110304595521567e-14, 'ErrorStdDev': 8.359226859017031, 'R2': 0.9996663757221723, 'Pearson': 0.9998331739456228} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_MO_Forecast', 'Length': 27, 'MAPE': 0.002, 'RMSE': 5.446107261690555, 'MAE': 3.930619364169902, 'SMAPE': 0.002, 'ErrorMean': 0.8706626809545284, 'ErrorStdDev': 5.376060900123032, 'R2': 0.9976162758232858, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 110, 'MAPE': 0.4799, 'RMSE': 536.2143981734155, 'MAE': 489.33341529738595, 'SMAPE': 0.6315, 'ErrorMean': -489.33341529738595, 'ErrorStdDev': 219.2685328127912, 'R2': -0.3727844971567431, 'Pearson': 0.9998284272407535} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_D_OC_Forecast', 'Length': 27, 'MAPE': 0.4793, 'RMSE': 962.7391568409264, 'MAE': 961.238814654535, 'SMAPE': 0.6303, 'ErrorMean': -961.238814654535, 'ErrorStdDev': 53.727323740552436, 'R2': -73.49050045621144, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_D_PHA_TD_Forecast', 'Length': 110, 'MAPE': 0.0106, 'RMSE': 8.359226859017031, 'MAE': 6.304203094999719, 'SMAPE': 0.0106, 'ErrorMean': -1.1110304595521567e-14, 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_Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.1978830433200269 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag26 0.1955567902238025 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag24 -0.17114317607608545 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.16909974358805152 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag5 0.16433556270903457 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag27 -0.14210284275563106 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7994153217889284 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5941504429027744 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.46298265184029963 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.2713488345493048 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag20 0.19788304332001033 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag26 0.1955567902238005 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag24 -0.17114317607609242 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag2 0.16909974358804647 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag5 0.16433556270905791 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_D_ConstantTrend_residue_zeroCycle_residue_Lag27 -0.14210284275562607 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_H'), (1, 'Signal_D')] +INFO:pyaf.std:START_FORECASTING '['Signal_H', 'Signal_D']' RangeIndex: 3650 entries, 0 to 3649 Data columns (total 2 columns): @@ -122,50 +122,46 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 16.51184892654419 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 25.933419704437256 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_D']' 32.6183717250824 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 26.27676510810852 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 32.79709267616272 Int64Index: 4015 entries, 0 to 4014 -Data columns (total 21 columns): +Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 TH_H_start 4015 non-null datetime64[ns] - 1 Signal_H 3650 non-null float64 - 2 Signal_H_Forecast 4015 non-null float64 - 3 Signal_H_Forecast_Lower_Bound 365 non-null float64 - 4 Signal_H_Forecast_Upper_Bound 365 non-null float64 - 5 Date 4015 non-null datetime64[ns] - 6 TH_D_start 168 non-null datetime64[ns] - 7 Signal_D 3651 non-null float64 - 8 Signal_D_Forecast 4015 non-null float64 - 9 Signal_D_Forecast_Lower_Bound 15 non-null float64 - 10 Signal_D_Forecast_Upper_Bound 15 non-null float64 - 11 Signal_H_BU_Forecast 4015 non-null float64 - 12 Signal_D_BU_Forecast 4015 non-null float64 - 13 Signal_D_AHP_TD_Forecast 4015 non-null float64 - 14 Signal_H_AHP_TD_Forecast 4015 non-null float64 - 15 Signal_D_PHA_TD_Forecast 4015 non-null float64 - 16 Signal_H_PHA_TD_Forecast 4015 non-null float64 - 17 Signal_D_MO_Forecast 4015 non-null float64 - 18 Signal_H_MO_Forecast 4015 non-null float64 - 19 Signal_H_OC_Forecast 4015 non-null float64 - 20 Signal_D_OC_Forecast 4015 non-null float64 -dtypes: datetime64[ns](3), float64(18) -memory usage: 850.1 KB - TH_H_start Signal_H ... Signal_H_OC_Forecast Signal_D_OC_Forecast -4010 2001-07-11 02:00:00 NaN ... 55.665774 55.665774 -4011 2001-07-11 03:00:00 NaN ... 47.981542 47.981542 -4012 2001-07-11 04:00:00 NaN ... 47.655460 47.655460 -4013 2001-07-11 05:00:00 NaN ... 47.912823 47.912823 -4014 2001-07-11 06:00:00 NaN ... 48.414964 48.414964 + 1 TH_D_start 168 non-null datetime64[ns] + 2 Signal_H 3650 non-null float64 + 3 Signal_H_Forecast 4015 non-null float64 + 4 Signal_H_Forecast_Lower_Bound 365 non-null float64 + 5 Signal_H_Forecast_Upper_Bound 365 non-null float64 + 6 Signal_D 3651 non-null float64 + 7 Signal_D_Forecast 4015 non-null float64 + 8 Signal_D_Forecast_Lower_Bound 15 non-null float64 + 9 Signal_D_Forecast_Upper_Bound 15 non-null float64 + 10 Signal_H_BU_Forecast 4015 non-null float64 + 11 Signal_D_BU_Forecast 4015 non-null float64 + 12 Signal_D_AHP_TD_Forecast 4015 non-null float64 + 13 Signal_H_AHP_TD_Forecast 4015 non-null float64 + 14 Signal_D_PHA_TD_Forecast 4015 non-null float64 + 15 Signal_H_PHA_TD_Forecast 4015 non-null float64 + 16 Signal_D_MO_Forecast 4015 non-null float64 + 17 Signal_H_MO_Forecast 4015 non-null float64 + 18 Signal_H_OC_Forecast 4015 non-null float64 + 19 Signal_D_OC_Forecast 4015 non-null float64 +dtypes: datetime64[ns](2), float64(18) +memory usage: 818.7 KB + TH_H_start TH_D_start ... Signal_H_OC_Forecast Signal_D_OC_Forecast +4010 2001-07-11 02:00:00 NaT ... 59.869430 59.869430 +4011 2001-07-11 03:00:00 NaT ... 49.809339 49.809339 +4012 2001-07-11 04:00:00 NaT ... 50.356958 50.356958 +4013 2001-07-11 05:00:00 NaT ... 50.822434 50.822434 +4014 2001-07-11 06:00:00 NaT ... 51.413610 51.413610 -[5 rows x 21 columns] +[5 rows x 20 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_10T_30T_H.log b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_10T_30T_H.log index fefeece90..b29cb7a9f 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_10T_30T_H.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_10T_30T_H.log @@ -1,31 +1,18 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.35457801818847656 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.9774734973907471 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'T': 60.0, '10T': 600.0, '30T': 1800.0, 'H': 3600.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA T {'TH_T_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 00:01:00'), 2: Timestamp('2001-01-25 00:02:00'), 3: Timestamp('2001-01-25 00:03:00'), 4: Timestamp('2001-01-25 00:04:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 10T {'TH_10T_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 00:10:00'), 2: Timestamp('2001-01-25 00:20:00'), 3: Timestamp('2001-01-25 00:30:00'), 4: Timestamp('2001-01-25 00:40:00')}, 'Signal': {0: 48.947470506733765, 1: 144.40104048674908, 2: 50.19769810913586, 3: 135.6749263510374, 4: 76.99715598638164}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 30T {'TH_30T_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 00:30:00'), 2: Timestamp('2001-01-25 01:00:00'), 3: Timestamp('2001-01-25 01:30:00'), 4: Timestamp('2001-01-25 02:00:00')}, 'Signal': {0: 243.54620910261872, 1: 352.58369541162125, 2: 327.3611836444439, 3: 355.66628643574467, 4: 402.4664088230068}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA H {'TH_H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 01:00:00'), 2: Timestamp('2001-01-25 02:00:00'), 3: Timestamp('2001-01-25 03:00:00'), 4: Timestamp('2001-01-25 04:00:00')}, 'Signal': {0: 596.1299045142399, 1: 683.0274700801883, 2: 781.0312842834695, 3: 871.6646046032467, 4: 1000.7788136709746}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'T': 360, '10T': 36, '30T': 12, 'H': 6} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_T'), (1, 'Signal_10T'), (2, 'Signal_30T'), (3, 'Signal_H')] +INFO:pyaf.std:START_TRAINING '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' -INFO:pyaf.std:START_TRAINING 'Signal_10T' -INFO:pyaf.std:START_TRAINING 'Signal_T' -INFO:pyaf.std:START_TRAINING 'Signal_30T' -INFO:pyaf.std:START_TRAINING 'Signal_H' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_H' 5.811363935470581 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_30T' 8.974646091461182 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_10T' 12.783576726913452 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_T' 35.05356431007385 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' 76.67015242576599 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_T'), (1, 'Signal_10T'), (2, 'Signal_30T'), (3, 'Signal_H')] -INFO:pyaf.std:START_FORECASTING 'Signal_T' -INFO:pyaf.std:START_FORECASTING 'Signal_10T' -INFO:pyaf.std:START_FORECASTING 'Signal_30T' -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 10.923758506774902 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_30T' 16.660996198654175 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_T' 24.071006059646606 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_10T' 25.07515859603882 +INFO:pyaf.std:START_FORECASTING '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' 63.994211196899414 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -34,13 +21,12 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_T_start', 'Signal_T', 'Signal_T_Forecast', - 'Signal_T_Forecast_Lower_Bound', 'Signal_T_Forecast_Upper_Bound', - 'Date', 'TH_10T_start', 'Signal_10T', 'Signal_10T_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_10T_start', 'TH_T_start', 'TH_H_start', 'TH_30T_start', 'Signal_T', + 'Signal_T_Forecast', 'Signal_T_Forecast_Lower_Bound', + 'Signal_T_Forecast_Upper_Bound', 'Signal_10T', 'Signal_10T_Forecast', 'Signal_10T_Forecast_Lower_Bound', 'Signal_10T_Forecast_Upper_Bound', - 'TH_30T_start', 'Signal_30T', 'Signal_30T_Forecast', - 'Signal_30T_Forecast_Lower_Bound', 'Signal_30T_Forecast_Upper_Bound', - 'TH_H_start', 'Signal_H', 'Signal_H_Forecast', + 'Signal_30T', 'Signal_30T_Forecast', 'Signal_30T_Forecast_Lower_Bound', + 'Signal_30T_Forecast_Upper_Bound', 'Signal_H', 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', 'Signal_T_BU_Forecast', 'Signal_10T_BU_Forecast', 'Signal_30T_BU_Forecast', 'Signal_H_BU_Forecast', @@ -54,54 +40,50 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_T_start', 'Signal_T', 'Signal_ 'Signal_H_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_T']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_T']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_10T']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_10T']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_30T']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_30T']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (3, ['Signal_H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (3, ['Signal_H']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_10T_AHP_TD_Forecast', 'Length': 2592, 'MAPE': 0.0852, 'RMSE': 133.30506740570164, 'MAE': 35.5272625883132, 'SMAPE': 0.1683, 'ErrorMean': -35.119425842619854, 'ErrorStdDev': 128.59575002512088, 'R2': 0.0913907062802427, 'Pearson': 0.39378815920167654} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_10T_AHP_TD_Forecast', 'Length': 648, 'MAPE': 0.084, 'RMSE': 238.22471061640107, 'MAE': 69.13451750497234, 'SMAPE': 0.1673, 'ErrorMean': -68.9856278071565, 'ErrorStdDev': 228.01753420366717, 'R2': 0.06699549215955491, 'Pearson': 0.38173690038942115} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_10T_BU_Forecast', 'Length': 2592, 'MAPE': 376970159463.6079, 'RMSE': 138.69326163426118, 'MAE': 75.62922110093156, 'SMAPE': 1.9635, 'ErrorMean': -0.23518920822800035, 'ErrorStdDev': 138.69306222297476, 'R2': 0.016454218275683496, 'Pearson': 0.12880715759454753} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_10T_BU_Forecast', 'Length': 648, 'MAPE': 741558807957.0298, 'RMSE': 246.65490959929275, 'MAE': 147.47047394805142, 'SMAPE': 1.9642, 'ErrorMean': 0.8412876433367718, 'ErrorStdDev': 246.65347486815676, 'R2': -0.0002064589639845238, 'Pearson': 0.012250718151836933} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_10T_MO_Forecast', 'Length': 2592, 'MAPE': 0.0693, 'RMSE': 119.48449264830099, 'MAE': 28.780729114532473, 'SMAPE': 0.1358, 'ErrorMean': -28.03355269599086, 'ErrorStdDev': 116.1493172888373, 'R2': 0.27002675155459666, 'Pearson': 0.5581388369055992} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_10T_MO_Forecast', 'Length': 648, 'MAPE': 0.0671, 'RMSE': 213.24252239915788, 'MAE': 55.41278298566566, 'SMAPE': 0.1335, 'ErrorMean': -54.747409446006095, 'ErrorStdDev': 206.0948677626561, 'R2': 0.25241987284904466, 'Pearson': 0.5508324716348143} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_10T_OC_Forecast', 'Length': 2592, 'MAPE': 94242539865.9565, 'RMSE': 127.44869368206099, 'MAE': 41.148340311283874, 'SMAPE': 1.8913, 'ErrorMean': 0.2974233229822772, 'ErrorStdDev': 127.44834663749373, 'R2': 0.16947132063229686, 'Pearson': 0.6533557947040763} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_10T_OC_Forecast', 'Length': 648, 'MAPE': 185389701989.3074, 'RMSE': 221.41925272294358, 'MAE': 78.24570515565, 'SMAPE': 1.8919, 'ErrorMean': -1.2338999355319744, 'ErrorStdDev': 221.41581462789838, 'R2': 0.19398915124313887, 'Pearson': 0.6499923974622688} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_10T_PHA_TD_Forecast', 'Length': 2592, 'MAPE': 0.0852, 'RMSE': 133.30329446523584, 'MAE': 35.522313089861754, 'SMAPE': 0.1683, 'ErrorMean': -35.00206955332689, 'ErrorStdDev': 128.62590502021527, 'R2': 0.0914148748959348, 'Pearson': 0.3937881592016765} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_10T_PHA_TD_Forecast', 'Length': 648, 'MAPE': 0.0839, 'RMSE': 238.20839445235504, 'MAE': 69.04440891826938, 'SMAPE': 0.1672, 'ErrorMean': -68.7771729658329, 'ErrorStdDev': 228.06345535047188, 'R2': 0.06712329194361433, 'Pearson': 0.3817369003894212} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_30T_AHP_TD_Forecast', 'Length': 2592, 'MAPE': 0.0172, 'RMSE': 178.16059659808693, 'MAE': 21.48591699739545, 'SMAPE': 0.0338, 'ErrorMean': -21.02486025522383, 'ErrorStdDev': 176.91566756914037, 'R2': 0.49257532208759225, 'Pearson': 0.7068906031306247} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_30T_AHP_TD_Forecast', 'Length': 648, 'MAPE': 0.0172, 'RMSE': 322.0952331328686, 'MAE': 42.471924526761846, 'SMAPE': 0.0342, 'ErrorMean': -42.306105322090026, 'ErrorStdDev': 319.30476454226795, 'R2': 0.46283207561430506, 'Pearson': 0.6871085755146279} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 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_Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag7 0.35979621185098587 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag3 0.2958224776318476 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag14 0.2814817013115565 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.24675178988997726 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag5 0.22496850781044014 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag20 0.1976372786887653 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.15900338968228453 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.1484813810449692 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag2 0.43433354895329385 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.37326379283322786 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag7 0.3597962118509966 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag3 0.29582247763186276 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag14 0.28148170131154887 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.24675178988997376 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag5 0.22496850781043462 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag20 0.1976372786887694 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.15900338968228717 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_30T_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.14848138104496872 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-01-26T18:00:00.000000 TimeDelta= Horizon=6 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_H' Length=60 Min=596.1299045142399 Max=5925.30488293134 Mean=3268.743198541395 StdDev=1556.967817730827 @@ -191,23 +200,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_H_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0104 MAPE_Forecast=0.0031 MAPE_Test=0.0024 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0103 SMAPE_Forecast=0.0031 SMAPE_Test=0.0024 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1724 MASE_Forecast=0.1653 MASE_Test=0.1617 -INFO:pyaf.std:MODEL_L1 L1_Fit=15.473903299568677 L1_Forecast=15.111720742004515 L1_Test=13.8022024041843 -INFO:pyaf.std:MODEL_L2 L2_Fit=19.531461086980915 L2_Forecast=18.36758938901621 L2_Test=15.05125073708809 +INFO:pyaf.std:MODEL_L1 L1_Fit=15.47390329956864 L1_Forecast=15.111720742005012 L1_Test=13.802202404184905 +INFO:pyaf.std:MODEL_L2 L2_Fit=19.531461086980944 L2_Forecast=18.367589389016747 L2_Test=15.051250737088115 INFO:pyaf.std:MODEL_COMPLEXITY 10 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 2505.261932614006 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_H_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9782881141726523 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7554540293942802 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.6218901489718754 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.5345128929199605 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.500003310515122 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag2 0.4603017104939513 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag10 0.44671886866244365 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag6 0.13692698857152596 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.12919611309179238 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag4 0.009037025747387406 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9782881141726479 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7554540293942905 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.6218901489718718 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.5345128929199618 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.5000033105151362 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag2 0.46030171049395696 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag10 0.4467188686624497 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag6 0.1369269885715224 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.12919611309179235 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag4 0.00903702574738574 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_T'), (1, 'Signal_10T'), (2, 'Signal_30T'), (3, 'Signal_H')] +INFO:pyaf.std:START_FORECASTING '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' RangeIndex: 3600 entries, 0 to 3599 Data columns (total 2 columns): @@ -217,74 +235,66 @@ Data columns (total 2 columns): 1 Signal 3600 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 56.4 KB -INFO:pyaf.std:START_FORECASTING 'Signal_T' -INFO:pyaf.std:START_FORECASTING 'Signal_10T' -INFO:pyaf.std:START_FORECASTING 'Signal_30T' -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 12.230888605117798 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_30T' 15.572245121002197 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_10T' 24.541491985321045 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_T' 25.602829694747925 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' 63.43291783332825 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 25.92288088798523 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 63.66168189048767 Int64Index: 3960 entries, 0 to 3959 -Data columns (total 41 columns): +Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_T_start 3960 non-null datetime64[ns] - 1 Signal_T 3600 non-null float64 - 2 Signal_T_Forecast 3960 non-null float64 - 3 Signal_T_Forecast_Lower_Bound 360 non-null float64 - 4 Signal_T_Forecast_Upper_Bound 360 non-null float64 - 5 Date 3960 non-null datetime64[ns] - 6 TH_10T_start 396 non-null datetime64[ns] - 7 Signal_10T 3601 non-null float64 - 8 Signal_10T_Forecast 3960 non-null float64 - 9 Signal_10T_Forecast_Lower_Bound 36 non-null float64 - 10 Signal_10T_Forecast_Upper_Bound 36 non-null float64 - 11 TH_30T_start 132 non-null datetime64[ns] - 12 Signal_30T 3601 non-null float64 - 13 Signal_30T_Forecast 3960 non-null float64 + 0 TH_10T_start 396 non-null datetime64[ns] + 1 TH_T_start 3960 non-null datetime64[ns] + 2 TH_H_start 66 non-null datetime64[ns] + 3 TH_30T_start 132 non-null datetime64[ns] + 4 Signal_T 3600 non-null float64 + 5 Signal_T_Forecast 3960 non-null float64 + 6 Signal_T_Forecast_Lower_Bound 360 non-null float64 + 7 Signal_T_Forecast_Upper_Bound 360 non-null float64 + 8 Signal_10T 360 non-null float64 + 9 Signal_10T_Forecast 396 non-null float64 + 10 Signal_10T_Forecast_Lower_Bound 36 non-null float64 + 11 Signal_10T_Forecast_Upper_Bound 36 non-null float64 + 12 Signal_30T 120 non-null float64 + 13 Signal_30T_Forecast 132 non-null float64 14 Signal_30T_Forecast_Lower_Bound 12 non-null float64 15 Signal_30T_Forecast_Upper_Bound 12 non-null float64 - 16 TH_H_start 66 non-null datetime64[ns] - 17 Signal_H 3601 non-null float64 - 18 Signal_H_Forecast 3960 non-null float64 - 19 Signal_H_Forecast_Lower_Bound 6 non-null float64 - 20 Signal_H_Forecast_Upper_Bound 6 non-null float64 - 21 Signal_T_BU_Forecast 3960 non-null float64 - 22 Signal_10T_BU_Forecast 3960 non-null float64 - 23 Signal_30T_BU_Forecast 3960 non-null float64 - 24 Signal_H_BU_Forecast 3960 non-null float64 - 25 Signal_H_AHP_TD_Forecast 3960 non-null float64 - 26 Signal_30T_AHP_TD_Forecast 3960 non-null float64 - 27 Signal_10T_AHP_TD_Forecast 3960 non-null float64 - 28 Signal_T_AHP_TD_Forecast 3960 non-null float64 - 29 Signal_H_PHA_TD_Forecast 3960 non-null float64 - 30 Signal_30T_PHA_TD_Forecast 3960 non-null float64 - 31 Signal_10T_PHA_TD_Forecast 3960 non-null float64 - 32 Signal_T_PHA_TD_Forecast 3960 non-null float64 - 33 Signal_30T_MO_Forecast 3960 non-null float64 - 34 Signal_10T_MO_Forecast 3960 non-null float64 - 35 Signal_T_MO_Forecast 3960 non-null float64 - 36 Signal_H_MO_Forecast 3960 non-null float64 - 37 Signal_T_OC_Forecast 3960 non-null float64 - 38 Signal_10T_OC_Forecast 3960 non-null float64 - 39 Signal_30T_OC_Forecast 3960 non-null float64 - 40 Signal_H_OC_Forecast 3960 non-null float64 -dtypes: datetime64[ns](5), float64(36) + 16 Signal_H 3601 non-null float64 + 17 Signal_H_Forecast 3960 non-null float64 + 18 Signal_H_Forecast_Lower_Bound 6 non-null float64 + 19 Signal_H_Forecast_Upper_Bound 6 non-null float64 + 20 Signal_T_BU_Forecast 3960 non-null float64 + 21 Signal_10T_BU_Forecast 3960 non-null float64 + 22 Signal_30T_BU_Forecast 396 non-null float64 + 23 Signal_H_BU_Forecast 132 non-null float64 + 24 Signal_H_AHP_TD_Forecast 3960 non-null float64 + 25 Signal_30T_AHP_TD_Forecast 3960 non-null float64 + 26 Signal_10T_AHP_TD_Forecast 3960 non-null float64 + 27 Signal_T_AHP_TD_Forecast 3960 non-null float64 + 28 Signal_H_PHA_TD_Forecast 3960 non-null float64 + 29 Signal_30T_PHA_TD_Forecast 3960 non-null float64 + 30 Signal_10T_PHA_TD_Forecast 3960 non-null float64 + 31 Signal_T_PHA_TD_Forecast 3960 non-null float64 + 32 Signal_30T_MO_Forecast 132 non-null float64 + 33 Signal_10T_MO_Forecast 132 non-null float64 + 34 Signal_T_MO_Forecast 132 non-null float64 + 35 Signal_H_MO_Forecast 132 non-null float64 + 36 Signal_T_OC_Forecast 132 non-null float64 + 37 Signal_10T_OC_Forecast 132 non-null float64 + 38 Signal_30T_OC_Forecast 132 non-null float64 + 39 Signal_H_OC_Forecast 132 non-null float64 +dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_T_start ... Signal_H_OC_Forecast -3955 2001-01-27 17:55:00 ... 24.711832 -3956 2001-01-27 17:56:00 ... 24.956722 -3957 2001-01-27 17:57:00 ... 25.147981 -3958 2001-01-27 17:58:00 ... 25.710048 -3959 2001-01-27 17:59:00 ... 25.889933 + TH_10T_start ... Signal_H_OC_Forecast +3955 NaT ... NaN +3956 NaT ... NaN +3957 NaT ... NaN +3958 NaT ... NaN +3959 NaT ... NaN -[5 rows x 41 columns] +[5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H.log b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H.log index bb6eae040..57c395dbc 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H.log @@ -1,21 +1,16 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3549070358276367 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.928253173828125 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'T': 60.0, 'H': 3600.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA T {'TH_T_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 00:01:00'), 2: Timestamp('2001-01-25 00:02:00'), 3: Timestamp('2001-01-25 00:03:00'), 4: Timestamp('2001-01-25 00:04:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA H {'TH_H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 01:00:00'), 2: Timestamp('2001-01-25 02:00:00'), 3: Timestamp('2001-01-25 03:00:00'), 4: Timestamp('2001-01-25 04:00:00')}, 'Signal': {0: 596.1299045142399, 1: 683.0274700801883, 2: 781.0312842834695, 3: 871.6646046032467, 4: 1000.7788136709746}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'T': 360, 'H': 6} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_T'), (1, 'Signal_H')] +INFO:pyaf.std:START_TRAINING '['Signal_T', 'Signal_H']' -INFO:pyaf.std:START_TRAINING 'Signal_T' -INFO:pyaf.std:START_TRAINING 'Signal_H' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_H' 5.42905330657959 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_T' 34.76533555984497 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_T', 'Signal_H']' 68.19360637664795 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_T'), (1, 'Signal_H')] -INFO:pyaf.std:START_FORECASTING 'Signal_T' -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 11.501038074493408 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_T' 25.52954649925232 +INFO:pyaf.std:START_FORECASTING '['Signal_T', 'Signal_H']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_H']' 49.685157775878906 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -24,41 +19,38 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_T_start', 'Signal_T', 'Signal_T_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_T_start', 'TH_H_start', 'Signal_T', 'Signal_T_Forecast', 'Signal_T_Forecast_Lower_Bound', 'Signal_T_Forecast_Upper_Bound', - 'Date', 'TH_H_start', 'Signal_H', 'Signal_H_Forecast', - 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', - 'Signal_T_BU_Forecast', 'Signal_H_BU_Forecast', - 'Signal_H_AHP_TD_Forecast', 'Signal_T_AHP_TD_Forecast', - 'Signal_H_PHA_TD_Forecast', 'Signal_T_PHA_TD_Forecast', - 'Signal_H_MO_Forecast', 'Signal_T_MO_Forecast', 'Signal_T_OC_Forecast', - 'Signal_H_OC_Forecast'], + 'Signal_H', 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', + 'Signal_H_Forecast_Upper_Bound', 'Signal_T_BU_Forecast', + 'Signal_H_BU_Forecast', 'Signal_H_AHP_TD_Forecast', + 'Signal_T_AHP_TD_Forecast', 'Signal_H_PHA_TD_Forecast', + 'Signal_T_PHA_TD_Forecast', 'Signal_H_MO_Forecast', + 'Signal_T_MO_Forecast', 'Signal_T_OC_Forecast', 'Signal_H_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_T']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_T']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_H']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_AHP_TD_Forecast', 'Length': 2592, 'MAPE': 0.0002, 'RMSE': 2.5843524602879064, 'MAE': 0.26833044247833227, 'SMAPE': 0.0002, 'ErrorMean': -0.01162602817221634, 'ErrorStdDev': 2.5843263095950357, 'R2': 0.9999488889550182, 'Pearson': 0.999974521137833} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_AHP_TD_Forecast', 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'Length': 2592, 'MAPE': 0.0002, 'RMSE': 2.5843524602879064, 'MAE': 0.26833044247833227, 'SMAPE': 0.0002, 'ErrorMean': -0.01162602817221634, 'ErrorStdDev': 2.5843263095950357, 'R2': 0.9999488889550182, 'Pearson': 0.999974521137833} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_MO_Forecast', 'Length': 648, 'MAPE': 0.0, 'RMSE': 2.0797946626696477, 'MAE': 0.21002201101800247, 'SMAPE': 0.0, 'ErrorMean': 0.04424245039532381, 'ErrorStdDev': 2.0793240354625273, 'R2': 0.9999885415884069, 'Pearson': 0.9999944446181755} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_OC_Forecast', 'Length': 2592, 'MAPE': 206016297317.3564, 'RMSE': 180.6642389364268, 'MAE': 41.903980607660124, 'SMAPE': 1.9771, 'ErrorMean': -0.7007211441905012, 'ErrorStdDev': 180.66288002895448, 'R2': 0.750221148061953, 'Pearson': 0.9985186895932006} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_OC_Forecast', 'Length': 648, 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_Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15870170042586296 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.11152387293482002 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag3 0.10163628203696315 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag20 0.10120313733875362 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.08020029139261702 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.0792566262747623 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-01-26T18:00:00.000000 TimeDelta= Horizon=6 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_H' Length=60 Min=596.1299045142399 Max=5925.30488293134 Mean=3268.743198541395 StdDev=1556.967817730827 @@ -96,23 +97,32 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_H_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0104 MAPE_Forecast=0.0031 MAPE_Test=0.0024 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0103 SMAPE_Forecast=0.0031 SMAPE_Test=0.0024 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1724 MASE_Forecast=0.1653 MASE_Test=0.1617 -INFO:pyaf.std:MODEL_L1 L1_Fit=15.473903299568677 L1_Forecast=15.111720742004515 L1_Test=13.8022024041843 -INFO:pyaf.std:MODEL_L2 L2_Fit=19.531461086980915 L2_Forecast=18.36758938901621 L2_Test=15.05125073708809 +INFO:pyaf.std:MODEL_L1 L1_Fit=15.47390329956864 L1_Forecast=15.111720742005012 L1_Test=13.802202404184905 +INFO:pyaf.std:MODEL_L2 L2_Fit=19.531461086980944 L2_Forecast=18.367589389016747 L2_Test=15.051250737088115 INFO:pyaf.std:MODEL_COMPLEXITY 10 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 2505.261932614006 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_H_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9782881141726523 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7554540293942802 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.6218901489718754 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.5345128929199605 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.500003310515122 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag2 0.4603017104939513 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag10 0.44671886866244365 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag6 0.13692698857152596 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.12919611309179238 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag4 0.009037025747387406 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9782881141726479 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7554540293942905 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.6218901489718718 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.5345128929199618 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.5000033105151362 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag2 0.46030171049395696 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag10 0.4467188686624497 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag6 0.1369269885715224 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.12919611309179235 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag4 0.00903702574738574 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_T'), (1, 'Signal_H')] +INFO:pyaf.std:START_FORECASTING '['Signal_T', 'Signal_H']' RangeIndex: 3600 entries, 0 to 3599 Data columns (total 2 columns): @@ -122,50 +132,46 @@ Data columns (total 2 columns): 1 Signal 3600 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 56.4 KB -INFO:pyaf.std:START_FORECASTING 'Signal_T' -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 10.8590087890625 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_T' 23.73881959915161 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_H']' 61.665623903274536 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 23.946741580963135 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 61.80827713012695 Int64Index: 3960 entries, 0 to 3959 -Data columns (total 21 columns): +Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 TH_T_start 3960 non-null datetime64[ns] - 1 Signal_T 3600 non-null float64 - 2 Signal_T_Forecast 3960 non-null float64 - 3 Signal_T_Forecast_Lower_Bound 360 non-null float64 - 4 Signal_T_Forecast_Upper_Bound 360 non-null float64 - 5 Date 3960 non-null datetime64[ns] - 6 TH_H_start 66 non-null datetime64[ns] - 7 Signal_H 3601 non-null float64 - 8 Signal_H_Forecast 3960 non-null float64 - 9 Signal_H_Forecast_Lower_Bound 6 non-null float64 - 10 Signal_H_Forecast_Upper_Bound 6 non-null float64 - 11 Signal_T_BU_Forecast 3960 non-null float64 - 12 Signal_H_BU_Forecast 3960 non-null float64 - 13 Signal_H_AHP_TD_Forecast 3960 non-null float64 - 14 Signal_T_AHP_TD_Forecast 3960 non-null float64 - 15 Signal_H_PHA_TD_Forecast 3960 non-null float64 - 16 Signal_T_PHA_TD_Forecast 3960 non-null float64 - 17 Signal_H_MO_Forecast 3960 non-null float64 - 18 Signal_T_MO_Forecast 3960 non-null float64 - 19 Signal_T_OC_Forecast 3960 non-null float64 - 20 Signal_H_OC_Forecast 3960 non-null float64 -dtypes: datetime64[ns](3), float64(18) -memory usage: 840.6 KB - TH_T_start Signal_T ... Signal_T_OC_Forecast Signal_H_OC_Forecast -3955 2001-01-27 17:55:00 NaN ... 49.423665 49.423665 -3956 2001-01-27 17:56:00 NaN ... 49.913443 49.913443 -3957 2001-01-27 17:57:00 NaN ... 50.295962 50.295962 -3958 2001-01-27 17:58:00 NaN ... 51.420095 51.420095 -3959 2001-01-27 17:59:00 NaN ... 51.779867 51.779867 + 1 TH_H_start 66 non-null datetime64[ns] + 2 Signal_T 3600 non-null float64 + 3 Signal_T_Forecast 3960 non-null float64 + 4 Signal_T_Forecast_Lower_Bound 360 non-null float64 + 5 Signal_T_Forecast_Upper_Bound 360 non-null float64 + 6 Signal_H 3601 non-null float64 + 7 Signal_H_Forecast 3960 non-null float64 + 8 Signal_H_Forecast_Lower_Bound 6 non-null float64 + 9 Signal_H_Forecast_Upper_Bound 6 non-null float64 + 10 Signal_T_BU_Forecast 3960 non-null float64 + 11 Signal_H_BU_Forecast 3960 non-null float64 + 12 Signal_H_AHP_TD_Forecast 3960 non-null float64 + 13 Signal_T_AHP_TD_Forecast 3960 non-null float64 + 14 Signal_H_PHA_TD_Forecast 3960 non-null float64 + 15 Signal_T_PHA_TD_Forecast 3960 non-null float64 + 16 Signal_H_MO_Forecast 3960 non-null float64 + 17 Signal_T_MO_Forecast 3960 non-null float64 + 18 Signal_T_OC_Forecast 3960 non-null float64 + 19 Signal_H_OC_Forecast 3960 non-null float64 +dtypes: datetime64[ns](2), float64(18) +memory usage: 809.7 KB + TH_T_start TH_H_start ... Signal_T_OC_Forecast Signal_H_OC_Forecast +3955 2001-01-27 17:55:00 NaT ... 49.423665 49.423665 +3956 2001-01-27 17:56:00 NaT ... 49.913443 49.913443 +3957 2001-01-27 17:57:00 NaT ... 50.295962 50.295962 +3958 2001-01-27 17:58:00 NaT ... 51.420095 51.420095 +3959 2001-01-27 17:59:00 NaT ... 51.779867 51.779867 -[5 rows x 21 columns] +[5 rows x 20 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H_12H_D.log b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H_12H_D.log index 48e87f4a9..135829c0f 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H_12H_D.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H_12H_D.log @@ -1,31 +1,18 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.38019347190856934 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.3878440856933594 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'T': 60.0, 'H': 3600.0, '12H': 43200.0, 'D': 86400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA T {'TH_T_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 00:01:00'), 2: Timestamp('2001-01-25 00:02:00'), 3: Timestamp('2001-01-25 00:03:00'), 4: Timestamp('2001-01-25 00:04:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA H {'TH_H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 01:00:00'), 2: Timestamp('2001-01-25 02:00:00'), 3: Timestamp('2001-01-25 03:00:00'), 4: Timestamp('2001-01-25 04:00:00')}, 'Signal': {0: 596.1299045142399, 1: 683.0274700801883, 2: 781.0312842834695, 3: 871.6646046032467, 4: 1000.7788136709746}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 12H {'TH_12H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 12:00:00'), 2: Timestamp('2001-01-26 00:00:00'), 3: Timestamp('2001-01-26 12:00:00'), 4: Timestamp('2001-01-27 00:00:00')}, 'Signal': {0: 13314.870043592704, 1: 26252.150828662037, 2: 39302.587804702096, 3: 52178.24097795129, 4: 65076.74225757554}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00')}, 'Signal': {0: 39567.020872254725, 1: 91480.82878265351, 2: 65076.74225757554}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'T': 360, 'H': 6, '12H': 1, 'D': 1} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_T'), (1, 'Signal_H'), (2, 'Signal_12H'), (3, 'Signal_D')] +INFO:pyaf.std:START_TRAINING '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' -INFO:pyaf.std:START_TRAINING 'Signal_H' -INFO:pyaf.std:START_TRAINING 'Signal_T' -INFO:pyaf.std:START_TRAINING 'Signal_D' -INFO:pyaf.std:START_TRAINING 'Signal_12H' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_12H' 2.7605769634246826 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_D' 2.875441789627075 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_H' 5.604475021362305 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_T' 38.023356914520264 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' 68.79878973960876 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_T'), (1, 'Signal_H'), (2, 'Signal_12H'), (3, 'Signal_D')] -INFO:pyaf.std:START_FORECASTING 'Signal_T' -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:START_FORECASTING 'Signal_12H' -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 6.611521244049072 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_12H' 7.593016624450684 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 9.115712642669678 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_T' 20.992531299591064 +INFO:pyaf.std:START_FORECASTING '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' 50.156755208969116 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -34,13 +21,12 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_T_start', 'Signal_T', 'Signal_T_Forecast', - 'Signal_T_Forecast_Lower_Bound', 'Signal_T_Forecast_Upper_Bound', - 'Date', 'TH_H_start', 'Signal_H', 'Signal_H_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_12H_start', 'TH_D_start', 'TH_T_start', 'TH_H_start', 'Signal_T', + 'Signal_T_Forecast', 'Signal_T_Forecast_Lower_Bound', + 'Signal_T_Forecast_Upper_Bound', 'Signal_H', 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', - 'TH_12H_start', 'Signal_12H', 'Signal_12H_Forecast', - 'Signal_12H_Forecast_Lower_Bound', 'Signal_12H_Forecast_Upper_Bound', - 'TH_D_start', 'Signal_D', 'Signal_D_Forecast', + 'Signal_12H', 'Signal_12H_Forecast', 'Signal_12H_Forecast_Lower_Bound', + 'Signal_12H_Forecast_Upper_Bound', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', 'Signal_T_BU_Forecast', 'Signal_H_BU_Forecast', 'Signal_12H_BU_Forecast', 'Signal_D_BU_Forecast', @@ -54,54 +40,50 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_T_start', 'Signal_T', 'Signal_ 'Signal_D_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_T']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_T']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_H']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_12H']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_12H']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (3, ['Signal_D']) -INFO:pyaf.hierarchical:MODEL_LEVEL (3, ['Signal_D']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_AHP_TD_Forecast', 'Length': 2592, 'MAPE': 0.0011, 'RMSE': 1202.5883361532553, 'MAE': 39.02807472269416, 'SMAPE': 0.0019, 'ErrorMean': -35.093333127948036, 'ErrorStdDev': 1202.0761890253987, 'R2': 0.2688653486993873, 'Pearson': 0.5379011828365643} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_AHP_TD_Forecast', 'Length': 648, 'MAPE': 0.001, 'RMSE': 1577.1532517504206, 'MAE': 61.956431071285365, 'SMAPE': 0.0014, 'ErrorMean': -61.956431071285365, 'ErrorStdDev': 1575.9358426521794, 'R2': 0.6188096353443449, 'Pearson': 0.999999999999998} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_BU_Forecast', 'Length': 2592, 'MAPE': 398091453923.2575, 'RMSE': 1353.8996115397288, 'MAE': 86.90405806474297, 'SMAPE': 0.0336, 'ErrorMean': -7.285767280091733, 'ErrorStdDev': 1353.8800078746153, 'R2': 0.07330606665674688, 'Pearson': 0.2711755332107838} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_BU_Forecast', 'Length': 648, 'MAPE': 692028649627.8706, 'RMSE': 2433.565138536411, 'MAE': 161.96919057932445, 'SMAPE': 0.0304, 'ErrorMean': 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0.8636448184137244} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_OC_Forecast', 'Length': 648, 'MAPE': 378417780645.9126, 'RMSE': 1237.969508053951, 'MAE': 86.11139810584152, 'SMAPE': 1.9979, 'ErrorMean': -10.427841976659161, 'ErrorStdDev': 1237.9255886292403, 'R2': 0.765137316481032, 'Pearson': 0.9939838871577188} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12H_PHA_TD_Forecast', 'Length': 2592, 'MAPE': 0.0012, 'RMSE': 1186.3447768268545, 'MAE': 38.644478910816325, 'SMAPE': 0.0019, 'ErrorMean': -30.372672874225714, 'ErrorStdDev': 1185.9559141244815, 'R2': 0.2884830709660344, 'Pearson': 0.5379011828365643} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12H_PHA_TD_Forecast', 'Length': 648, 'MAPE': 0.0008, 'RMSE': 1278.2279374405796, 'MAE': 50.213535692573764, 'SMAPE': 0.001, 'ErrorMean': -50.213535692573764, 'ErrorStdDev': 1277.2412696459694, 'R2': 0.7496136012364756, 'Pearson': 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Mean=54.47905330902324 StdDev=26.666240075001483 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_T' Min=-0.6219775856788415 Max=110.89054328721227 Mean=54.47905330902324 StdDev=26.666240075001483 @@ -113,20 +95,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_T_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0676 MAPE_Forecast=0.0121 MAPE_Test=0.0106 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0434 SMAPE_Forecast=0.0121 SMAPE_Test=0.0106 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4465 MASE_Forecast=0.422 MASE_Test=0.4333 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.0312409416576525 L1_Forecast=0.9877210458877513 L1_Test=1.0022630122794538 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.3610663224935071 L2_Forecast=1.2266096711100578 L2_Test=1.2722982191717307 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.0312409416576527 L1_Forecast=0.987721045887753 L1_Test=1.0022630122794585 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.3610663224935073 L2_Forecast=1.2266096711100611 L2_Test=1.2722982191717376 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 41.89462645967267 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_T_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag21 0.39823982557236004 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag42 0.327115937754902 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag1 0.2491442711708512 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag21 0.3982398255723595 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag42 0.327115937754903 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag1 0.24914427117084909 INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag63 0.2441478386494532 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1587017004258638 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.11152387293481955 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag3 0.10163628203696211 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag20 0.10120313733875462 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.08020029139261758 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.07925662627476189 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15870170042586296 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.11152387293482002 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag3 0.10163628203696315 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag20 0.10120313733875362 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.08020029139261702 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_T_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.0792566262747623 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-01-26T18:00:00.000000 TimeDelta= Horizon=6 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_H' Length=60 Min=596.1299045142399 Max=5925.30488293134 Mean=3268.743198541395 StdDev=1556.967817730827 @@ -139,20 +130,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_H_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0104 MAPE_Forecast=0.0031 MAPE_Test=0.0024 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0103 SMAPE_Forecast=0.0031 SMAPE_Test=0.0024 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1724 MASE_Forecast=0.1653 MASE_Test=0.1617 -INFO:pyaf.std:MODEL_L1 L1_Fit=15.473903299568677 L1_Forecast=15.111720742004515 L1_Test=13.8022024041843 -INFO:pyaf.std:MODEL_L2 L2_Fit=19.531461086980915 L2_Forecast=18.36758938901621 L2_Test=15.05125073708809 +INFO:pyaf.std:MODEL_L1 L1_Fit=15.47390329956864 L1_Forecast=15.111720742005012 L1_Test=13.802202404184905 +INFO:pyaf.std:MODEL_L2 L2_Fit=19.531461086980944 L2_Forecast=18.367589389016747 L2_Test=15.051250737088115 INFO:pyaf.std:MODEL_COMPLEXITY 10 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START 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_Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag10 0.44671886866244365 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag6 0.13692698857152596 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.12919611309179238 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag4 0.009037025747387406 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag1 0.9782881141726479 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag7 0.7554540293942905 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.6218901489718718 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.5345128929199618 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag9 -0.5000033105151362 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag2 0.46030171049395696 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag10 0.4467188686624497 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag6 0.1369269885715224 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.12919611309179235 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_H_ConstantTrend_residue_zeroCycle_residue_Lag4 0.00903702574738574 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_12H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-01-27T00:00:00.000000 TimeDelta= Horizon=1 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_12H' Length=5 Min=13314.870043592704 Max=65076.74225757554 Mean=39224.91838249673 StdDev=18307.018869271556 @@ -168,6 +168,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2078 MASE_Forecast=0.2078 MASE_Test=0.2078 INFO:pyaf.std:MODEL_L1 L1_Fit=2688.6628055592514 L1_Forecast=2688.6628055592514 L1_Test=2688.6628055592514 INFO:pyaf.std:MODEL_L2 L2_Fit=4045.5996888950554 L2_Forecast=4045.5996888950554 L2_Test=4045.5996888950554 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 13314.870043592704 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (8031.658717051163, array([3174.66512964, 1411.60963708, 652.89455046])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_Signal_12H_PolyTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-01-27T00:00:00.000000 TimeDelta= Horizon=1 @@ -184,10 +193,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3695 MASE_Forecast=0.3695 MASE_Test=0.3695 INFO:pyaf.std:MODEL_L1 L1_Fit=14470.189149541728 L1_Forecast=14470.189149541728 L1_Test=14470.189149541728 INFO:pyaf.std:MODEL_L2 L2_Fit=20743.083792899262 L2_Forecast=20743.083792899262 L2_Test=20743.083792899262 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 39567.020872254725 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 8503.240461773605 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_Signal_D_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_T'), (1, 'Signal_H'), (2, 'Signal_12H'), (3, 'Signal_D')] +INFO:pyaf.std:START_FORECASTING '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' RangeIndex: 3600 entries, 0 to 3599 Data columns (total 2 columns): @@ -197,74 +215,66 @@ Data columns (total 2 columns): 1 Signal 3600 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 56.4 KB -INFO:pyaf.std:START_FORECASTING 'Signal_T' -INFO:pyaf.std:START_FORECASTING 'Signal_H' -INFO:pyaf.std:START_FORECASTING 'Signal_12H' -INFO:pyaf.std:START_FORECASTING 'Signal_D' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_12H' 10.897340059280396 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_D' 10.793879270553589 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_H' 13.585105419158936 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_T' 25.44849705696106 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' 53.76600742340088 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 25.876385927200317 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 53.92520570755005 Int64Index: 3960 entries, 0 to 3959 -Data columns (total 41 columns): +Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_T_start 3960 non-null datetime64[ns] - 1 Signal_T 3600 non-null float64 - 2 Signal_T_Forecast 3960 non-null float64 - 3 Signal_T_Forecast_Lower_Bound 360 non-null float64 - 4 Signal_T_Forecast_Upper_Bound 360 non-null float64 - 5 Date 3960 non-null datetime64[ns] - 6 TH_H_start 66 non-null datetime64[ns] - 7 Signal_H 3601 non-null float64 - 8 Signal_H_Forecast 3960 non-null float64 - 9 Signal_H_Forecast_Lower_Bound 6 non-null float64 - 10 Signal_H_Forecast_Upper_Bound 6 non-null float64 - 11 TH_12H_start 6 non-null datetime64[ns] - 12 Signal_12H 3601 non-null float64 - 13 Signal_12H_Forecast 3960 non-null float64 + 0 TH_12H_start 6 non-null datetime64[ns] + 1 TH_D_start 3 non-null datetime64[ns] + 2 TH_T_start 3960 non-null datetime64[ns] + 3 TH_H_start 66 non-null datetime64[ns] + 4 Signal_T 3600 non-null float64 + 5 Signal_T_Forecast 3960 non-null float64 + 6 Signal_T_Forecast_Lower_Bound 360 non-null float64 + 7 Signal_T_Forecast_Upper_Bound 360 non-null float64 + 8 Signal_H 60 non-null float64 + 9 Signal_H_Forecast 66 non-null float64 + 10 Signal_H_Forecast_Lower_Bound 6 non-null float64 + 11 Signal_H_Forecast_Upper_Bound 6 non-null float64 + 12 Signal_12H 5 non-null float64 + 13 Signal_12H_Forecast 6 non-null float64 14 Signal_12H_Forecast_Lower_Bound 1 non-null float64 15 Signal_12H_Forecast_Upper_Bound 1 non-null float64 - 16 TH_D_start 3 non-null datetime64[ns] - 17 Signal_D 3601 non-null float64 - 18 Signal_D_Forecast 3960 non-null float64 - 19 Signal_D_Forecast_Lower_Bound 0 non-null float64 - 20 Signal_D_Forecast_Upper_Bound 0 non-null float64 - 21 Signal_T_BU_Forecast 3960 non-null float64 - 22 Signal_H_BU_Forecast 3960 non-null float64 - 23 Signal_12H_BU_Forecast 3960 non-null float64 - 24 Signal_D_BU_Forecast 3960 non-null float64 - 25 Signal_D_AHP_TD_Forecast 3960 non-null float64 - 26 Signal_12H_AHP_TD_Forecast 3960 non-null float64 - 27 Signal_H_AHP_TD_Forecast 3960 non-null float64 - 28 Signal_T_AHP_TD_Forecast 3960 non-null float64 - 29 Signal_D_PHA_TD_Forecast 3960 non-null float64 - 30 Signal_12H_PHA_TD_Forecast 3960 non-null float64 - 31 Signal_H_PHA_TD_Forecast 3960 non-null float64 - 32 Signal_T_PHA_TD_Forecast 3960 non-null float64 - 33 Signal_12H_MO_Forecast 3960 non-null float64 - 34 Signal_H_MO_Forecast 3960 non-null float64 - 35 Signal_T_MO_Forecast 3960 non-null float64 - 36 Signal_D_MO_Forecast 3960 non-null float64 - 37 Signal_T_OC_Forecast 3960 non-null float64 - 38 Signal_H_OC_Forecast 3960 non-null float64 - 39 Signal_12H_OC_Forecast 3960 non-null float64 - 40 Signal_D_OC_Forecast 3960 non-null float64 -dtypes: datetime64[ns](5), float64(36) + 16 Signal_D 3601 non-null float64 + 17 Signal_D_Forecast 3960 non-null float64 + 18 Signal_D_Forecast_Lower_Bound 0 non-null float64 + 19 Signal_D_Forecast_Upper_Bound 0 non-null float64 + 20 Signal_T_BU_Forecast 3960 non-null float64 + 21 Signal_H_BU_Forecast 3960 non-null float64 + 22 Signal_12H_BU_Forecast 66 non-null float64 + 23 Signal_D_BU_Forecast 6 non-null float64 + 24 Signal_D_AHP_TD_Forecast 3960 non-null float64 + 25 Signal_12H_AHP_TD_Forecast 3960 non-null float64 + 26 Signal_H_AHP_TD_Forecast 3960 non-null float64 + 27 Signal_T_AHP_TD_Forecast 3960 non-null float64 + 28 Signal_D_PHA_TD_Forecast 3960 non-null float64 + 29 Signal_12H_PHA_TD_Forecast 3960 non-null float64 + 30 Signal_H_PHA_TD_Forecast 3960 non-null float64 + 31 Signal_T_PHA_TD_Forecast 3960 non-null float64 + 32 Signal_12H_MO_Forecast 6 non-null float64 + 33 Signal_H_MO_Forecast 6 non-null float64 + 34 Signal_T_MO_Forecast 6 non-null float64 + 35 Signal_D_MO_Forecast 6 non-null float64 + 36 Signal_T_OC_Forecast 6 non-null float64 + 37 Signal_H_OC_Forecast 6 non-null float64 + 38 Signal_12H_OC_Forecast 6 non-null float64 + 39 Signal_D_OC_Forecast 6 non-null float64 +dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_T_start ... Signal_D_OC_Forecast -3955 2001-01-27 17:55:00 ... 24.711832 -3956 2001-01-27 17:56:00 ... 24.956722 -3957 2001-01-27 17:57:00 ... 25.147981 -3958 2001-01-27 17:58:00 ... 25.710048 -3959 2001-01-27 17:59:00 ... 25.889933 + TH_12H_start TH_D_start ... Signal_12H_OC_Forecast Signal_D_OC_Forecast +3955 NaT NaT ... NaN NaN +3956 NaT NaT ... NaN NaN +3957 NaT NaT ... NaN NaN +3958 NaT NaT ... NaN NaN +3959 NaT NaT ... NaN NaN -[5 rows x 41 columns] +[5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M.log b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M.log index 35718f67e..8bc42aa80 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M.log @@ -1,26 +1,17 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3742516040802002 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.6983253955841064 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'M': 2419200.0, '2M': 5097600.0, '6M': 15638400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA M {'TH_M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-02-28 00:00:00'), 2: Timestamp('2001-03-31 00:00:00'), 3: Timestamp('2001-04-30 00:00:00'), 4: Timestamp('2001-05-31 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 2M {'TH_2M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-03-31 00:00:00'), 2: Timestamp('2001-05-31 00:00:00'), 3: Timestamp('2001-07-31 00:00:00'), 4: Timestamp('2001-09-30 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: 0.2559389701067345, 2: 7.426331735672635, 3: 13.535884056849122, 4: 16.03047951939704}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 6M {'TH_6M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-07-31 00:00:00'), 2: Timestamp('2002-01-31 00:00:00'), 3: Timestamp('2002-07-31 00:00:00'), 4: Timestamp('2003-01-31 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: 21.21815476262849, 2: 56.62764847921909, 3: 94.73224181234255, 4: 42.68897101726309}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'M': 36, '2M': 17, '6M': 5} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_M'), (1, 'Signal_2M'), (2, 'Signal_6M')] +INFO:pyaf.std:START_TRAINING '['Signal_M', 'Signal_2M', 'Signal_6M']' -INFO:pyaf.std:START_TRAINING 'Signal_2M' -INFO:pyaf.std:START_TRAINING 'Signal_M' -INFO:pyaf.std:START_TRAINING 'Signal_6M' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_6M' 6.991122722625732 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_2M' 9.509503602981567 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_M' 12.9471595287323 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M']' 31.488470554351807 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_M'), (1, 'Signal_2M'), (2, 'Signal_6M')] -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_2M' -INFO:pyaf.std:START_FORECASTING 'Signal_6M' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_6M' 1.1742703914642334 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2M' 1.5672969818115234 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 2.037860155105591 +INFO:pyaf.std:START_FORECASTING '['Signal_M', 'Signal_2M', 'Signal_6M']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M']' 8.709264278411865 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -29,57 +20,54 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'Signal_M', 'Signal_M_Forecast', - 'Signal_M_Forecast_Lower_Bound', 'Signal_M_Forecast_Upper_Bound', - 'Date', 'TH_2M_start', 'Signal_2M', 'Signal_2M_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'TH_6M_start', 'TH_2M_start', 'Signal_M', + 'Signal_M_Forecast', 'Signal_M_Forecast_Lower_Bound', + 'Signal_M_Forecast_Upper_Bound', 'Signal_2M', 'Signal_2M_Forecast', 'Signal_2M_Forecast_Lower_Bound', 'Signal_2M_Forecast_Upper_Bound', - 'TH_6M_start', 'Signal_6M', 'Signal_6M_Forecast', - 'Signal_6M_Forecast_Lower_Bound', 'Signal_6M_Forecast_Upper_Bound', - 'Signal_M_BU_Forecast', 'Signal_2M_BU_Forecast', - 'Signal_6M_BU_Forecast', 'Signal_6M_AHP_TD_Forecast', - 'Signal_2M_AHP_TD_Forecast', 'Signal_M_AHP_TD_Forecast', - 'Signal_6M_PHA_TD_Forecast', 'Signal_2M_PHA_TD_Forecast', - 'Signal_M_PHA_TD_Forecast', 'Signal_2M_MO_Forecast', - 'Signal_M_MO_Forecast', 'Signal_6M_MO_Forecast', 'Signal_M_OC_Forecast', + 'Signal_6M', 'Signal_6M_Forecast', 'Signal_6M_Forecast_Lower_Bound', + 'Signal_6M_Forecast_Upper_Bound', 'Signal_M_BU_Forecast', + 'Signal_2M_BU_Forecast', 'Signal_6M_BU_Forecast', + 'Signal_6M_AHP_TD_Forecast', 'Signal_2M_AHP_TD_Forecast', + 'Signal_M_AHP_TD_Forecast', 'Signal_6M_PHA_TD_Forecast', + 'Signal_2M_PHA_TD_Forecast', 'Signal_M_PHA_TD_Forecast', + 'Signal_2M_MO_Forecast', 'Signal_M_MO_Forecast', + 'Signal_6M_MO_Forecast', 'Signal_M_OC_Forecast', 'Signal_2M_OC_Forecast', 'Signal_6M_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_M']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_2M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_2M']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_6M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_6M']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.414, 'RMSE': 16.45235887837465, 'MAE': 9.210187434886754, 'SMAPE': 0.7113, 'ErrorMean': -7.889094952440082, 'ErrorStdDev': 14.43753072703977, 'R2': -0.1791158858964157, 'Pearson': 0.4262990351221317} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.3669, 'RMSE': 20.88370998146209, 'MAE': 12.185471011280077, 'SMAPE': 0.7008, 'ErrorMean': -10.854121589837, 'ErrorStdDev': 17.841451373216636, 'R2': -0.2774224913725094, 'Pearson': 0.4226135632263022} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_BU_Forecast', 'Length': 259, 'MAPE': 62422770984.7541, 'RMSE': 14.05234794373008, 'MAE': 12.554649574102259, 'SMAPE': 1.3407, 'ErrorMean': 3.017749070795792e-16, 'ErrorStdDev': 14.05234794373008, 'R2': 0.13980298492491605, 'Pearson': 0.3755152988386296} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_BU_Forecast', 'Length': 65, 'MAPE': 82574882602.5213, 'RMSE': 17.83909402922174, 'MAE': 16.872359020076644, 'SMAPE': 1.365, 'ErrorMean': -0.3573824996237475, 'ErrorStdDev': 17.83551382866155, 'R2': 0.06789499285945555, 'Pearson': 0.26647359469881626} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_MO_Forecast', 'Length': 259, 'MAPE': 0.4474, 'RMSE': 2.7035674905310647, 'MAE': 1.1671933591504389, 'SMAPE': 0.0721, 'ErrorMean': -6.515594584672733e-16, 'ErrorStdDev': 2.7035674905310647, 'R2': 0.9681598890007562, 'Pearson': 0.9839512308415209} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_MO_Forecast', 'Length': 65, 'MAPE': 0.0242, 'RMSE': 1.3221466137669615, 'MAE': 0.7505668794770102, 'SMAPE': 0.0241, 'ErrorMean': -0.04922265535936422, 'ErrorStdDev': 1.321230032392095, 'R2': 0.9948798994987323, 'Pearson': 0.9974402218200018} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_OC_Forecast', 'Length': 259, 'MAPE': 20807590328.5778, 'RMSE': 10.30874486428773, 'MAE': 8.257354551223123, 'SMAPE': 1.2913, 'ErrorMean': -9.739099273931875e-16, 'ErrorStdDev': 10.30874486428773, 'R2': 0.5370738390595151, 'Pearson': 0.7446704671577259} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_OC_Forecast', 'Length': 65, 'MAPE': 27524960867.6746, 'RMSE': 13.100887476769936, 'MAE': 11.099503573320813, 'SMAPE': 1.3084, 'ErrorMean': -0.46809051314523026, 'ErrorStdDev': 13.092522444146917, 'R2': 0.4972863418507413, 'Pearson': 0.7253372872579017} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_PHA_TD_Forecast', 'Length': 259, 'MAPE': 0.4096, 'RMSE': 16.414353045698554, 'MAE': 9.2530716932096, 'SMAPE': 0.7143, 'ErrorMean': -8.2803248747435, 'ErrorStdDev': 14.172762817373947, 'R2': -0.1736745360142622, 'Pearson': 0.42629903512213163} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_PHA_TD_Forecast', 'Length': 65, 'MAPE': 0.3626, 'RMSE': 20.828858222318555, 'MAE': 12.08617804712387, 'SMAPE': 0.6988, 'ErrorMean': -11.342915326943302, 'ErrorStdDev': 17.469390565536113, 'R2': -0.27072091854152425, 'Pearson': 0.4226135632263022} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6M_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.0979, 'RMSE': 5.326628092740373, 'MAE': 1.3882898709580629, 'SMAPE': 0.0275, 'ErrorMean': -1.9203857723245952e-16, 'ErrorStdDev': 5.326628092740373, 'R2': 0.968951630509972, 'Pearson': 0.9843534720025043} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6M_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.0075, 'RMSE': 2.303758644524386, 'MAE': 0.724402621470224, 'SMAPE': 0.0076, 'ErrorMean': -0.16673053118507364, 'ErrorStdDev': 2.297717306848599, 'R2': 0.9963649342442041, 'Pearson': 0.9982385734463485} 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'RMSE': 19.711528061074375, 'MAE': 11.42575782328684, 'SMAPE': 0.3712, 'ErrorMean': 10.166874332625964, 'ErrorStdDev': 16.88724384874909, 'R2': -11.58848414685206, 'Pearson': 0.2775985303863618} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 130, 'MAPE': 1.0067, 'RMSE': 12.631486410964593, 'MAE': 10.343080307790597, 'SMAPE': 1.548, 'ErrorMean': -8.36307890719707, 'ErrorStdDev': 9.466433337977861, 'R2': -2.696275236380923, 'Pearson': 0.03909983703188895} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 32, 'MAPE': 0.7965, 'RMSE': 15.447921944904252, 'MAE': 13.068545896490715, 'SMAPE': 1.4482, 'ErrorMean': -11.712612505612515, 'ErrorStdDev': 10.072388034087234, 'R2': -6.731664222638842, 'Pearson': -0.013815763862450939} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 41.66358995437622 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-07-31T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_M' Length=360 Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_M' Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 @@ -91,20 +79,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_M_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3608 MAPE_Forecast=0.0933 MAPE_Test=0.0727 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1559 SMAPE_Forecast=0.1004 SMAPE_Test=0.0754 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5456 MASE_Forecast=0.6072 MASE_Test=0.5062 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.2707655069523887 L1_Forecast=1.3119893773007625 L1_Test=1.269372776026798 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.7464635036586067 L2_Test=1.4940617790422326 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.270765506952389 L1_Forecast=1.3119893773007616 L1_Test=1.269372776026798 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.746463503658606 L2_Test=1.4940617790422324 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12.516770159495985 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_M_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529058 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969627 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630132 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067375 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684192 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395425 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1522696318163203 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070928 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag56 0.14777440880242368 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529054 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969634 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813113 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630128 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067456 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684117 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395358 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.15226963181632097 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070792 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag56 0.1477744088024236 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_2M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-07-31T00:00:00.000000 TimeDelta= Horizon=17 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_2M' Length=180 Min=-0.41263817098822364 Max=54.50574426645973 Mean=27.79443879544697 StdDev=12.710746738178202 @@ -117,49 +114,67 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_2M_PolyTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.8914 MAPE_Forecast=0.0503 MAPE_Test=0.0423 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1437 SMAPE_Forecast=0.0501 SMAPE_Test=0.0417 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3351 MASE_Forecast=0.2419 MASE_Test=0.1923 -INFO:pyaf.std:MODEL_L1 L1_Fit=2.3254083078458745 L1_Forecast=1.5648871681954617 L1_Test=1.5010490628282729 -INFO:pyaf.std:MODEL_L2 L2_Fit=3.816061994080786 L2_Forecast=1.9209564028146704 L2_Test=1.9619396247933936 +INFO:pyaf.std:MODEL_L1 L1_Fit=2.3254083078458754 L1_Forecast=1.5648871681954581 L1_Test=1.5010490628282671 +INFO:pyaf.std:MODEL_L2 L2_Fit=3.8160619940807856 L2_Forecast=1.920956402814668 L2_Test=1.9619396247933885 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (18.699624361314886, array([11.35389737, 2.54733243, -1.1543063 ])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_2M_PolyTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag21 0.625735294705792 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag1 0.5645747604726261 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag22 -0.46173321401484885 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag10 0.3947633996074761 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag12 -0.18994759552340598 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag31 -0.1811162830569539 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag20 -0.14938498763315758 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag42 0.13856514460582142 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag43 -0.10901605686385624 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag7 -0.10774018536071794 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag21 0.6257352947057914 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag1 0.5645747604726263 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag22 -0.46173321401484846 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag10 0.3947633996074763 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag12 -0.18994759552340576 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag31 -0.18111628305695376 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag20 -0.14938498763315713 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag42 0.13856514460582156 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag43 -0.1090160568638561 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag7 -0.10774018536071775 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_6M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-07-31T00:00:00.000000 TimeDelta= Horizon=5 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_6M' Length=60 Min=1.9048968112034605 Max=140.39468221087236 Mean=82.18464232125778 StdDev=31.75370912609833 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_6M' Min=1.9048968112034605 Max=140.39468221087236 Mean=82.18464232125778 StdDev=31.75370912609833 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_6M_LinearTrend_residue_zeroCycle_residue_AR(15)' [LinearTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Signal_6M_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_6M_LinearTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_6M_LinearTrend_residue_zeroCycle_residue_AR(15)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.5762 MAPE_Forecast=0.0498 MAPE_Test=0.0509 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.162 SMAPE_Forecast=0.0505 SMAPE_Test=0.0506 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2348 MASE_Forecast=0.1464 MASE_Test=0.1324 -INFO:pyaf.std:MODEL_L1 L1_Fit=8.171979013139508 L1_Forecast=4.756466303320375 L1_Test=5.4424694412956685 -INFO:pyaf.std:MODEL_L2 L2_Fit=12.923374182199401 L2_Forecast=5.818306127355801 L2_Test=5.756351264627611 -INFO:pyaf.std:MODEL_COMPLEXITY 27 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_Signal_6M' Min=-72.88969478962326 Max=41.36112194203524 Mean=2.294706307547803 StdDev=39.1708297320527 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(15)' [ConstantTrend + Seasonal_MonthOfYear + AR] +INFO:pyaf.std:TREND_DETAIL 'Diff_Signal_6M_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(15)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3074 MAPE_Forecast=0.0328 MAPE_Test=0.0331 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.4185 SMAPE_Forecast=0.0329 SMAPE_Test=0.0334 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.518 MASE_Forecast=0.1045 MASE_Test=0.0931 +INFO:pyaf.std:MODEL_L1 L1_Fit=18.0332441033259 L1_Forecast=3.3964724101477697 L1_Test=3.823967910857101 +INFO:pyaf.std:MODEL_L2 L2_Fit=26.045563755387104 L2_Forecast=4.084925813800209 L2_Test=4.223210122281393 +INFO:pyaf.std:MODEL_COMPLEXITY 47 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 1.9048968112034605 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.266389428277819 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear 15.967734819413437 {1: 11.986774440544878, 7: 16.269930720766652} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag7 0.4606344044622859 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag4 0.33414525651571714 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag11 -0.2707987390809661 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag5 -0.23168350566408247 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag6 -0.22813621345807256 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag3 0.15779608137816015 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag10 0.07510625352835502 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag14 0.06508007687712096 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag2 -0.03671647084834112 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag9 -0.022758417359315386 +INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 0.7055701501810617 +INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag9 0.4266253176035747 +INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 -0.3629466237149812 +INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.2814335198417697 +INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag14 0.16897648178910674 +INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag12 0.16634023997995073 +INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag5 -0.16153285042303595 +INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.1361137166350481 +INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.0863812130830312 +INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 0.06485923430943304 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_M'), (1, 'Signal_2M'), (2, 'Signal_6M')] +INFO:pyaf.std:START_FORECASTING '['Signal_M', 'Signal_2M', 'Signal_6M']' RangeIndex: 360 entries, 0 to 359 Data columns (total 2 columns): @@ -169,62 +184,56 @@ Data columns (total 2 columns): 1 Signal 360 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 5.8 KB -INFO:pyaf.std:START_FORECASTING 'Signal_2M' -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_6M' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_6M' 1.310546636581421 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2M' 1.4805259704589844 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 2.4151809215545654 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M']' 7.24275803565979 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.728558301925659 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 7.477752208709717 Int64Index: 396 entries, 0 to 395 -Data columns (total 31 columns): +Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 TH_M_start 396 non-null datetime64[ns] - 1 Signal_M 360 non-null float64 - 2 Signal_M_Forecast 396 non-null float64 - 3 Signal_M_Forecast_Lower_Bound 36 non-null float64 - 4 Signal_M_Forecast_Upper_Bound 36 non-null float64 - 5 Date 396 non-null datetime64[ns] - 6 TH_2M_start 181 non-null datetime64[ns] - 7 Signal_2M 361 non-null float64 - 8 Signal_2M_Forecast 396 non-null float64 + 1 TH_6M_start 60 non-null datetime64[ns] + 2 TH_2M_start 181 non-null datetime64[ns] + 3 Signal_M 360 non-null float64 + 4 Signal_M_Forecast 396 non-null float64 + 5 Signal_M_Forecast_Lower_Bound 36 non-null float64 + 6 Signal_M_Forecast_Upper_Bound 36 non-null float64 + 7 Signal_2M 180 non-null float64 + 8 Signal_2M_Forecast 181 non-null float64 9 Signal_2M_Forecast_Lower_Bound 1 non-null float64 10 Signal_2M_Forecast_Upper_Bound 1 non-null float64 - 11 TH_6M_start 60 non-null datetime64[ns] - 12 Signal_6M 361 non-null float64 - 13 Signal_6M_Forecast 396 non-null float64 - 14 Signal_6M_Forecast_Lower_Bound 0 non-null float64 - 15 Signal_6M_Forecast_Upper_Bound 0 non-null float64 - 16 Signal_M_BU_Forecast 396 non-null float64 - 17 Signal_2M_BU_Forecast 396 non-null float64 - 18 Signal_6M_BU_Forecast 396 non-null float64 - 19 Signal_6M_AHP_TD_Forecast 396 non-null float64 - 20 Signal_2M_AHP_TD_Forecast 396 non-null float64 - 21 Signal_M_AHP_TD_Forecast 396 non-null float64 - 22 Signal_6M_PHA_TD_Forecast 396 non-null float64 - 23 Signal_2M_PHA_TD_Forecast 396 non-null float64 - 24 Signal_M_PHA_TD_Forecast 396 non-null float64 - 25 Signal_2M_MO_Forecast 396 non-null float64 - 26 Signal_M_MO_Forecast 396 non-null float64 - 27 Signal_6M_MO_Forecast 396 non-null float64 - 28 Signal_M_OC_Forecast 396 non-null float64 - 29 Signal_2M_OC_Forecast 396 non-null float64 - 30 Signal_6M_OC_Forecast 396 non-null float64 -dtypes: datetime64[ns](4), float64(27) -memory usage: 119.0 KB - TH_M_start Signal_M ... Signal_2M_OC_Forecast Signal_6M_OC_Forecast -391 2033-08-17 NaN ... 6.048980 6.048980 -392 2033-09-16 NaN ... 6.285242 6.285242 -393 2033-10-16 NaN ... 7.150714 7.150714 -394 2033-11-15 NaN ... 7.336162 7.336162 -395 2033-12-15 NaN ... 7.699144 7.699144 + 11 Signal_6M 361 non-null float64 + 12 Signal_6M_Forecast 396 non-null float64 + 13 Signal_6M_Forecast_Lower_Bound 0 non-null float64 + 14 Signal_6M_Forecast_Upper_Bound 0 non-null float64 + 15 Signal_M_BU_Forecast 396 non-null float64 + 16 Signal_2M_BU_Forecast 396 non-null float64 + 17 Signal_6M_BU_Forecast 181 non-null float64 + 18 Signal_6M_AHP_TD_Forecast 396 non-null float64 + 19 Signal_2M_AHP_TD_Forecast 396 non-null float64 + 20 Signal_M_AHP_TD_Forecast 396 non-null float64 + 21 Signal_6M_PHA_TD_Forecast 396 non-null float64 + 22 Signal_2M_PHA_TD_Forecast 396 non-null float64 + 23 Signal_M_PHA_TD_Forecast 396 non-null float64 + 24 Signal_2M_MO_Forecast 181 non-null float64 + 25 Signal_M_MO_Forecast 181 non-null float64 + 26 Signal_6M_MO_Forecast 181 non-null float64 + 27 Signal_M_OC_Forecast 181 non-null float64 + 28 Signal_2M_OC_Forecast 181 non-null float64 + 29 Signal_6M_OC_Forecast 181 non-null float64 +dtypes: datetime64[ns](3), float64(27) +memory usage: 115.9 KB + TH_M_start TH_6M_start ... Signal_2M_OC_Forecast Signal_6M_OC_Forecast +391 2033-08-17 NaT ... NaN NaN +392 2033-09-16 NaT ... NaN NaN +393 2033-10-16 NaT ... NaN NaN +394 2033-11-15 NaT ... NaN NaN +395 2033-12-15 NaT ... NaN NaN -[5 rows x 31 columns] +[5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M_12M.log b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M_12M.log index b99a90ee6..4339ce67e 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M_12M.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M_12M.log @@ -1,31 +1,18 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3724076747894287 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.083630084991455 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'M': 2419200.0, '2M': 5097600.0, '6M': 15638400.0, '12M': 31536000.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA M {'TH_M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-02-28 00:00:00'), 2: Timestamp('2001-03-31 00:00:00'), 3: Timestamp('2001-04-30 00:00:00'), 4: Timestamp('2001-05-31 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 2M {'TH_2M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-03-31 00:00:00'), 2: Timestamp('2001-05-31 00:00:00'), 3: Timestamp('2001-07-31 00:00:00'), 4: Timestamp('2001-09-30 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: 0.2559389701067345, 2: 7.426331735672635, 3: 13.535884056849122, 4: 16.03047951939704}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 6M {'TH_6M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-07-31 00:00:00'), 2: Timestamp('2002-01-31 00:00:00'), 3: Timestamp('2002-07-31 00:00:00'), 4: Timestamp('2003-01-31 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: 21.21815476262849, 2: 56.62764847921909, 3: 94.73224181234255, 4: 42.68897101726309}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 12M {'TH_12M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2002-01-31 00:00:00'), 2: Timestamp('2003-01-31 00:00:00'), 3: Timestamp('2004-01-31 00:00:00'), 4: Timestamp('2005-01-31 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: 77.84580324184758, 2: 137.42121282960562, 3: 112.70370368167085, 4: 118.6795320316762}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'M': 36, '2M': 17, '6M': 5, '12M': 2} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_M'), (1, 'Signal_2M'), (2, 'Signal_6M'), (3, 'Signal_12M')] +INFO:pyaf.std:START_TRAINING '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' -INFO:pyaf.std:START_TRAINING 'Signal_2M' -INFO:pyaf.std:START_TRAINING 'Signal_M' -INFO:pyaf.std:START_TRAINING 'Signal_6M' -INFO:pyaf.std:START_TRAINING 'Signal_12M' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_12M' 3.9018585681915283 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_6M' 6.822707653045654 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_2M' 9.827259063720703 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_M' 12.624533414840698 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' 32.46165728569031 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_M'), (1, 'Signal_2M'), (2, 'Signal_6M'), (3, 'Signal_12M')] -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_2M' -INFO:pyaf.std:START_FORECASTING 'Signal_6M' -INFO:pyaf.std:START_FORECASTING 'Signal_12M' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_6M' 1.2495687007904053 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_12M' 1.2816269397735596 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2M' 1.587369441986084 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 2.127450704574585 +INFO:pyaf.std:START_FORECASTING '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' 5.117384433746338 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -34,13 +21,12 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'Signal_M', 'Signal_M_Forecast', - 'Signal_M_Forecast_Lower_Bound', 'Signal_M_Forecast_Upper_Bound', - 'Date', 'TH_2M_start', 'Signal_2M', 'Signal_2M_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_12M_start', 'TH_6M_start', 'TH_M_start', 'TH_2M_start', 'Signal_M', + 'Signal_M_Forecast', 'Signal_M_Forecast_Lower_Bound', + 'Signal_M_Forecast_Upper_Bound', 'Signal_2M', 'Signal_2M_Forecast', 'Signal_2M_Forecast_Lower_Bound', 'Signal_2M_Forecast_Upper_Bound', - 'TH_6M_start', 'Signal_6M', 'Signal_6M_Forecast', - 'Signal_6M_Forecast_Lower_Bound', 'Signal_6M_Forecast_Upper_Bound', - 'TH_12M_start', 'Signal_12M', 'Signal_12M_Forecast', + 'Signal_6M', 'Signal_6M_Forecast', 'Signal_6M_Forecast_Lower_Bound', + 'Signal_6M_Forecast_Upper_Bound', 'Signal_12M', 'Signal_12M_Forecast', 'Signal_12M_Forecast_Lower_Bound', 'Signal_12M_Forecast_Upper_Bound', 'Signal_M_BU_Forecast', 'Signal_2M_BU_Forecast', 'Signal_6M_BU_Forecast', 'Signal_12M_BU_Forecast', @@ -54,54 +40,50 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'Signal_M', 'Signal_ 'Signal_6M_OC_Forecast', 'Signal_12M_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_M']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_2M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_2M']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_6M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_6M']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (3, ['Signal_12M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (3, ['Signal_12M']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12M_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.139, 'RMSE': 7.887994719625689, 'MAE': 1.5286180059470547, 'SMAPE': 0.0172, 'ErrorMean': 5.486816492355986e-16, 'ErrorStdDev': 7.887994719625689, 'R2': 0.96609942097025, 'Pearson': 0.9829035664662398} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12M_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.0052, 'RMSE': 4.298787083001468, 'MAE': 1.03823765734521, 'SMAPE': 0.0054, 'ErrorMean': -0.7817827199541559, 'ErrorStdDev': 4.227101390286416, 'R2': 0.9936144044368704, 'Pearson': 0.9982303220124706} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12M_BU_Forecast', 'Length': 259, 'MAPE': 62512253581.8399, 'RMSE': 32.225341720980595, 'MAE': 12.572058420002671, 'SMAPE': 0.2331, 'ErrorMean': 0.2224943307159361, 'ErrorStdDev': 32.22457362490265, 'R2': 0.4341921658160214, 'Pearson': 0.6598541202971208} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12M_BU_Forecast', 'Length': 65, 'MAPE': 79926167810.3309, 'RMSE': 41.23461311823179, 'MAE': 15.714593860115713, 'SMAPE': 0.2061, 'ErrorMean': 0.2706397019428056, 'ErrorStdDev': 41.233724948905426, 'R2': 0.4124654822757453, 'Pearson': 0.6448025163259655} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12M_MO_Forecast', 'Length': 259, 'MAPE': 62512253581.8399, 'RMSE': 32.225341720980595, 'MAE': 12.572058420002671, 'SMAPE': 0.2331, 'ErrorMean': 0.2224943307159361, 'ErrorStdDev': 32.22457362490265, 'R2': 0.4341921658160214, 'Pearson': 0.6598541202971208} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12M_MO_Forecast', 'Length': 65, 'MAPE': 79926167810.3309, 'RMSE': 41.23461311823179, 'MAE': 15.714593860115713, 'SMAPE': 0.2061, 'ErrorMean': 0.2706397019428056, 'ErrorStdDev': 41.233724948905426, 'R2': 0.4124654822757453, 'Pearson': 0.6448025163259655} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12M_OC_Forecast', 'Length': 259, 'MAPE': 70554345727.8796, 'RMSE': 27.939855487264428, 'MAE': 14.159789348097151, 'SMAPE': 1.9012, 'ErrorMean': 0.16687074803695207, 'ErrorStdDev': 27.93935716516523, 'R2': 0.5746737327172498, 'Pearson': 0.8885072154597893} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12M_OC_Forecast', 'Length': 65, 'MAPE': 92341002788.1906, 'RMSE': 35.12702562542373, 'MAE': 18.063484557680262, 'SMAPE': 1.908, 'ErrorMean': 0.4047159999490746, 'ErrorStdDev': 35.12469408049784, 'R2': 0.5736244493549778, 'Pearson': 0.8961026565679833} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_12M_PHA_TD_Forecast', 'Length': 259, 'MAPE': 0.139, 'RMSE': 7.887994719625689, 'MAE': 1.5286180059470547, 'SMAPE': 0.0172, 'ErrorMean': 5.486816492355986e-16, 'ErrorStdDev': 7.887994719625689, 'R2': 0.96609942097025, 'Pearson': 0.9829035664662398} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_12M_PHA_TD_Forecast', 'Length': 65, 'MAPE': 0.0052, 'RMSE': 4.298787083001468, 'MAE': 1.03823765734521, 'SMAPE': 0.0054, 'ErrorMean': -0.7817827199541559, 'ErrorStdDev': 4.227101390286416, 'R2': 0.9936144044368704, 'Pearson': 0.9982303220124706} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.4828, 'RMSE': 18.29893310618518, 'MAE': 11.215329318322162, 'SMAPE': 0.8705, 'ErrorMean': -10.114326357619454, 'ErrorStdDev': 15.249634590907561, 'R2': -0.45865196374767847, 'Pearson': 0.2595571494169025} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.4438, 'RMSE': 23.0809300893257, 'MAE': 14.755314125472394, 'SMAPE': 0.8525, 'ErrorMean': -13.785610070352137, 'ErrorStdDev': 18.51178783306859, 'R2': -0.5603637873894445, 'Pearson': 0.2658881915929311} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_BU_Forecast', 'Length': 259, 'MAPE': 62422770984.7541, 'RMSE': 14.05234794373008, 'MAE': 12.554649574102259, 'SMAPE': 1.3407, 'ErrorMean': 3.017749070795792e-16, 'ErrorStdDev': 14.05234794373008, 'R2': 0.13980298492491605, 'Pearson': 0.3755152988386296} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_BU_Forecast', 'Length': 65, 'MAPE': 82574882602.5213, 'RMSE': 17.83909402922174, 'MAE': 16.872359020076644, 'SMAPE': 1.365, 'ErrorMean': -0.3573824996237475, 'ErrorStdDev': 17.83551382866155, 'R2': 0.06789499285945555, 'Pearson': 0.26647359469881626} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_MO_Forecast', 'Length': 259, 'MAPE': 0.4096, 'RMSE': 16.414353045698554, 'MAE': 9.2530716932096, 'SMAPE': 0.7143, 'ErrorMean': -8.2803248747435, 'ErrorStdDev': 14.172762817373947, 'R2': -0.1736745360142622, 'Pearson': 0.42629903512213163} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_MO_Forecast', 'Length': 65, 'MAPE': 0.3626, 'RMSE': 20.828858222318555, 'MAE': 12.08617804712387, 'SMAPE': 0.6988, 'ErrorMean': -11.342915326943302, 'ErrorStdDev': 17.469390565536113, 'R2': -0.27072091854152425, 'Pearson': 0.4226135632263022} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_OC_Forecast', 'Length': 259, 'MAPE': 15605692746.6495, 'RMSE': 15.924444346384202, 'MAE': 10.366019580899199, 'SMAPE': 1.3907, 'ErrorMean': -0.055623582678984, 'ErrorStdDev': 15.924347200376463, 'R2': -0.10466022468198033, 'Pearson': 0.5574531568345897} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_OC_Forecast', 'Length': 65, 'MAPE': 20643720650.9252, 'RMSE': 19.723546788395065, 'MAE': 13.579750215545602, 'SMAPE': 1.4066, 'ErrorMean': -0.8635900864463069, 'ErrorStdDev': 19.704631690965474, 'R2': -0.13943427965630062, 'Pearson': 0.550872141467011} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2M_PHA_TD_Forecast', 'Length': 259, 'MAPE': 0.4743, 'RMSE': 18.255541955911987, 'MAE': 11.18600862125028, 'SMAPE': 0.8703, 'ErrorMean': -10.398547517119743, 'ErrorStdDev': 15.004500046261649, 'R2': -0.45174253954764754, 'Pearson': 0.2595571494169025} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2M_PHA_TD_Forecast', 'Length': 65, 'MAPE': 0.4423, 'RMSE': 23.057641516027182, 'MAE': 14.818823956434633, 'SMAPE': 0.8546, 'ErrorMean': -14.122805794250738, 'ErrorStdDev': 18.226387156523867, 'R2': -0.557216573346135, 'Pearson': 0.265888191592931} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_6M_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.1809, 'RMSE': 24.29494977950279, 'MAE': 8.070743333340507, 'SMAPE': 0.2001, 'ErrorMean': -6.018726207892662, 'ErrorStdDev': 23.537619242884112, 'R2': 0.3540988483016295, 'Pearson': 0.6355191862686351} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_6M_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.0952, 'RMSE': 30.76524908118996, 'MAE': 9.811597283081364, 'SMAPE': 0.1707, 'ErrorMean': -8.095715581793627, 'ErrorStdDev': 29.680969327944187, 'R2': 0.35172512998066363, 'Pearson': 0.6337454009310847} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 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1.1036, 'ErrorMean': 45.13662673821462, 'ErrorStdDev': 22.982776401178242, 'R2': -83.35090860579044, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 44, 'MAPE': 1.0385, 'RMSE': 10.71256983316866, 'MAE': 8.917112991303807, 'SMAPE': 1.2819, 'ErrorMean': -5.423206409132171, 'ErrorStdDev': 9.238397300098265, 'R2': -1.8702263604502463, 'Pearson': 0.0021011393507872067} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 10, 'MAPE': 0.6495, 'RMSE': 12.010162451695138, 'MAE': 10.02411236215176, 'SMAPE': 1.1433, 'ErrorMean': -8.290333286653208, 'ErrorStdDev': 8.689900811419978, 'R2': -3.7425465447411463, 'Pearson': 0.0} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 38.86502122879028 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-07-31T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_M' Length=360 Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_M' Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 @@ -113,20 +95,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_M_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3608 MAPE_Forecast=0.0933 MAPE_Test=0.0727 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1559 SMAPE_Forecast=0.1004 SMAPE_Test=0.0754 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5456 MASE_Forecast=0.6072 MASE_Test=0.5062 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.2707655069523887 L1_Forecast=1.3119893773007625 L1_Test=1.269372776026798 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.7464635036586067 L2_Test=1.4940617790422326 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.270765506952389 L1_Forecast=1.3119893773007616 L1_Test=1.269372776026798 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.746463503658606 L2_Test=1.4940617790422324 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12.516770159495985 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_M_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529058 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969627 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630132 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067375 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684192 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395425 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1522696318163203 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070928 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag56 0.14777440880242368 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529054 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969634 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813113 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630128 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067456 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684117 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395358 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.15226963181632097 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070792 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag56 0.1477744088024236 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_2M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-07-31T00:00:00.000000 TimeDelta= Horizon=17 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_2M' Length=180 Min=-0.41263817098822364 Max=54.50574426645973 Mean=27.79443879544697 StdDev=12.710746738178202 @@ -139,46 +130,64 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_2M_PolyTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.8914 MAPE_Forecast=0.0503 MAPE_Test=0.0423 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1437 SMAPE_Forecast=0.0501 SMAPE_Test=0.0417 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3351 MASE_Forecast=0.2419 MASE_Test=0.1923 -INFO:pyaf.std:MODEL_L1 L1_Fit=2.3254083078458745 L1_Forecast=1.5648871681954617 L1_Test=1.5010490628282729 -INFO:pyaf.std:MODEL_L2 L2_Fit=3.816061994080786 L2_Forecast=1.9209564028146704 L2_Test=1.9619396247933936 +INFO:pyaf.std:MODEL_L1 L1_Fit=2.3254083078458754 L1_Forecast=1.5648871681954581 L1_Test=1.5010490628282671 +INFO:pyaf.std:MODEL_L2 L2_Fit=3.8160619940807856 L2_Forecast=1.920956402814668 L2_Test=1.9619396247933885 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (18.699624361314886, array([11.35389737, 2.54733243, -1.1543063 ])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_2M_PolyTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag21 0.625735294705792 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag1 0.5645747604726261 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag22 -0.46173321401484885 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag10 0.3947633996074761 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag12 -0.18994759552340598 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag31 -0.1811162830569539 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag20 -0.14938498763315758 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag42 0.13856514460582142 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag43 -0.10901605686385624 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag7 -0.10774018536071794 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag21 0.6257352947057914 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag1 0.5645747604726263 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag22 -0.46173321401484846 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag10 0.3947633996074763 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag12 -0.18994759552340576 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag31 -0.18111628305695376 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag20 -0.14938498763315713 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag42 0.13856514460582156 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag43 -0.1090160568638561 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_2M_PolyTrend_residue_zeroCycle_residue_Lag7 -0.10774018536071775 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_6M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-07-31T00:00:00.000000 TimeDelta= Horizon=5 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_6M' Length=60 Min=1.9048968112034605 Max=140.39468221087236 Mean=82.18464232125778 StdDev=31.75370912609833 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_6M' Min=1.9048968112034605 Max=140.39468221087236 Mean=82.18464232125778 StdDev=31.75370912609833 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_6M_LinearTrend_residue_zeroCycle_residue_AR(15)' [LinearTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Signal_6M_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_6M_LinearTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_6M_LinearTrend_residue_zeroCycle_residue_AR(15)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.5762 MAPE_Forecast=0.0498 MAPE_Test=0.0509 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.162 SMAPE_Forecast=0.0505 SMAPE_Test=0.0506 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2348 MASE_Forecast=0.1464 MASE_Test=0.1324 -INFO:pyaf.std:MODEL_L1 L1_Fit=8.171979013139508 L1_Forecast=4.756466303320375 L1_Test=5.4424694412956685 -INFO:pyaf.std:MODEL_L2 L2_Fit=12.923374182199401 L2_Forecast=5.818306127355801 L2_Test=5.756351264627611 -INFO:pyaf.std:MODEL_COMPLEXITY 27 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_Signal_6M' Min=-72.88969478962326 Max=41.36112194203524 Mean=2.294706307547803 StdDev=39.1708297320527 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(15)' [ConstantTrend + Seasonal_MonthOfYear + AR] +INFO:pyaf.std:TREND_DETAIL 'Diff_Signal_6M_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(15)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3074 MAPE_Forecast=0.0328 MAPE_Test=0.0331 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.4185 SMAPE_Forecast=0.0329 SMAPE_Test=0.0334 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.518 MASE_Forecast=0.1045 MASE_Test=0.0931 +INFO:pyaf.std:MODEL_L1 L1_Fit=18.0332441033259 L1_Forecast=3.3964724101477697 L1_Test=3.823967910857101 +INFO:pyaf.std:MODEL_L2 L2_Fit=26.045563755387104 L2_Forecast=4.084925813800209 L2_Test=4.223210122281393 +INFO:pyaf.std:MODEL_COMPLEXITY 47 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 1.9048968112034605 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.266389428277819 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear 15.967734819413437 {1: 11.986774440544878, 7: 16.269930720766652} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag7 0.4606344044622859 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag4 0.33414525651571714 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag11 -0.2707987390809661 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag5 -0.23168350566408247 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag6 -0.22813621345807256 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag3 0.15779608137816015 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag10 0.07510625352835502 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag14 0.06508007687712096 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag2 -0.03671647084834112 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_6M_LinearTrend_residue_zeroCycle_residue_Lag9 -0.022758417359315386 +INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag7 0.7055701501810617 +INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag9 0.4266253176035747 +INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag2 -0.3629466237149812 +INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag4 0.2814335198417697 +INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag14 0.16897648178910674 +INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag12 0.16634023997995073 +INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag5 -0.16153285042303595 +INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag11 -0.1361137166350481 +INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag3 0.0863812130830312 +INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_Signal_6M_ConstantTrend_residue_Seasonal_MonthOfYear_residue_Lag10 0.06485923430943304 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_12M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-01-31T00:00:00.000000 TimeDelta= Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_12M' Length=30 Min=1.9048968112034605 Max=261.3922179181285 Mean=159.71637546704648 StdDev=52.03362036740663 @@ -191,18 +200,27 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_12M_ConstantTrend_residue_zeroCycle_residu INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.6362 MAPE_Forecast=0.0617 MAPE_Test=0.0706 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2031 SMAPE_Forecast=0.0638 SMAPE_Test=0.0746 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3485 MASE_Forecast=0.3451 MASE_Test=0.2045 -INFO:pyaf.std:MODEL_L1 L1_Fit=17.996002888194866 L1_Forecast=12.112090054655733 L1_Test=17.54681660505466 -INFO:pyaf.std:MODEL_L2 L2_Fit=27.064820608446233 L2_Forecast=14.306630191801721 L2_Test=22.335970645828688 +INFO:pyaf.std:MODEL_L1 L1_Fit=17.996002888194877 L1_Forecast=12.112090054655733 L1_Test=17.54681660505463 +INFO:pyaf.std:MODEL_L2 L2_Fit=27.064820608446233 L2_Forecast=14.306630191801712 L2_Test=22.335970645828667 INFO:pyaf.std:MODEL_COMPLEXITY 5 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 144.7371563479106 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_12M_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_12M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.6630139833059062 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_12M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.6630139833059061 INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_12M_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.36379714696276777 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_12M_ConstantTrend_residue_zeroCycle_residue_Lag7 0.1805051913899593 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_12M_ConstantTrend_residue_zeroCycle_residue_Lag5 0.14627795763309365 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_12M_ConstantTrend_residue_zeroCycle_residue_Lag3 0.039954049931991184 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_12M_ConstantTrend_residue_zeroCycle_residue_Lag7 0.18050519138995952 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_12M_ConstantTrend_residue_zeroCycle_residue_Lag5 0.14627795763309337 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_12M_ConstantTrend_residue_zeroCycle_residue_Lag3 0.039954049931991635 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_M'), (1, 'Signal_2M'), (2, 'Signal_6M'), (3, 'Signal_12M')] +INFO:pyaf.std:START_FORECASTING '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' RangeIndex: 360 entries, 0 to 359 Data columns (total 2 columns): @@ -212,74 +230,66 @@ Data columns (total 2 columns): 1 Signal 360 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 5.8 KB -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_2M' -INFO:pyaf.std:START_FORECASTING 'Signal_12M' -INFO:pyaf.std:START_FORECASTING 'Signal_6M' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_12M' 1.1232459545135498 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2M' 1.5099198818206787 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_6M' 1.4795241355895996 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 2.135326385498047 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' 4.974066734313965 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.407212257385254 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 5.2717506885528564 Int64Index: 396 entries, 0 to 395 -Data columns (total 41 columns): +Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_M_start 396 non-null datetime64[ns] - 1 Signal_M 360 non-null float64 - 2 Signal_M_Forecast 396 non-null float64 - 3 Signal_M_Forecast_Lower_Bound 36 non-null float64 - 4 Signal_M_Forecast_Upper_Bound 36 non-null float64 - 5 Date 396 non-null datetime64[ns] - 6 TH_2M_start 181 non-null datetime64[ns] - 7 Signal_2M 361 non-null float64 - 8 Signal_2M_Forecast 396 non-null float64 - 9 Signal_2M_Forecast_Lower_Bound 1 non-null float64 - 10 Signal_2M_Forecast_Upper_Bound 1 non-null float64 - 11 TH_6M_start 60 non-null datetime64[ns] - 12 Signal_6M 361 non-null float64 - 13 Signal_6M_Forecast 396 non-null float64 + 0 TH_12M_start 32 non-null datetime64[ns] + 1 TH_6M_start 60 non-null datetime64[ns] + 2 TH_M_start 396 non-null datetime64[ns] + 3 TH_2M_start 181 non-null datetime64[ns] + 4 Signal_M 360 non-null float64 + 5 Signal_M_Forecast 396 non-null float64 + 6 Signal_M_Forecast_Lower_Bound 36 non-null float64 + 7 Signal_M_Forecast_Upper_Bound 36 non-null float64 + 8 Signal_2M 180 non-null float64 + 9 Signal_2M_Forecast 181 non-null float64 + 10 Signal_2M_Forecast_Lower_Bound 1 non-null float64 + 11 Signal_2M_Forecast_Upper_Bound 1 non-null float64 + 12 Signal_6M 60 non-null float64 + 13 Signal_6M_Forecast 60 non-null float64 14 Signal_6M_Forecast_Lower_Bound 0 non-null float64 15 Signal_6M_Forecast_Upper_Bound 0 non-null float64 - 16 TH_12M_start 32 non-null datetime64[ns] - 17 Signal_12M 361 non-null float64 - 18 Signal_12M_Forecast 396 non-null float64 - 19 Signal_12M_Forecast_Lower_Bound 1 non-null float64 - 20 Signal_12M_Forecast_Upper_Bound 1 non-null float64 - 21 Signal_M_BU_Forecast 396 non-null float64 - 22 Signal_2M_BU_Forecast 396 non-null float64 - 23 Signal_6M_BU_Forecast 396 non-null float64 - 24 Signal_12M_BU_Forecast 396 non-null float64 - 25 Signal_12M_AHP_TD_Forecast 396 non-null float64 - 26 Signal_6M_AHP_TD_Forecast 396 non-null float64 - 27 Signal_2M_AHP_TD_Forecast 396 non-null float64 - 28 Signal_M_AHP_TD_Forecast 396 non-null float64 - 29 Signal_12M_PHA_TD_Forecast 396 non-null float64 - 30 Signal_6M_PHA_TD_Forecast 396 non-null float64 - 31 Signal_2M_PHA_TD_Forecast 396 non-null float64 - 32 Signal_M_PHA_TD_Forecast 396 non-null float64 - 33 Signal_6M_MO_Forecast 396 non-null float64 - 34 Signal_2M_MO_Forecast 396 non-null float64 - 35 Signal_M_MO_Forecast 396 non-null float64 - 36 Signal_12M_MO_Forecast 396 non-null float64 - 37 Signal_M_OC_Forecast 396 non-null float64 - 38 Signal_2M_OC_Forecast 396 non-null float64 - 39 Signal_6M_OC_Forecast 396 non-null float64 - 40 Signal_12M_OC_Forecast 396 non-null float64 -dtypes: datetime64[ns](5), float64(36) -memory usage: 149.9 KB - TH_M_start Signal_M ... Signal_6M_OC_Forecast Signal_12M_OC_Forecast -391 2033-08-17 NaN ... 4.536735 4.536735 -392 2033-09-16 NaN ... 4.713931 4.713931 -393 2033-10-16 NaN ... 5.363035 5.363035 -394 2033-11-15 NaN ... 5.502121 5.502121 -395 2033-12-15 NaN ... 5.774358 5.774358 + 16 Signal_12M 361 non-null float64 + 17 Signal_12M_Forecast 396 non-null float64 + 18 Signal_12M_Forecast_Lower_Bound 1 non-null float64 + 19 Signal_12M_Forecast_Upper_Bound 1 non-null float64 + 20 Signal_M_BU_Forecast 396 non-null float64 + 21 Signal_2M_BU_Forecast 396 non-null float64 + 22 Signal_6M_BU_Forecast 181 non-null float64 + 23 Signal_12M_BU_Forecast 60 non-null float64 + 24 Signal_12M_AHP_TD_Forecast 396 non-null float64 + 25 Signal_6M_AHP_TD_Forecast 396 non-null float64 + 26 Signal_2M_AHP_TD_Forecast 396 non-null float64 + 27 Signal_M_AHP_TD_Forecast 396 non-null float64 + 28 Signal_12M_PHA_TD_Forecast 396 non-null float64 + 29 Signal_6M_PHA_TD_Forecast 396 non-null float64 + 30 Signal_2M_PHA_TD_Forecast 396 non-null float64 + 31 Signal_M_PHA_TD_Forecast 396 non-null float64 + 32 Signal_6M_MO_Forecast 60 non-null float64 + 33 Signal_2M_MO_Forecast 60 non-null float64 + 34 Signal_M_MO_Forecast 60 non-null float64 + 35 Signal_12M_MO_Forecast 60 non-null float64 + 36 Signal_M_OC_Forecast 60 non-null float64 + 37 Signal_2M_OC_Forecast 60 non-null float64 + 38 Signal_6M_OC_Forecast 60 non-null float64 + 39 Signal_12M_OC_Forecast 60 non-null float64 +dtypes: datetime64[ns](4), float64(36) +memory usage: 146.8 KB + TH_12M_start TH_6M_start ... Signal_6M_OC_Forecast Signal_12M_OC_Forecast +391 NaT NaT ... NaN NaN +392 NaT NaT ... NaN NaN +393 NaT NaT ... NaN NaN +394 NaT NaT ... NaN NaN +395 NaT NaT ... NaN NaN -[5 rows x 41 columns] +[5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_Q_A.log b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_Q_A.log index d98f68f3c..38d820732 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_Q_A.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_Q_A.log @@ -1,26 +1,17 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3852651119232178 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.9815919399261475 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'M': 2419200.0, 'Q': 7862400.0, 'A': 31536000.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA M {'TH_M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-02-28 00:00:00'), 2: Timestamp('2001-03-31 00:00:00'), 3: Timestamp('2001-04-30 00:00:00'), 4: Timestamp('2001-05-31 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA Q {'TH_Q_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-05-02 00:00:00'), 2: Timestamp('2001-08-02 00:00:00'), 3: Timestamp('2001-11-02 00:00:00'), 4: Timestamp('2002-01-31 00:00:00')}, 'Signal': {0: 2.160835781310195, 1: 13.92196646750147, 2: 23.070728844417328, 3: 28.762012210399895, 4: 40.97491799170526}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA A {'TH_A_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2002-01-31 00:00:00'), 2: Timestamp('2003-01-31 00:00:00'), 3: Timestamp('2004-02-01 00:00:00'), 4: Timestamp('2005-01-31 00:00:00')}, 'Signal': {0: 67.9155433036289, 1: 146.19702259647588, 2: 99.27213122050448, 3: 127.36564488404751, 4: 155.37956250958317}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'M': 36, 'Q': 11, 'A': 2} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_M'), (1, 'Signal_Q'), (2, 'Signal_A')] +INFO:pyaf.std:START_TRAINING '['Signal_M', 'Signal_Q', 'Signal_A']' -INFO:pyaf.std:START_TRAINING 'Signal_M' -INFO:pyaf.std:START_TRAINING 'Signal_Q' -INFO:pyaf.std:START_TRAINING 'Signal_A' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_A' 4.63890528678894 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_Q' 4.768761873245239 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_M' 13.155908823013306 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_M', 'Signal_Q', 'Signal_A']' 35.204349517822266 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_M'), (1, 'Signal_Q'), (2, 'Signal_A')] -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:START_FORECASTING 'Signal_A' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 1.038102626800537 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_A' 1.2401161193847656 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 2.054128408432007 +INFO:pyaf.std:START_FORECASTING '['Signal_M', 'Signal_Q', 'Signal_A']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_Q', 'Signal_A']' 7.21161961555481 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -29,13 +20,13 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'Signal_M', 'Signal_M_Forecast', - 'Signal_M_Forecast_Lower_Bound', 'Signal_M_Forecast_Upper_Bound', - 'Date', 'TH_Q_start', 'Signal_Q', 'Signal_Q_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_A_start', 'TH_M_start', 'TH_Q_start', 'Signal_M', + 'Signal_M_Forecast', 'Signal_M_Forecast_Lower_Bound', + 'Signal_M_Forecast_Upper_Bound', 'Signal_Q', 'Signal_Q_Forecast', 'Signal_Q_Forecast_Lower_Bound', 'Signal_Q_Forecast_Upper_Bound', - 'TH_A_start', 'Signal_A', 'Signal_A_Forecast', - 'Signal_A_Forecast_Lower_Bound', 'Signal_A_Forecast_Upper_Bound', - 'Signal_M_BU_Forecast', 'Signal_Q_BU_Forecast', 'Signal_A_BU_Forecast', + 'Signal_A', 'Signal_A_Forecast', 'Signal_A_Forecast_Lower_Bound', + 'Signal_A_Forecast_Upper_Bound', 'Signal_M_BU_Forecast', + 'Signal_Q_BU_Forecast', 'Signal_A_BU_Forecast', 'Signal_A_AHP_TD_Forecast', 'Signal_Q_AHP_TD_Forecast', 'Signal_M_AHP_TD_Forecast', 'Signal_A_PHA_TD_Forecast', 'Signal_Q_PHA_TD_Forecast', 'Signal_M_PHA_TD_Forecast', @@ -43,42 +34,39 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'Signal_M', 'Signal_ 'Signal_M_OC_Forecast', 'Signal_Q_OC_Forecast', 'Signal_A_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_M']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_Q']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_Q']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_A']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_A']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.0087, 'RMSE': 6.707612789869883, 'MAE': 1.279653817178674, 'SMAPE': 0.0091, 'ErrorMean': -0.3267599031037659, 'ErrorStdDev': 6.6996490433865015, 'R2': 0.9706812657599104, 'Pearson': 0.9854988876498958} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.0069, 'RMSE': 6.508934632555061, 'MAE': 1.4144850449909752, 'SMAPE': 0.0065, 'ErrorMean': 1.2645190116664988, 'ErrorStdDev': 6.3849214341296845, 'R2': 0.9831204197101734, 'Pearson': 0.9972160605956175} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_BU_Forecast', 'Length': 259, 'MAPE': 0.0546, 'RMSE': 33.70257840572294, 'MAE': 8.267317708122912, 'SMAPE': 0.0944, 'ErrorMean': -8.267317708122912, 'ErrorStdDev': 32.67285186675414, 'R2': 0.25982313958125713, 'Pearson': 0.923267812392531} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_BU_Forecast', 'Length': 65, 'MAPE': 0.0456, 'RMSE': 38.62360019507947, 'MAE': 9.507123355508199, 'SMAPE': 0.0726, 'ErrorMean': -9.507123355508199, 'ErrorStdDev': 37.43523871344343, 'R2': 0.40564307187755144, 'Pearson': 0.991655131968765} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_MO_Forecast', 'Length': 259, 'MAPE': 0.0546, 'RMSE': 33.70257840572294, 'MAE': 8.267317708122912, 'SMAPE': 0.0944, 'ErrorMean': -8.267317708122912, 'ErrorStdDev': 32.67285186675414, 'R2': 0.25982313958125713, 'Pearson': 0.923267812392531} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_MO_Forecast', 'Length': 65, 'MAPE': 0.0456, 'RMSE': 38.62360019507947, 'MAE': 9.507123355508199, 'SMAPE': 0.0726, 'ErrorMean': -9.507123355508199, 'ErrorStdDev': 37.43523871344343, 'R2': 0.40564307187755144, 'Pearson': 0.991655131968765} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_OC_Forecast', 'Length': 259, 'MAPE': 39388441345.1199, 'RMSE': 24.979934239808617, 'MAE': 9.888638722596943, 'SMAPE': 1.9244, 'ErrorMean': -2.010950453580727, 'ErrorStdDev': 24.898859269018864, 'R2': 0.5933771031400343, 'Pearson': 0.973996630118727} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_OC_Forecast', 'Length': 65, 'MAPE': 50358082751.7634, 'RMSE': 27.497081090583475, 'MAE': 11.65346863086707, 'SMAPE': 1.9198, 'ErrorMean': -1.5818520805207412, 'ErrorStdDev': 27.45154298937448, 'R2': 0.6987583536569502, 'Pearson': 0.9935433442412095} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_PHA_TD_Forecast', 'Length': 259, 'MAPE': 0.0087, 'RMSE': 6.707612789869883, 'MAE': 1.279653817178674, 'SMAPE': 0.0091, 'ErrorMean': -0.3267599031037659, 'ErrorStdDev': 6.6996490433865015, 'R2': 0.9706812657599104, 'Pearson': 0.9854988876498958} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_PHA_TD_Forecast', 'Length': 65, 'MAPE': 0.0069, 'RMSE': 6.508934632555061, 'MAE': 1.4144850449909752, 'SMAPE': 0.0065, 'ErrorMean': 1.2645190116664988, 'ErrorStdDev': 6.3849214341296845, 'R2': 0.9831204197101734, 'Pearson': 0.9972160605956175} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.9834, 'RMSE': 13.796087659264716, 'MAE': 12.132077328555651, 'SMAPE': 1.9032, 'ErrorMean': -11.76254023650412, 'ErrorStdDev': 7.209346841894748, 'R2': -3.5405167357722647, 'Pearson': -0.0340387088062765} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.9564, 'RMSE': 17.169191930086694, 'MAE': 16.00120450371565, 'SMAPE': 1.895, 'ErrorMean': -15.762535867902923, 'ErrorStdDev': 6.806145351462016, 'R2': -8.012005572683027, 'Pearson': 0.10201256465908363} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_BU_Forecast', 'Length': 259, 'MAPE': 0.3608, 'RMSE': 1.8075756006784895, 'MAE': 1.2707655069523887, 'SMAPE': 0.1559, 'ErrorMean': -2.400482215405744e-16, 'ErrorStdDev': 1.8075756006784895, 'R2': 0.9220553464049209, 'Pearson': 0.9602371363938411} 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25.293112639986443, 'ErrorStdDev': 18.930936879839113, 'R2': -1.683413792252066, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 4, 'MAPE': 0.732, 'RMSE': 42.908727197237496, 'MAE': 38.82916266506209, 'SMAPE': 0.4902, 'ErrorMean': 38.82916266506209, 'ErrorStdDev': 18.2607501603055, 'R2': -11.203744795206887, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 17, 'MAPE': 0.9288, 'RMSE': 17.784348385238747, 'MAE': 14.888903517001367, 'SMAPE': 0.5078, 'ErrorMean': -1.17291227257665, 'ErrorStdDev': 17.745628314838083, 'R2': 0.1496847464868839, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 4, 'MAPE': 0.2772, 'RMSE': 18.995493932545745, 'MAE': 16.81210720136117, 'SMAPE': 0.2895, 'ErrorMean': -7.555522406925082, 'ErrorStdDev': 17.428220531650247, 'R2': -1.3916797932358285, 'Pearson': 0.0} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 43.74532723426819 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-07-31T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_M' Length=360 Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_M' Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 @@ -90,20 +78,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_M_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3608 MAPE_Forecast=0.0933 MAPE_Test=0.0727 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1559 SMAPE_Forecast=0.1004 SMAPE_Test=0.0754 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5456 MASE_Forecast=0.6072 MASE_Test=0.5062 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.2707655069523887 L1_Forecast=1.3119893773007625 L1_Test=1.269372776026798 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.7464635036586067 L2_Test=1.4940617790422326 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.270765506952389 L1_Forecast=1.3119893773007616 L1_Test=1.269372776026798 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.746463503658606 L2_Test=1.4940617790422324 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12.516770159495985 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_M_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529058 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969627 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630132 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067375 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684192 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395425 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1522696318163203 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070928 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag56 0.14777440880242368 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529054 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969634 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813113 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630128 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067456 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684117 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395358 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.15226963181632097 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070792 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_M_ConstantTrend_residue_zeroCycle_residue_Lag56 0.1477744088024236 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_Q_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2030-01-31T00:00:00.000000 TimeDelta= Horizon=11 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_Q' Length=23 Min=2.160835781310195 Max=75.03963759407748 Mean=40.983659969638914 StdDev=20.52825329281211 @@ -119,6 +116,15 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6427 MASE_Forecast=0.6427 MASE_Test=0.6427 INFO:pyaf.std:MODEL_L1 L1_Fit=17.563495413078574 L1_Forecast=17.563495413078574 L1_Test=17.563495413078574 INFO:pyaf.std:MODEL_L2 L2_Fit=21.671282270069106 L2_Forecast=21.671282270069106 L2_Test=21.671282270069106 INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 2.160835781310195 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 2.617754102927246 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_Signal_Q_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_A_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2030-01-31T00:00:00.000000 TimeDelta= Horizon=2 @@ -132,18 +138,27 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_A_Lag1Trend_residue_zeroCycle_residue_AR(5 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1192 MAPE_Forecast=0.1192 MAPE_Test=0.1192 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1232 SMAPE_Forecast=0.1232 SMAPE_Test=0.1232 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3719 MASE_Forecast=0.3719 MASE_Test=0.3719 -INFO:pyaf.std:MODEL_L1 L1_Fit=18.854777652229817 L1_Forecast=18.854777652229817 L1_Test=18.854777652229817 -INFO:pyaf.std:MODEL_L2 L2_Fit=25.076094853478473 L2_Forecast=25.076094853478473 L2_Test=25.076094853478473 +INFO:pyaf.std:MODEL_L1 L1_Fit=18.854777652229835 L1_Forecast=18.854777652229835 L1_Test=18.854777652229835 +INFO:pyaf.std:MODEL_L2 L2_Fit=25.076094853478477 L2_Forecast=25.076094853478477 L2_Test=25.076094853478477 INFO:pyaf.std:MODEL_COMPLEXITY 37 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 67.9155433036289 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_A_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.7891371610531491 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag5 0.6884019292278546 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.3725783965804328 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.3570957200306535 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag4 0.16563878703133955 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.7891371610531487 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag5 0.6884019292278549 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.372578396580432 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.35709572003065254 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_A_Lag1Trend_residue_zeroCycle_residue_Lag4 0.1656387870313394 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_M'), (1, 'Signal_Q'), (2, 'Signal_A')] +INFO:pyaf.std:START_FORECASTING '['Signal_M', 'Signal_Q', 'Signal_A']' RangeIndex: 360 entries, 0 to 359 Data columns (total 2 columns): @@ -153,62 +168,56 @@ Data columns (total 2 columns): 1 Signal 360 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 5.8 KB -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:START_FORECASTING 'Signal_A' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 0.937145471572876 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_A' 1.1655542850494385 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 2.3534209728240967 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_Q', 'Signal_A']' 7.11902642250061 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.648498058319092 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 7.413800954818726 Int64Index: 396 entries, 0 to 395 -Data columns (total 31 columns): +Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_M_start 396 non-null datetime64[ns] - 1 Signal_M 360 non-null float64 - 2 Signal_M_Forecast 396 non-null float64 - 3 Signal_M_Forecast_Lower_Bound 36 non-null float64 - 4 Signal_M_Forecast_Upper_Bound 36 non-null float64 - 5 Date 396 non-null datetime64[ns] - 6 TH_Q_start 25 non-null datetime64[ns] - 7 Signal_Q 361 non-null float64 - 8 Signal_Q_Forecast 396 non-null float64 + 0 TH_A_start 25 non-null datetime64[ns] + 1 TH_M_start 396 non-null datetime64[ns] + 2 TH_Q_start 25 non-null datetime64[ns] + 3 Signal_M 360 non-null float64 + 4 Signal_M_Forecast 396 non-null float64 + 5 Signal_M_Forecast_Lower_Bound 36 non-null float64 + 6 Signal_M_Forecast_Upper_Bound 36 non-null float64 + 7 Signal_Q 23 non-null float64 + 8 Signal_Q_Forecast 25 non-null float64 9 Signal_Q_Forecast_Lower_Bound 1 non-null float64 10 Signal_Q_Forecast_Upper_Bound 1 non-null float64 - 11 TH_A_start 25 non-null datetime64[ns] - 12 Signal_A 361 non-null float64 - 13 Signal_A_Forecast 396 non-null float64 - 14 Signal_A_Forecast_Lower_Bound 1 non-null float64 - 15 Signal_A_Forecast_Upper_Bound 1 non-null float64 - 16 Signal_M_BU_Forecast 396 non-null float64 - 17 Signal_Q_BU_Forecast 396 non-null float64 - 18 Signal_A_BU_Forecast 396 non-null float64 - 19 Signal_A_AHP_TD_Forecast 396 non-null float64 - 20 Signal_Q_AHP_TD_Forecast 396 non-null float64 - 21 Signal_M_AHP_TD_Forecast 396 non-null float64 - 22 Signal_A_PHA_TD_Forecast 396 non-null float64 - 23 Signal_Q_PHA_TD_Forecast 396 non-null float64 - 24 Signal_M_PHA_TD_Forecast 396 non-null float64 - 25 Signal_Q_MO_Forecast 396 non-null float64 - 26 Signal_M_MO_Forecast 396 non-null float64 - 27 Signal_A_MO_Forecast 396 non-null float64 - 28 Signal_M_OC_Forecast 396 non-null float64 - 29 Signal_Q_OC_Forecast 396 non-null float64 - 30 Signal_A_OC_Forecast 396 non-null float64 -dtypes: datetime64[ns](4), float64(27) -memory usage: 119.0 KB - TH_M_start Signal_M ... Signal_Q_OC_Forecast Signal_A_OC_Forecast -391 2033-08-17 NaN ... 6.048980 6.048980 -392 2033-09-16 NaN ... 6.285242 6.285242 -393 2033-10-16 NaN ... 7.150714 7.150714 -394 2033-11-15 NaN ... 7.336162 7.336162 -395 2033-12-15 NaN ... 7.699144 7.699144 + 11 Signal_A 361 non-null float64 + 12 Signal_A_Forecast 396 non-null float64 + 13 Signal_A_Forecast_Lower_Bound 1 non-null float64 + 14 Signal_A_Forecast_Upper_Bound 1 non-null float64 + 15 Signal_M_BU_Forecast 396 non-null float64 + 16 Signal_Q_BU_Forecast 396 non-null float64 + 17 Signal_A_BU_Forecast 25 non-null float64 + 18 Signal_A_AHP_TD_Forecast 396 non-null float64 + 19 Signal_Q_AHP_TD_Forecast 396 non-null float64 + 20 Signal_M_AHP_TD_Forecast 396 non-null float64 + 21 Signal_A_PHA_TD_Forecast 396 non-null float64 + 22 Signal_Q_PHA_TD_Forecast 396 non-null float64 + 23 Signal_M_PHA_TD_Forecast 396 non-null float64 + 24 Signal_Q_MO_Forecast 25 non-null float64 + 25 Signal_M_MO_Forecast 25 non-null float64 + 26 Signal_A_MO_Forecast 25 non-null float64 + 27 Signal_M_OC_Forecast 25 non-null float64 + 28 Signal_Q_OC_Forecast 25 non-null float64 + 29 Signal_A_OC_Forecast 25 non-null float64 +dtypes: datetime64[ns](3), float64(27) +memory usage: 115.9 KB + TH_A_start TH_M_start ... Signal_Q_OC_Forecast Signal_A_OC_Forecast +391 NaT 2033-08-17 ... NaN NaN +392 NaT 2033-09-16 ... NaN NaN +393 NaT 2033-10-16 ... NaN NaN +394 NaT 2033-11-15 ... NaN NaN +395 NaT 2033-12-15 ... NaN NaN -[5 rows x 31 columns] +[5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_2W_M_Q.log b/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_2W_M_Q.log index 571f0e1a9..f0d088d9d 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_2W_M_Q.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_2W_M_Q.log @@ -1,31 +1,18 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.37487053871154785 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.2555201053619385 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'W': 604800.0, '2W': 1209600.0, 'M': 2419200.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-28 00:00:00'), 1: Timestamp('2001-02-04 00:00:00'), 2: Timestamp('2001-02-11 00:00:00'), 3: Timestamp('2001-02-18 00:00:00'), 4: Timestamp('2001-02-25 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA 2W {'TH_2W_start': {0: Timestamp('2001-01-28 00:00:00'), 1: Timestamp('2001-02-11 00:00:00'), 2: Timestamp('2001-02-25 00:00:00'), 3: Timestamp('2001-03-11 00:00:00'), 4: Timestamp('2001-03-25 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: 0.2559389701067345, 2: 7.426331735672635, 3: 13.535884056849122, 4: 16.03047951939704}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA M {'TH_M_start': {0: Timestamp('2001-01-28 00:00:00'), 1: Timestamp('2001-02-25 00:00:00'), 2: Timestamp('2001-03-28 00:00:00'), 3: Timestamp('2001-04-27 00:00:00'), 4: Timestamp('2001-05-28 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: 7.682270705779369, 2: 29.566363576246165, 3: 54.9125974828608, 4: 61.61979017711737}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA Q {'TH_Q_start': {0: Timestamp('2001-01-28 00:00:00'), 1: Timestamp('2001-04-29 00:00:00'), 2: Timestamp('2001-07-30 00:00:00'), 3: Timestamp('2001-10-30 00:00:00'), 4: Timestamp('2002-01-28 00:00:00')}, 'Signal': {0: 39.15353109322899, 1: 172.76079184763768, 2: 101.47037417974255, 3: 135.17045147539451, 4: 187.1071220902626}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'W': 36, '2W': 18, 'M': 9, 'Q': 2} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_W'), (1, 'Signal_2W'), (2, 'Signal_M'), (3, 'Signal_Q')] +INFO:pyaf.std:START_TRAINING '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' -INFO:pyaf.std:START_TRAINING 'Signal_W' -INFO:pyaf.std:START_TRAINING 'Signal_2W' -INFO:pyaf.std:START_TRAINING 'Signal_M' -INFO:pyaf.std:START_TRAINING 'Signal_Q' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_Q' 4.791046619415283 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_M' 6.513915061950684 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_2W' 9.060413599014282 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_W' 12.332218170166016 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' 31.25135564804077 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_W'), (1, 'Signal_2W'), (2, 'Signal_M'), (3, 'Signal_Q')] -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_2W' -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 0.8239243030548096 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 1.0087966918945312 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2W' 1.5456969738006592 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 2.5730836391448975 +INFO:pyaf.std:START_FORECASTING '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' 5.840206623077393 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -34,13 +21,12 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'Signal_W', 'Signal_W_Forecast', - 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', - 'Date', 'TH_2W_start', 'Signal_2W', 'Signal_2W_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_2W_start', 'TH_W_start', 'TH_Q_start', 'TH_M_start', 'Signal_W', + 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', + 'Signal_W_Forecast_Upper_Bound', 'Signal_2W', 'Signal_2W_Forecast', 'Signal_2W_Forecast_Lower_Bound', 'Signal_2W_Forecast_Upper_Bound', - 'TH_M_start', 'Signal_M', 'Signal_M_Forecast', - 'Signal_M_Forecast_Lower_Bound', 'Signal_M_Forecast_Upper_Bound', - 'TH_Q_start', 'Signal_Q', 'Signal_Q_Forecast', + 'Signal_M', 'Signal_M_Forecast', 'Signal_M_Forecast_Lower_Bound', + 'Signal_M_Forecast_Upper_Bound', 'Signal_Q', 'Signal_Q_Forecast', 'Signal_Q_Forecast_Lower_Bound', 'Signal_Q_Forecast_Upper_Bound', 'Signal_W_BU_Forecast', 'Signal_2W_BU_Forecast', 'Signal_M_BU_Forecast', 'Signal_Q_BU_Forecast', 'Signal_Q_AHP_TD_Forecast', @@ -53,54 +39,60 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'Signal_W', 'Signal_ 'Signal_Q_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_2W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_2W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_M']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_M']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (3, ['Signal_Q']) -INFO:pyaf.hierarchical:MODEL_LEVEL (3, ['Signal_Q']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 259, 'MAPE': 184815134.6778, 'RMSE': 19.604196951614746, 'MAE': 12.511121059802496, 'SMAPE': 1.0052, 'ErrorMean': -12.470971637824137, 'ErrorStdDev': 15.126116637335798, 'R2': -0.674164990461847, 'Pearson': 0.06737707580730365} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 65, 'MAPE': 2088190460.5708, 'RMSE': 24.891800096977697, 'MAE': 16.844652436551048, 'SMAPE': 1.0462, 'ErrorMean': -16.427014344535362, 'ErrorStdDev': 18.701735528884168, 'R2': -0.8148129130418285, 'Pearson': -0.15925899717892547} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_BU_Forecast', 'Length': 259, 'MAPE': 62422770984.7541, 'RMSE': 14.05234794373008, 'MAE': 12.554649574102259, 'SMAPE': 1.3407, 'ErrorMean': 3.017749070795792e-16, 'ErrorStdDev': 14.05234794373008, 'R2': 0.13980298492491605, 'Pearson': 0.3755152988386296} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_BU_Forecast', 'Length': 65, 'MAPE': 82574882602.5213, 'RMSE': 17.83909402922174, 'MAE': 16.872359020076644, 'SMAPE': 1.365, 'ErrorMean': -0.3573824996237475, 'ErrorStdDev': 17.83551382866155, 'R2': 0.06789499285945555, 'Pearson': 0.26647359469881626} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_MO_Forecast', 'Length': 259, 'MAPE': 3029051077.0172, 'RMSE': 19.79497362274211, 'MAE': 12.65534045755681, 'SMAPE': 1.0058, 'ErrorMean': -11.934599591292868, 'ErrorStdDev': 15.792603120466493, 'R2': -0.7069075395784887, 'Pearson': -0.01570415961183796} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_MO_Forecast', 'Length': 65, 'MAPE': 10369810649.5111, 'RMSE': 25.4435850319944, 'MAE': 17.472615569611683, 'SMAPE': 1.021, 'ErrorMean': -15.081578287110265, 'ErrorStdDev': 20.49199882027358, 'R2': -0.8961638363820568, 'Pearson': -0.1046231184293304} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_OC_Forecast', 'Length': 259, 'MAPE': 18430768533.0716, 'RMSE': 12.919957268064627, 'MAE': 9.65707068577512, 'SMAPE': 1.4489, 'ErrorMean': -5.639676720492743, 'ErrorStdDev': 11.624084578879671, 'R2': 0.27285286639780804, 'Pearson': 0.6974215288157001} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_OC_Forecast', 'Length': 65, 'MAPE': 41591647402.0359, 'RMSE': 19.824064090647706, 'MAE': 14.412073245218838, 'SMAPE': 1.4546, 'ErrorMean': -6.053188721238654, 'ErrorStdDev': 18.877299154677214, 'R2': -0.15107769334633314, 'Pearson': 0.2782856656856752} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_PHA_TD_Forecast', 'Length': 259, 'MAPE': 995518085.169, 'RMSE': 19.53792115983047, 'MAE': 12.489703590460282, 'SMAPE': 1.0, 'ErrorMean': -12.270073606325479, 'ErrorStdDev': 15.204461744603327, 'R2': -0.6628644454389436, 'Pearson': 0.06737707580730365} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_PHA_TD_Forecast', 'Length': 65, 'MAPE': 11248166318.0865, 'RMSE': 25.687843625434027, 'MAE': 17.76065002230262, 'SMAPE': 1.0462, 'ErrorMean': -15.511016758783791, 'ErrorStdDev': 20.4761732077427, 'R2': -0.9327449591545331, 'Pearson': -0.15925899717892544} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_AHP_TD_Forecast', 'Length': 259, 'MAPE': 645664229.4101, 'RMSE': 10.351484966614443, 'MAE': 1.8314959091642895, 'SMAPE': 0.0868, 'ErrorMean': -1.7023630632895113, 'ErrorStdDev': 10.21054361994465, 'R2': -0.03552478626914879, 'Pearson': -0.015075772847508565} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_AHP_TD_Forecast', 'Length': 65, 'MAPE': 1761404128.4755, 'RMSE': 13.580484359109493, 'MAE': 2.931757519576251, 'SMAPE': 0.1178, 'ErrorMean': -2.579476693890098, 'ErrorStdDev': 13.333261244485362, 'R2': 0.07557600471635195, 'Pearson': 0.495211058611211} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_BU_Forecast', 'Length': 259, 'MAPE': 120260145771.9832, 'RMSE': 20.937443844783346, 'MAE': 13.39086620290186, 'SMAPE': 1.0041, 'ErrorMean': 10.72980719837389, 'ErrorStdDev': 17.979093198467485, 'R2': -3.236454108812053, 'Pearson': 0.007662419871430583} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_BU_Forecast', 'Length': 65, 'MAPE': 163345362835.4809, 'RMSE': 28.266012858732676, 'MAE': 18.84211761424819, 'SMAPE': 1.0234, 'ErrorMean': 13.826954952840566, 'ErrorStdDev': 24.653251300024483, 'R2': -3.0046982218106377, 'Pearson': -0.12147622547034424} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_MO_Forecast', 'Length': 259, 'MAPE': 0.0211, 'RMSE': 6.576028178371982, 'MAE': 0.9247070047900701, 'SMAPE': 0.0286, 'ErrorMean': -0.7003356398657797, 'ErrorStdDev': 6.53862956545759, 'R2': 0.5820902359609728, 'Pearson': 0.7660516765875351} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_MO_Forecast', 'Length': 65, 'MAPE': 0.0115, 'RMSE': 3.802322573025479, 'MAE': 0.6994954170653832, 'SMAPE': 0.0107, 'ErrorMean': -0.03479615045792632, 'ErrorStdDev': 3.8021633548878992, 'R2': 0.9275332797472107, 'Pearson': 0.9636966264896041} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_OC_Forecast', 'Length': 259, 'MAPE': 63533936110.5718, 'RMSE': 11.817400433794523, 'MAE': 7.6642477678584155, 'SMAPE': 1.9653, 'ErrorMean': 5.105678514060379, 'ErrorStdDev': 10.657532543872863, 'R2': -0.34958142098333345, 'Pearson': 0.23804654136908585} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_OC_Forecast', 'Length': 65, 'MAPE': 88781434268.9438, 'RMSE': 13.725263565697977, 'MAE': 10.06796187655318, 'SMAPE': 1.9368, 'ErrorMean': 7.688324977231344, 'ErrorStdDev': 11.36980734191908, 'R2': 0.055760690677669644, 'Pearson': 0.6245311356433255} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 259, 'MAPE': 4243906339.4885, 'RMSE': 11.423164521824761, 'MAE': 2.2105784095650276, 'SMAPE': 0.0896, 'ErrorMean': -1.3175418124349585, 'ErrorStdDev': 11.34692783378662, 'R2': -0.26103764248986994, 'Pearson': -0.015075772847508568} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 65, 'MAPE': 11577587555.438, 'RMSE': 13.167892552241797, 'MAE': 3.1403959438568565, 'SMAPE': 0.0996, 'ErrorMean': -0.824878432776594, 'ErrorStdDev': 13.14203065886414, 'R2': 0.13089301813662269, 'Pearson': 0.495211058611211} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.0025, 'RMSE': 2.294838725457077, 'MAE': 0.2425101178448236, 'SMAPE': 0.0029, 'ErrorMean': -0.2425101178448236, 'ErrorStdDev': 2.281988961103964, 'R2': 0.9826033671129429, 'Pearson': 0.9988950001546018} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.0022, 'RMSE': 3.234338161429145, 'MAE': 0.5163804934370652, 'SMAPE': 0.0023, 'ErrorMean': -0.5163804934370652, 'ErrorStdDev': 3.1928505333752404, 'R2': 0.9933407047011198, 'Pearson': 0.9996926813854645} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 259, 'MAPE': 11850886889.5847, 'RMSE': 19.136265733852, 'MAE': 2.8366969062395775, 'SMAPE': 0.0917, 'ErrorMean': -0.5907330208908879, 'ErrorStdDev': 19.127145650478724, 'R2': -0.20969321601963542, 'Pearson': -0.010211349161543455} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 65, 'MAPE': 17446815585.1639, 'RMSE': 36.96846641215745, 'MAE': 7.61754966861926, 'SMAPE': 0.1064, 'ErrorMean': -4.1281865515914795, 'ErrorStdDev': 36.7372506410329, 'R2': 0.1299979152000419, 'Pearson': 0.3782251946385122} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 259, 'MAPE': 11850886889.5847, 'RMSE': 19.136265733852, 'MAE': 2.8366969062395775, 'SMAPE': 0.0917, 'ErrorMean': -0.5907330208908879, 'ErrorStdDev': 19.127145650478724, 'R2': -0.20969321601963542, 'Pearson': -0.010211349161543455} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 65, 'MAPE': 17446815585.1639, 'RMSE': 36.96846641215745, 'MAE': 7.61754966861926, 'SMAPE': 0.1064, 'ErrorMean': -4.1281865515914795, 'ErrorStdDev': 36.7372506410329, 'R2': 0.1299979152000419, 'Pearson': 0.3782251946385122} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 259, 'MAPE': 64315208469.3725, 'RMSE': 15.074282056786034, 'MAE': 7.64776056083752, 'SMAPE': 1.9904, 'ErrorMean': 5.21528113303527, 'ErrorStdDev': 14.143366721928256, 'R2': 0.24935580023546566, 'Pearson': 0.6442345755580912} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 65, 'MAPE': 85099855447.3034, 'RMSE': 30.51569840480917, 'MAE': 13.425036513358604, 'SMAPE': 1.9714, 'ErrorMean': 3.5949345760977915, 'ErrorStdDev': 30.303206010698755, 'R2': 0.40720565545618415, 'Pearson': 0.8572879697624659} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 259, 'MAPE': 0.0025, 'RMSE': 2.294838725457077, 'MAE': 0.2425101178448236, 'SMAPE': 0.0029, 'ErrorMean': -0.2425101178448236, 'ErrorStdDev': 2.281988961103964, 'R2': 0.9826033671129429, 'Pearson': 0.9988950001546018} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 65, 'MAPE': 0.0022, 'RMSE': 3.234338161429145, 'MAE': 0.5163804934370652, 'SMAPE': 0.0023, 'ErrorMean': -0.5163804934370652, 'ErrorStdDev': 3.1928505333752404, 'R2': 0.9933407047011198, 'Pearson': 0.9996926813854645} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.9979, 'RMSE': 14.064813213331014, 'MAE': 12.501148084980345, 'SMAPE': 1.993, 'ErrorMean': -12.493027223653764, 'ErrorStdDev': 6.460900983217171, 'R2': -3.7191233395886334, 'Pearson': 0.0727826359701955} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.9953, 'RMSE': 17.67439567969688, 'MAE': 16.754507258381523, 'SMAPE': 1.9839, 'ErrorMean': -16.754507258381523, 'ErrorStdDev': 5.627677067079157, 'R2': -8.550165270473544, 'Pearson': 0.20193523619142248} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 259, 'MAPE': 0.3608, 'RMSE': 1.8075756006784895, 'MAE': 1.2707655069523887, 'SMAPE': 0.1559, 'ErrorMean': -2.400482215405744e-16, 'ErrorStdDev': 1.8075756006784895, 'R2': 0.9220553464049209, 'Pearson': 0.9602371363938411} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 65, 'MAPE': 0.0933, 'RMSE': 1.7464635036586067, 'MAE': 1.3119893773007625, 'SMAPE': 0.1004, 'ErrorMean': -0.5843126393025114, 'ErrorStdDev': 1.6458169731664665, 'R2': 0.9067517329439065, 'Pearson': 0.9602369974911369} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 259, 'MAPE': 0.9933, 'RMSE': 14.002746716547808, 'MAE': 12.374394337352268, 'SMAPE': 1.9583, 'ErrorMean': -12.224560993988629, 'ErrorStdDev': 6.829130553155516, 'R2': -3.677565278010868, 'Pearson': -0.008207058038190173} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 65, 'MAPE': 0.972, 'RMSE': 17.45491040344237, 'MAE': 16.385351185123675, 'SMAPE': 1.9235, 'ErrorMean': -16.082635486027833, 'ErrorStdDev': 6.784005676277037, 'R2': -8.31444512336017, 'Pearson': -0.011828455609973094} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 259, 'MAPE': 0.7525, 'RMSE': 8.685992704560448, 'MAE': 6.841717526650219, 'SMAPE': 0.7795, 'ErrorMean': -5.6396767204927425, 'ErrorStdDev': 6.606096862142541, 'R2': -0.7998334168118142, 'Pearson': 0.4748942525127652} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 65, 'MAPE': 0.5729, 'RMSE': 12.391920498762858, 'MAE': 9.677407118512493, 'SMAPE': 0.7715, 'ErrorMean': -6.280118860917417, 'ErrorStdDev': 10.682686962576804, 'R2': -3.6946041417483624, 'Pearson': 0.39700618339599025} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 259, 'MAPE': 0.99, 'RMSE': 14.000706362632474, 'MAE': 12.402443539965942, 'SMAPE': 1.9785, 'ErrorMean': -12.392945634738592, 'ErrorStdDev': 6.514190444488953, 'R2': -3.6762022321031296, 'Pearson': 0.07278263597019549} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 65, 'MAPE': 0.9757, 'RMSE': 17.353828661002087, 'MAE': 16.298183753663192, 'SMAPE': 1.9462, 'ErrorMean': -16.298183753663192, 'ErrorStdDev': 5.960249619541676, 'R2': -8.206877208146322, 'Pearson': 0.20193523619142245} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 15.525006771087646 +/usr/lib/python3/dist-packages/numpy/core/fromnumeric.py:3372: RuntimeWarning: Mean of empty slice. + return _methods._mean(a, axis=axis, dtype=dtype, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +/usr/lib/python3/dist-packages/numpy/core/_methods.py:233: RuntimeWarning: Degrees of freedom <= 0 for slice + ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof, +/usr/lib/python3/dist-packages/numpy/core/_methods.py:194: RuntimeWarning: invalid value encountered in true_divide + arrmean = um.true_divide( +/usr/lib/python3/dist-packages/numpy/core/_methods.py:226: RuntimeWarning: invalid value encountered in double_scalars + ret = ret.dtype.type(ret / rcount) +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 6, 'MAPE': 0.9326, 'RMSE': 21.728165979134875, 'MAE': 18.414159393365953, 'SMAPE': 1.808, 'ErrorMean': -18.414159393365953, 'ErrorStdDev': 11.533946880948722, 'R2': -2.668999356163141, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_AHP_TD_Forecast', 'Length': 1, 'MAPE': 1.0, 'RMSE': 23.317870041845083, 'MAE': 23.317870041845083, 'SMAPE': 2.0, 'ErrorMean': -23.317870041845083, 'ErrorStdDev': 0.0, 'R2': -5437230632882.765, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_BU_Forecast', 'Length': 6, 'MAPE': 0.6473, 'RMSE': 13.274980795281872, 'MAE': 10.771139341166196, 'SMAPE': 0.7681, 'ErrorMean': -10.01776535960562, 'ErrorStdDev': 8.710309541858326, 'R2': -0.3695228988651553, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_BU_Forecast', 'Length': 1, 'MAPE': 0.5048, 'RMSE': 11.771586350794337, 'MAE': 11.771586350794337, 'SMAPE': 0.6753, 'ErrorMean': -11.771586350794337, 'ErrorStdDev': 0.0, 'R2': -1385702452141.0752, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_MO_Forecast', 'Length': 6, 'MAPE': 0.5758, 'RMSE': 17.277621849901333, 'MAE': 11.311246455588991, 'SMAPE': 0.7486, 'ErrorMean': -6.487619412857839, 'ErrorStdDev': 16.013338538296694, 'R2': -1.3199008512889434, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_MO_Forecast', 'Length': 1, 'MAPE': 0.4419, 'RMSE': 10.304942462618406, 'MAE': 10.304942462618406, 'SMAPE': 0.362, 'ErrorMean': 10.304942462618406, 'ErrorStdDev': 0.0, 'R2': -1061918391577.759, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_OC_Forecast', 'Length': 6, 'MAPE': 1.0222, 'RMSE': 10.689430870418192, 'MAE': 8.36371807568267, 'SMAPE': 0.6337, 'ErrorMean': -4.743474710803223, 'ErrorStdDev': 9.579320435261558, 'R2': 0.11200471202514395, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_OC_Forecast', 'Length': 1, 'MAPE': 0.0565, 'RMSE': 1.3180389180796546, 'MAE': 1.3180389180796546, 'SMAPE': 0.055, 'ErrorMean': 1.3180389180796546, 'ErrorStdDev': 0.0, 'R2': -17372265894.725864, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_2W_PHA_TD_Forecast', 'Length': 6, 'MAPE': 1.0298, 'RMSE': 21.742564806447106, 'MAE': 18.59935005501401, 'SMAPE': 1.7903, 'ErrorMean': -17.85084126117022, 'ErrorStdDev': 12.413161991654466, 'R2': -2.673863714494424, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_2W_PHA_TD_Forecast', 'Length': 1, 'MAPE': 1.0, 'RMSE': 23.317870041845083, 'MAE': 23.317870041845083, 'SMAPE': 2.0, 'ErrorMean': -23.317870041845083, 'ErrorStdDev': 0.0, 'R2': -5437230632882.765, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_AHP_TD_Forecast', 'Length': 6, 'MAPE': 0.8984, 'RMSE': 45.74558152297604, 'MAE': 36.5487770596126, 'SMAPE': 1.7474, 'ErrorMean': -36.5487770596126, 'ErrorStdDev': 27.510818314291985, 'R2': -1.8158649898815735, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_AHP_TD_Forecast', 'Length': 1, 'MAPE': 1.0, 'RMSE': 42.784676995021144, 'MAE': 42.784676995021144, 'SMAPE': 2.0, 'ErrorMean': -42.784676995021144, 'ErrorStdDev': 0.0, 'R2': -18305285855681.914, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_BU_Forecast', 'Length': 6, 'MAPE': 1.0236, 'RMSE': 24.421856397727975, 'MAE': 19.192416080523397, 'SMAPE': 0.6753, 'ErrorMean': -16.229272756570015, 'ErrorStdDev': 18.249322609458716, 'R2': 0.19745227290010103, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_BU_Forecast', 'Length': 1, 'MAPE': 0.4125, 'RMSE': 17.646683506010284, 'MAE': 17.646683506010284, 'SMAPE': 0.5196, 'ErrorMean': -17.646683506010284, 'ErrorStdDev': 0.0, 'R2': -3114054387611.9536, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_MO_Forecast', 'Length': 6, 'MAPE': 0.5935, 'RMSE': 31.934981854001215, 'MAE': 21.63750180020112, 'SMAPE': 0.7142, 'ErrorMean': -14.563646044168456, 'ErrorStdDev': 28.420824757834943, 'R2': -0.3722930697143514, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_MO_Forecast', 'Length': 1, 'MAPE': 0.4458, 'RMSE': 19.0746816646162, 'MAE': 19.0746816646162, 'SMAPE': 0.3646, 'ErrorMean': 19.0746816646162, 'ErrorStdDev': 0.0, 'R2': -3638434806063.454, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_OC_Forecast', 'Length': 6, 'MAPE': 1.2105, 'RMSE': 32.48278859897112, 'MAE': 25.66879749693285, 'SMAPE': 0.9706, 'ErrorMean': -22.943294748362206, 'ErrorStdDev': 22.99427714139376, 'R2': -0.41977699220697984, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_OC_Forecast', 'Length': 1, 'MAPE': 0.4242, 'RMSE': 18.148768035096406, 'MAE': 18.148768035096406, 'SMAPE': 0.5384, 'ErrorMean': -18.148768035096406, 'ErrorStdDev': 0.0, 'R2': -3293777811916.3706, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 6, 'MAPE': 1.3348, 'RMSE': 45.80437057367682, 'MAE': 37.38009321844972, 'SMAPE': 1.8669, 'ErrorMean': -35.46973817285247, 'ErrorStdDev': 28.98168451971008, 'R2': -1.8231071494831435, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 1, 'MAPE': 1.0, 'RMSE': 42.784676995021144, 'MAE': 42.784676995021144, 'SMAPE': 2.0, 'ErrorMean': -42.784676995021144, 'ErrorStdDev': 0.0, 'R2': -18305285855681.914, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 1, 'MAPE': 0.3901, 'RMSE': 15.275188881393447, 'MAE': 15.275188881393447, 'SMAPE': 0.4847, 'ErrorMean': -15.275188881393447, 'ErrorStdDev': 0.0, 'R2': -2333313953621.46, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 1, 'MAPE': 0.9325, 'RMSE': 36.51067212362493, 'MAE': 36.51067212362493, 'SMAPE': 1.7471, 'ErrorMean': -36.51067212362493, 'ErrorStdDev': 0.0, 'R2': -13330291789187.424, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 1, 'MAPE': 0.9325, 'RMSE': 36.51067212362493, 'MAE': 36.51067212362493, 'SMAPE': 1.7471, 'ErrorMean': -36.51067212362493, 'ErrorStdDev': 0.0, 'R2': -13330291789187.424, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 1, 'MAPE': 0.7425, 'RMSE': 29.072126036313605, 'MAE': 29.072126036313605, 'SMAPE': 1.181, 'ErrorMean': -29.072126036313605, 'ErrorStdDev': 0.0, 'R2': -8451885122712.034, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 1, 'MAPE': 0.3901, 'RMSE': 15.275188881393447, 'MAE': 15.275188881393447, 'SMAPE': 0.4847, 'ErrorMean': -15.275188881393447, 'ErrorStdDev': 0.0, 'R2': -2333313953621.46, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 0, 'MAPE': None, 'RMSE': nan, 'MAE': nan, 'SMAPE': None, 'ErrorMean': nan, 'ErrorStdDev': nan, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 6, 'MAPE': 0.9651, 'RMSE': 9.401572257513607, 'MAE': 7.95691815459922, 'SMAPE': 1.8844, 'ErrorMean': -7.95691815459922, 'ErrorStdDev': 5.007695517327193, 'R2': -2.646410029475135, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 1, 'MAPE': 1.0, 'RMSE': 11.912826793784575, 'MAE': 11.912826793784575, 'SMAPE': 2.0, 'ErrorMean': -11.912826793784575, 'ErrorStdDev': 0.0, 'R2': -1419154422186.1165, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 6, 'MAPE': 0.3159, 'RMSE': 1.7685424241954468, 'MAE': 1.4843785622268288, 'SMAPE': 0.2417, 'ErrorMean': 0.5013197475223127, 'ErrorStdDev': 1.696001420082917, 'R2': 0.870968575960472, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 1, 'MAPE': 0.0308, 'RMSE': 0.3665431027338286, 'MAE': 0.3665431027338286, 'SMAPE': 0.0312, 'ErrorMean': -0.3665431027338286, 'ErrorStdDev': 0.0, 'R2': -1343538460.6174202, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 6, 'MAPE': 1.0662, 'RMSE': 9.845033993892581, 'MAE': 7.392233532283323, 'SMAPE': 1.014, 'ErrorMean': -1.972741707568116, 'ErrorStdDev': 9.645360775840464, 'R2': -2.998517176653303, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 1, 'MAPE': 0.4167, 'RMSE': 4.963488362895941, 'MAE': 4.963488362895941, 'SMAPE': 0.3448, 'ErrorMean': 4.963488362895941, 'ErrorStdDev': 0.0, 'R2': -246362167285.03424, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 6, 'MAPE': 1.806, 'RMSE': 10.088559902162848, 'MAE': 8.090127007128357, 'SMAPE': 0.7233, 'ErrorMean': 5.775610396324709, 'ErrorStdDev': 8.271720827578381, 'R2': -3.1987776802132215, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 1, 'MAPE': 1.068, 'RMSE': 12.723082166140163, 'MAE': 12.723082166140163, 'SMAPE': 0.6962, 'ErrorMean': 12.723082166140163, 'ErrorStdDev': 0.0, 'R2': -1618768198062.5386, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 6, 'MAPE': 0.8489, 'RMSE': 9.3817446808881, 'MAE': 7.735731748489172, 'SMAPE': 1.6816, 'ErrorMean': -7.676289353439198, 'ErrorStdDev': 5.393673610782192, 'R2': -2.631045953382735, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 1, 'MAPE': 1.0, 'RMSE': 11.912826793784575, 'MAE': 11.912826793784575, 'SMAPE': 2.0, 'ErrorMean': -11.912826793784575, 'ErrorStdDev': 0.0, 'R2': -1419154422186.1165, 'Pearson': 0.0} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 38.797916412353516 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_W_start' TimeMin=2001-01-28T00:00:00.000000 TimeMax=2006-01-08T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_W' Length=360 Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_W' Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 @@ -112,20 +104,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3608 MAPE_Forecast=0.0933 MAPE_Test=0.0727 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1559 SMAPE_Forecast=0.1004 SMAPE_Test=0.0754 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5456 MASE_Forecast=0.6072 MASE_Test=0.5062 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.2707655069523887 L1_Forecast=1.3119893773007625 L1_Test=1.269372776026798 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.7464635036586067 L2_Test=1.4940617790422326 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.270765506952389 L1_Forecast=1.3119893773007616 L1_Test=1.269372776026798 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.746463503658606 L2_Test=1.4940617790422324 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12.516770159495985 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_W_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529058 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969627 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630132 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067375 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684192 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395425 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1522696318163203 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070928 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag56 0.14777440880242368 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529054 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969634 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813113 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630128 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067456 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684117 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395358 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.15226963181632097 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070792 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag56 0.1477744088024236 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_2W_start' TimeMin=2001-01-28T00:00:00.000000 TimeMax=2005-12-25T00:00:00.000000 TimeDelta= Horizon=18 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_2W' Length=180 Min=-0.41263817098822364 Max=54.50574426645973 Mean=27.79443879544697 StdDev=12.710746738178202 @@ -138,20 +139,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_2W_PolyTrend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.8964 MAPE_Forecast=0.0527 MAPE_Test=0.0492 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.143 SMAPE_Forecast=0.0527 SMAPE_Test=0.0476 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3316 MASE_Forecast=0.2436 MASE_Test=0.2129 -INFO:pyaf.std:MODEL_L1 L1_Fit=2.3053562564736425 L1_Forecast=1.591857632823238 L1_Test=1.6231680227323801 -INFO:pyaf.std:MODEL_L2 L2_Fit=3.8151287861216696 L2_Forecast=2.009635496660876 L2_Test=2.0847200807291975 +INFO:pyaf.std:MODEL_L1 L1_Fit=2.3053562564736425 L1_Forecast=1.5918576328232352 L1_Test=1.623168022732378 +INFO:pyaf.std:MODEL_L2 L2_Fit=3.8151287861216696 L2_Forecast=2.0096354966608727 L2_Test=2.084720080729197 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (18.706255917598163, array([10.96460892, 2.61971852, -0.47456439])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_2W_PolyTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag21 0.6307297929424716 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag1 0.5615488467733352 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag22 -0.46569582858441344 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag10 0.3982865569582167 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag12 -0.18902076610922425 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag31 -0.18532476993292146 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag20 -0.152841188091913 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag42 0.1330709400010971 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag33 0.11515149464400948 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag7 -0.11432784499671368 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag21 0.6307297929424714 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag1 0.5615488467733346 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag22 -0.46569582858441355 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag10 0.398286556958216 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag12 -0.1890207661092233 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag31 -0.18532476993292182 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag20 -0.15284118809191252 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag42 0.13307094000109737 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag33 0.11515149464400884 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_2W_PolyTrend_residue_zeroCycle_residue_Lag7 -0.11432784499671315 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_M_start' TimeMin=2001-01-28T00:00:00.000000 TimeMax=2007-10-28T00:00:00.000000 TimeDelta= Horizon=9 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_M' Length=14 Min=1.9048968112034605 Max=96.32920484609802 Mean=52.62443024801729 StdDev=27.28407216591746 @@ -164,13 +174,22 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Signal_M_Lag1Trend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.4433 MAPE_Forecast=0.4433 MAPE_Test=0.4433 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.5793 SMAPE_Forecast=0.5793 SMAPE_Test=0.5793 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6609 MASE_Forecast=0.6609 MASE_Test=0.6609 -INFO:pyaf.std:MODEL_L1 L1_Fit=20.35473688213415 L1_Forecast=20.35473688213415 L1_Test=20.35473688213415 -INFO:pyaf.std:MODEL_L2 L2_Fit=29.447267241408845 L2_Forecast=29.447267241408845 L2_Test=29.447267241408845 +INFO:pyaf.std:MODEL_L1 L1_Fit=20.354736882134148 L1_Forecast=20.354736882134148 L1_Test=20.354736882134148 +INFO:pyaf.std:MODEL_L2 L2_Fit=29.44726724140883 L2_Forecast=29.44726724140883 L2_Test=29.44726724140883 INFO:pyaf.std:MODEL_COMPLEXITY 67 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 1.9048968112034605 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 0.0 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_Signal_M_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_Signal_M_Lag1Trend_residue_zeroCycle_residue_Lag1 -1.365240735629916 -INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_Signal_M_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.9484405479957252 -INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_Signal_M_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.47333327432668887 +INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_Signal_M_Lag1Trend_residue_zeroCycle_residue_Lag1 -1.3652407356299154 +INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_Signal_M_Lag1Trend_residue_zeroCycle_residue_Lag2 -0.9484405479957255 +INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_Signal_M_Lag1Trend_residue_zeroCycle_residue_Lag3 -0.4733332743266887 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_Q_start' TimeMin=2001-01-28T00:00:00.000000 TimeMax=2007-04-29T00:00:00.000000 TimeDelta= Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_Q' Length=6 Min=39.15353109322899 Max=272.5452226620775 Mean=192.85929432770362 StdDev=76.93391233657083 @@ -183,13 +202,22 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Signal_Q_PolyTrend_residue_Seasonal_DayOfMont INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1297 MAPE_Forecast=0.1297 MAPE_Test=0.1297 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1492 SMAPE_Forecast=0.1492 SMAPE_Test=0.1492 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2207 MASE_Forecast=0.2207 MASE_Test=0.2207 -INFO:pyaf.std:MODEL_L1 L1_Fit=16.062475432536434 L1_Forecast=16.062475432536434 L1_Test=16.062475432536434 +INFO:pyaf.std:MODEL_L1 L1_Fit=16.062475432536427 L1_Forecast=16.062475432536427 L1_Test=16.062475432536427 INFO:pyaf.std:MODEL_L2 L2_Fit=18.456837369965267 L2_Forecast=18.456837369965267 L2_Test=18.456837369965267 INFO:pyaf.std:MODEL_COMPLEXITY 52 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 39.15353109322899 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (59.631883409618226, array([-11.54796233, -13.6024255 , -12.5591831 ])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Diff_Signal_Q_PolyTrend_residue_Seasonal_DayOfMonth 9.59353799860087 {28: -74.90707229101167, 29: 65.3135342924108, 30: 9.59353799860087} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_W'), (1, 'Signal_2W'), (2, 'Signal_M'), (3, 'Signal_Q')] +INFO:pyaf.std:START_FORECASTING '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' RangeIndex: 360 entries, 0 to 359 Data columns (total 2 columns): @@ -199,74 +227,66 @@ Data columns (total 2 columns): 1 Signal 360 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 5.8 KB -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_2W' -INFO:pyaf.std:START_FORECASTING 'Signal_M' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_M' 0.8302743434906006 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 1.2629504203796387 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_2W' 1.738013744354248 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 2.1431503295898438 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' 7.440672874450684 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.4071221351623535 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 7.77457332611084 Int64Index: 396 entries, 0 to 395 -Data columns (total 41 columns): +Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_W_start 396 non-null datetime64[ns] - 1 Signal_W 360 non-null float64 - 2 Signal_W_Forecast 396 non-null float64 - 3 Signal_W_Forecast_Lower_Bound 36 non-null float64 - 4 Signal_W_Forecast_Upper_Bound 36 non-null float64 - 5 Date 396 non-null datetime64[ns] - 6 TH_2W_start 198 non-null datetime64[ns] - 7 Signal_2W 361 non-null float64 - 8 Signal_2W_Forecast 396 non-null float64 - 9 Signal_2W_Forecast_Lower_Bound 18 non-null float64 - 10 Signal_2W_Forecast_Upper_Bound 18 non-null float64 - 11 TH_M_start 14 non-null datetime64[ns] - 12 Signal_M 361 non-null float64 - 13 Signal_M_Forecast 396 non-null float64 + 0 TH_2W_start 198 non-null datetime64[ns] + 1 TH_W_start 396 non-null datetime64[ns] + 2 TH_Q_start 6 non-null datetime64[ns] + 3 TH_M_start 14 non-null datetime64[ns] + 4 Signal_W 360 non-null float64 + 5 Signal_W_Forecast 396 non-null float64 + 6 Signal_W_Forecast_Lower_Bound 36 non-null float64 + 7 Signal_W_Forecast_Upper_Bound 36 non-null float64 + 8 Signal_2W 180 non-null float64 + 9 Signal_2W_Forecast 198 non-null float64 + 10 Signal_2W_Forecast_Lower_Bound 18 non-null float64 + 11 Signal_2W_Forecast_Upper_Bound 18 non-null float64 + 12 Signal_M 14 non-null float64 + 13 Signal_M_Forecast 14 non-null float64 14 Signal_M_Forecast_Lower_Bound 0 non-null float64 15 Signal_M_Forecast_Upper_Bound 0 non-null float64 - 16 TH_Q_start 6 non-null datetime64[ns] - 17 Signal_Q 361 non-null float64 - 18 Signal_Q_Forecast 396 non-null float64 - 19 Signal_Q_Forecast_Lower_Bound 0 non-null float64 - 20 Signal_Q_Forecast_Upper_Bound 0 non-null float64 - 21 Signal_W_BU_Forecast 396 non-null float64 - 22 Signal_2W_BU_Forecast 396 non-null float64 - 23 Signal_M_BU_Forecast 396 non-null float64 - 24 Signal_Q_BU_Forecast 396 non-null float64 - 25 Signal_Q_AHP_TD_Forecast 396 non-null float64 - 26 Signal_M_AHP_TD_Forecast 396 non-null float64 - 27 Signal_2W_AHP_TD_Forecast 396 non-null float64 - 28 Signal_W_AHP_TD_Forecast 396 non-null float64 - 29 Signal_Q_PHA_TD_Forecast 396 non-null float64 - 30 Signal_M_PHA_TD_Forecast 396 non-null float64 - 31 Signal_2W_PHA_TD_Forecast 396 non-null float64 - 32 Signal_W_PHA_TD_Forecast 396 non-null float64 - 33 Signal_M_MO_Forecast 396 non-null float64 - 34 Signal_2W_MO_Forecast 396 non-null float64 - 35 Signal_W_MO_Forecast 396 non-null float64 - 36 Signal_Q_MO_Forecast 396 non-null float64 - 37 Signal_W_OC_Forecast 396 non-null float64 - 38 Signal_2W_OC_Forecast 396 non-null float64 - 39 Signal_M_OC_Forecast 396 non-null float64 - 40 Signal_Q_OC_Forecast 396 non-null float64 -dtypes: datetime64[ns](5), float64(36) -memory usage: 149.9 KB - TH_W_start Signal_W ... Signal_M_OC_Forecast Signal_Q_OC_Forecast -391 2008-07-27 NaN ... 4.536735 4.536735 -392 2008-08-03 NaN ... 16.412255 16.412255 -393 2008-08-10 NaN ... 5.363035 5.363035 -394 2008-08-17 NaN ... 18.758436 18.758436 -395 2008-08-24 NaN ... 5.774358 5.774358 + 16 Signal_Q 361 non-null float64 + 17 Signal_Q_Forecast 396 non-null float64 + 18 Signal_Q_Forecast_Lower_Bound 0 non-null float64 + 19 Signal_Q_Forecast_Upper_Bound 0 non-null float64 + 20 Signal_W_BU_Forecast 396 non-null float64 + 21 Signal_2W_BU_Forecast 396 non-null float64 + 22 Signal_M_BU_Forecast 198 non-null float64 + 23 Signal_Q_BU_Forecast 14 non-null float64 + 24 Signal_Q_AHP_TD_Forecast 396 non-null float64 + 25 Signal_M_AHP_TD_Forecast 396 non-null float64 + 26 Signal_2W_AHP_TD_Forecast 396 non-null float64 + 27 Signal_W_AHP_TD_Forecast 396 non-null float64 + 28 Signal_Q_PHA_TD_Forecast 396 non-null float64 + 29 Signal_M_PHA_TD_Forecast 396 non-null float64 + 30 Signal_2W_PHA_TD_Forecast 396 non-null float64 + 31 Signal_W_PHA_TD_Forecast 396 non-null float64 + 32 Signal_M_MO_Forecast 14 non-null float64 + 33 Signal_2W_MO_Forecast 14 non-null float64 + 34 Signal_W_MO_Forecast 14 non-null float64 + 35 Signal_Q_MO_Forecast 14 non-null float64 + 36 Signal_W_OC_Forecast 8 non-null float64 + 37 Signal_2W_OC_Forecast 8 non-null float64 + 38 Signal_M_OC_Forecast 8 non-null float64 + 39 Signal_Q_OC_Forecast 8 non-null float64 +dtypes: datetime64[ns](4), float64(36) +memory usage: 146.8 KB + TH_2W_start TH_W_start ... Signal_M_OC_Forecast Signal_Q_OC_Forecast +391 NaT 2008-07-27 ... NaN NaN +392 2008-08-03 2008-08-03 ... NaN NaN +393 NaT 2008-08-10 ... NaN NaN +394 2008-08-17 2008-08-17 ... NaN NaN +395 NaT 2008-08-24 ... NaN NaN -[5 rows x 41 columns] +[5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_Q_A.log b/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_Q_A.log index 9dd16baeb..05ada8883 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_Q_A.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_Q_A.log @@ -1,26 +1,17 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3772878646850586 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.9521405696868896 INFO:pyaf.std:START_HIERARCHICAL_TRAINING +INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'W': 604800.0, 'Q': 7862400.0, 'A': 31536000.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-28 00:00:00'), 1: Timestamp('2001-02-04 00:00:00'), 2: Timestamp('2001-02-11 00:00:00'), 3: Timestamp('2001-02-18 00:00:00'), 4: Timestamp('2001-02-25 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA Q {'TH_Q_start': {0: Timestamp('2001-01-28 00:00:00'), 1: Timestamp('2001-04-29 00:00:00'), 2: Timestamp('2001-07-30 00:00:00'), 3: Timestamp('2001-10-30 00:00:00'), 4: Timestamp('2002-01-28 00:00:00')}, 'Signal': {0: 39.15353109322899, 1: 172.76079184763768, 2: 101.47037417974255, 3: 135.17045147539451, 4: 187.1071220902626}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA A {'TH_A_start': {0: Timestamp('2001-01-28 00:00:00'), 1: Timestamp('2002-01-28 00:00:00'), 2: Timestamp('2003-01-28 00:00:00'), 3: Timestamp('2004-01-29 00:00:00'), 4: Timestamp('2005-01-28 00:00:00')}, 'Signal': {0: 448.5551485960038, 1: 614.4802658251592, 2: 638.8309639512009, 3: 727.5679628875453, 4: 788.5536050225546}} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'W': 36, 'Q': 2, 'A': 1} -INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_W'), (1, 'Signal_Q'), (2, 'Signal_A')] +INFO:pyaf.std:START_TRAINING '['Signal_W', 'Signal_Q', 'Signal_A']' -INFO:pyaf.std:START_TRAINING 'Signal_Q' -INFO:pyaf.std:START_TRAINING 'Signal_A' -INFO:pyaf.std:START_TRAINING 'Signal_W' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_A' 1.6198875904083252 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_Q' 4.385876655578613 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Signal_W' 12.67052435874939 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_W', 'Signal_Q', 'Signal_A']' 35.772974491119385 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_W'), (1, 'Signal_Q'), (2, 'Signal_A')] -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:START_FORECASTING 'Signal_A' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_A' 0.980262041091919 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 1.0212490558624268 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 2.565838575363159 +INFO:pyaf.std:START_FORECASTING '['Signal_W', 'Signal_Q', 'Signal_A']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_Q', 'Signal_A']' 5.386401176452637 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -29,13 +20,13 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'Signal_W', 'Signal_W_Forecast', - 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', - 'Date', 'TH_Q_start', 'Signal_Q', 'Signal_Q_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'TH_Q_start', 'TH_A_start', 'Signal_W', + 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', + 'Signal_W_Forecast_Upper_Bound', 'Signal_Q', 'Signal_Q_Forecast', 'Signal_Q_Forecast_Lower_Bound', 'Signal_Q_Forecast_Upper_Bound', - 'TH_A_start', 'Signal_A', 'Signal_A_Forecast', - 'Signal_A_Forecast_Lower_Bound', 'Signal_A_Forecast_Upper_Bound', - 'Signal_W_BU_Forecast', 'Signal_Q_BU_Forecast', 'Signal_A_BU_Forecast', + 'Signal_A', 'Signal_A_Forecast', 'Signal_A_Forecast_Lower_Bound', + 'Signal_A_Forecast_Upper_Bound', 'Signal_W_BU_Forecast', + 'Signal_Q_BU_Forecast', 'Signal_A_BU_Forecast', 'Signal_A_AHP_TD_Forecast', 'Signal_Q_AHP_TD_Forecast', 'Signal_W_AHP_TD_Forecast', 'Signal_A_PHA_TD_Forecast', 'Signal_Q_PHA_TD_Forecast', 'Signal_W_PHA_TD_Forecast', @@ -43,42 +34,39 @@ INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'Signal_W', 'Signal_ 'Signal_W_OC_Forecast', 'Signal_Q_OC_Forecast', 'Signal_A_OC_Forecast'], dtype='object') INFO:pyaf.hierarchical:STRUCTURE_LEVEL (0, ['Signal_W']) -INFO:pyaf.hierarchical:MODEL_LEVEL (0, ['Signal_W']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (1, ['Signal_Q']) -INFO:pyaf.hierarchical:MODEL_LEVEL (1, ['Signal_Q']) INFO:pyaf.hierarchical:STRUCTURE_LEVEL (2, ['Signal_A']) -INFO:pyaf.hierarchical:MODEL_LEVEL (2, ['Signal_A']) -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.0013, 'RMSE': 9.395424908110185, 'MAE': 0.5838032976634272, 'SMAPE': 0.0011, 'ErrorMean': 0.5838032976634272, 'ErrorStdDev': 9.377269480695032, 'R2': 0.8859274941168319, 'Pearson': 1.0} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.0, 'RMSE': 0.0, 'MAE': 0.0, 'SMAPE': 0.0, 'ErrorMean': 0.0, 'ErrorStdDev': 0.0, 'R2': 1.0, 'Pearson': 1.0} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_BU_Forecast', 'Length': 259, 'MAPE': 13271078166.4924, 'RMSE': 30.474292788496076, 'MAE': 2.9667866057769423, 'SMAPE': 0.0224, 'ErrorMean': -0.3125709724791831, 'ErrorStdDev': 30.47268974583876, 'R2': -0.20009437988644097, 'Pearson': 0.09169301177136681} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_BU_Forecast', 'Length': 65, 'MAPE': 36204161246.2509, 'RMSE': 93.58405680218675, 'MAE': 14.649045893793213, 'SMAPE': 0.0511, 'ErrorMean': -7.408213644545485, 'ErrorStdDev': 93.29037494914299, 'R2': 0.28964831895432186, 'Pearson': 0.6110492262092064} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_MO_Forecast', 'Length': 259, 'MAPE': 13271078166.4924, 'RMSE': 30.474292788496076, 'MAE': 2.9667866057769423, 'SMAPE': 0.0224, 'ErrorMean': -0.3125709724791831, 'ErrorStdDev': 30.47268974583876, 'R2': -0.20009437988644097, 'Pearson': 0.09169301177136681} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_MO_Forecast', 'Length': 65, 'MAPE': 36204161246.2509, 'RMSE': 93.58405680218675, 'MAE': 14.649045893793213, 'SMAPE': 0.0511, 'ErrorMean': -7.408213644545485, 'ErrorStdDev': 93.29037494914299, 'R2': 0.28964831895432186, 'Pearson': 0.6110492262092064} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_OC_Forecast', 'Length': 259, 'MAPE': 46092656075.4928, 'RMSE': 16.53770981685625, 'MAE': 5.533154773738859, 'SMAPE': 1.9951, 'ErrorMean': 3.685376441359299, 'ErrorStdDev': 16.12184376775862, 'R2': 0.6465736995331748, 'Pearson': 0.9155273960720332} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_OC_Forecast', 'Length': 65, 'MAPE': 65022316691.7676, 'RMSE': 66.9625814559759, 'MAE': 14.6742320043321, 'SMAPE': 1.9821, 'ErrorMean': -1.6697686659804034, 'ErrorStdDev': 66.94175967100294, 'R2': 0.6363077449849237, 'Pearson': 0.9759742091935967} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_PHA_TD_Forecast', 'Length': 259, 'MAPE': 0.0013, 'RMSE': 9.395424908110185, 'MAE': 0.5838032976634272, 'SMAPE': 0.0011, 'ErrorMean': 0.5838032976634272, 'ErrorStdDev': 9.377269480695032, 'R2': 0.8859274941168319, 'Pearson': 1.0} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_PHA_TD_Forecast', 'Length': 65, 'MAPE': 0.0, 'RMSE': 0.0, 'MAE': 0.0, 'SMAPE': 0.0, 'ErrorMean': 0.0, 'ErrorStdDev': 0.0, 'R2': 1.0, 'Pearson': 1.0} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 259, 'MAPE': 0.009, 'RMSE': 17.32725910291221, 'MAE': 1.5615994576337682, 'SMAPE': 0.0166, 'ErrorMean': -1.4596812848563918, 'ErrorStdDev': 17.2656664674751, 'R2': 0.008208006144052149, 'Pearson': 0.13415471077674931} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.0245, 'RMSE': 35.269566201719975, 'MAE': 5.776190997556919, 'SMAPE': 0.0438, 'ErrorMean': -5.776190997556919, 'ErrorStdDev': 34.79336025188212, 'R2': 0.20812312621799922, 'Pearson': 0.5912378798785645} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 259, 'MAPE': 123778516911.0274, 'RMSE': 21.202703091557442, 'MAE': 13.900745528675355, 'SMAPE': 1.9963, 'ErrorMean': 10.854957853528015, 'ErrorStdDev': 18.21330580610972, 'R2': -0.4850576813929601, 'Pearson': 0.0445496407765462} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 65, 'MAPE': 155739859754.8788, 'RMSE': 40.057988251094905, 'MAE': 21.85723115325756, 'SMAPE': 1.9884, 'ErrorMean': 9.290740797712697, 'ErrorStdDev': 38.96568436656251, 'R2': -0.021493702068489773, 'Pearson': 0.18686035642830728} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 259, 'MAPE': 0.0025, 'RMSE': 2.294838725457077, 'MAE': 0.2425101178448236, 'SMAPE': 0.0029, 'ErrorMean': -0.2425101178448236, 'ErrorStdDev': 2.281988961103964, 'R2': 0.9826033671129429, 'Pearson': 0.9988950001546018} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 65, 'MAPE': 0.0022, 'RMSE': 3.234338161429145, 'MAE': 0.5163804934370652, 'SMAPE': 0.0023, 'ErrorMean': -0.5163804934370652, 'ErrorStdDev': 3.1928505333752404, 'R2': 0.9933407047011198, 'Pearson': 0.9996926813854645} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 259, 'MAPE': 41259505637.0276, 'RMSE': 16.468381633813255, 'MAE': 5.810087950786782, 'SMAPE': 1.9901, 'ErrorMean': 3.7554372959936586, 'ErrorStdDev': 16.03447175159748, 'R2': 0.10409346560348098, 'Pearson': 0.4929280128186859} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 65, 'MAPE': 51913286584.9747, 'RMSE': 31.298825263303634, 'MAE': 10.592474112318788, 'SMAPE': 1.9636, 'ErrorMean': 5.2220644851280165, 'ErrorStdDev': 30.860111882104032, 'R2': 0.37638938551947, 'Pearson': 0.7495034233160456} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 259, 'MAPE': 0.0228, 'RMSE': 19.73739412818435, 'MAE': 2.100134616649757, 'SMAPE': 0.0205, 'ErrorMean': -0.9211461258404028, 'ErrorStdDev': 19.71588742071062, 'R2': -0.2868872135314062, 'Pearson': 0.13415471077674931} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 65, 'MAPE': 0.0228, 'RMSE': 34.266837356566626, 'MAE': 5.443859870145388, 'SMAPE': 0.0368, 'ErrorMean': -2.548355778406996, 'ErrorStdDev': 34.171947928791674, 'R2': 0.25250985679800086, 'Pearson': 0.5912378798785645} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 259, 'MAPE': 1.0005, 'RMSE': 14.09226298249122, 'MAE': 12.525818474027027, 'SMAPE': 1.9951, 'ErrorMean': -12.501133078405783, 'ErrorStdDev': 6.504886449714639, 'R2': -3.7375615872071135, 'Pearson': -0.10204198548161875} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 65, 'MAPE': 0.9964, 'RMSE': 17.68633914727732, 'MAE': 16.76903906261268, 'SMAPE': 1.9882, 'ErrorMean': -16.76903906261268, 'ErrorStdDev': 5.621914384716685, 'R2': -8.563076674073969, 'Pearson': 0.19426252496744958} -INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 259, 'MAPE': 0.3608, 'RMSE': 1.8075756006784895, 'MAE': 1.2707655069523887, 'SMAPE': 0.1559, 'ErrorMean': -2.400482215405744e-16, 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+INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_BU_Forecast', 'Length': 1, 'MAPE': 0.9468, 'RMSE': 424.67680638416823, 'MAE': 424.67680638416823, 'SMAPE': 1.7978, 'ErrorMean': -424.67680638416823, 'ErrorStdDev': 0.0, 'R2': -1803503898806562.2, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_BU_Forecast', 'Length': 1, 'MAPE': 0.7946, 'RMSE': 716.8609349960077, 'MAE': 716.8609349960077, 'SMAPE': 1.3184, 'ErrorMean': -716.8609349960077, 'ErrorStdDev': 0.0, 'R2': -5138896001233503.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_MO_Forecast', 'Length': 1, 'MAPE': 0.9468, 'RMSE': 424.67680638416823, 'MAE': 424.67680638416823, 'SMAPE': 1.7978, 'ErrorMean': -424.67680638416823, 'ErrorStdDev': 0.0, 'R2': -1803503898806562.2, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_MO_Forecast', 'Length': 1, 'MAPE': 0.7946, 'RMSE': 716.8609349960077, 'MAE': 716.8609349960077, 'SMAPE': 1.3184, 'ErrorMean': -716.8609349960077, 'ErrorStdDev': 0.0, 'R2': -5138896001233503.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_OC_Forecast', 'Length': 1, 'MAPE': 0.5335, 'RMSE': 239.28729404315308, 'MAE': 239.28729404315308, 'SMAPE': 0.7275, 'ErrorMean': -239.28729404315308, 'ErrorStdDev': 0.0, 'R2': -572584090904943.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_OC_Forecast', 'Length': 1, 'MAPE': 0.5888, 'RMSE': 531.1800217851564, 'MAE': 531.1800217851564, 'SMAPE': 0.8344, 'ErrorMean': -531.1800217851564, 'ErrorStdDev': 0.0, 'R2': -2821522155436791.5, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_A_PHA_TD_Forecast', 'Length': 1, 'MAPE': 0.3371, 'RMSE': 151.20505409482763, 'MAE': 151.20505409482763, 'SMAPE': 0.2885, 'ErrorMean': 151.20505409482763, 'ErrorStdDev': 0.0, 'R2': -228629683838196.47, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_A_PHA_TD_Forecast', 'Length': 1, 'MAPE': 0.0, 'RMSE': 0.0, 'MAE': 0.0, 'SMAPE': 0.0, 'ErrorMean': 0.0, 'ErrorStdDev': 0.0, 'R2': 1.0, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 3, 'MAPE': 0.779, 'RMSE': 160.99749705822407, 'MAE': 134.81808650904864, 'SMAPE': 1.4295, 'ErrorMean': -126.01915092593515, 'ErrorStdDev': 100.19664494841754, 'R2': -3.4770689062746083, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_AHP_TD_Forecast', 'Length': 2, 'MAPE': 0.7975, 'RMSE': 201.0674631855413, 'MAE': 187.72620742059985, 'SMAPE': 1.4235, 'ErrorMean': -187.72620742059985, 'ErrorStdDev': 72.02080115735264, 'R2': -36.932563136716986, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 3, 'MAPE': 0.9118, 'RMSE': 148.8077333972599, 'MAE': 131.47650131052686, 'SMAPE': 1.6769, 'ErrorMean': -131.47650131052686, 'ErrorStdDev': 69.69699507133012, 'R2': -2.824780647738382, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_BU_Forecast', 'Length': 2, 'MAPE': 0.8954, 'RMSE': 207.2242919486873, 'MAE': 204.20546827760398, 'SMAPE': 1.6232, 'ErrorMean': -204.20546827760398, 'ErrorStdDev': 35.2425013181424, 'R2': -39.29117390609707, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 3, 'MAPE': 0.2124, 'RMSE': 21.322662098864694, 'MAE': 20.936706840603104, 'SMAPE': 0.2495, 'ErrorMean': -20.936706840603104, 'ErrorStdDev': 4.038592038446573, 'R2': 0.9214694969761675, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_MO_Forecast', 'Length': 2, 'MAPE': 0.0705, 'RMSE': 18.43856444060919, 'MAE': 16.7823660367046, 'SMAPE': 0.0734, 'ErrorMean': -16.7823660367046, 'ErrorStdDev': 7.637594440696731, 'R2': 0.6810057879776434, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 3, 'MAPE': 1.9026, 'RMSE': 146.8409922534578, 'MAE': 145.39719441844372, 'SMAPE': 1.1458, 'ErrorMean': -31.987645445362578, 'ErrorStdDev': 143.31457547940414, 'R2': -2.7243471137665436, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_OC_Forecast', 'Length': 2, 'MAPE': 0.7899, 'RMSE': 175.54006944362365, 'MAE': 175.53722724927175, 'SMAPE': 0.8182, 'ErrorMean': 0.9989143655716646, 'ErrorStdDev': 175.53722724927175, 'R2': -27.912198960759017, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 3, 'MAPE': 1.9665, 'RMSE': 183.39144317148777, 'MAE': 181.3116219040957, 'SMAPE': 1.774, 'ErrorMean': -79.5256155308881, 'ErrorStdDev': 165.2516199707416, 'R2': -4.809164386560529, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_Q_PHA_TD_Forecast', 'Length': 2, 'MAPE': 0.742, 'RMSE': 195.35102924913187, 'MAE': 176.92544577972512, 'SMAPE': 1.1948, 'ErrorMean': -82.82156279822738, 'ErrorStdDev': 176.92544577972512, 'R2': -34.806345645205965, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 3, 'MAPE': 1.042, 'RMSE': 15.191870657202548, 'MAE': 12.811429591352693, 'SMAPE': 1.5735, 'ErrorMean': -11.381358130580038, 'ErrorStdDev': 10.062684590538026, 'R2': -2.375096523125031, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_AHP_TD_Forecast', 'Length': 2, 'MAPE': 0.8817, 'RMSE': 20.617837932001354, 'MAE': 20.596510464409498, 'SMAPE': 1.6174, 'ErrorMean': -20.596510464409498, 'ErrorStdDev': 0.9375487612620557, 'R2': -94.61818603078379, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 3, 'MAPE': 0.4945, 'RMSE': 2.5975979338232156, 'MAE': 2.2448129897806264, 'SMAPE': 0.3612, 'ErrorMean': -0.7380650266594776, 'ErrorStdDev': 2.4905370991464437, 'R2': 0.9013249058271015, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_BU_Forecast', 'Length': 2, 'MAPE': 0.0337, 'RMSE': 0.8924147685240064, 'MAE': 0.7474459100957418, 'SMAPE': 0.0346, 'ErrorMean': -0.7474459100957418, 'ErrorStdDev': 0.4875743333696967, 'R2': 0.8208619860423398, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 3, 'MAPE': 0.1379, 'RMSE': 2.9555370409606017, 'MAE': 2.160060284078923, 'SMAPE': 0.1476, 'ErrorMean': -2.041175493978972, 'ErrorStdDev': 2.137475567876706, 'R2': 0.8722571790605442, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_MO_Forecast', 'Length': 2, 'MAPE': 0.2094, 'RMSE': 6.813991912574255, 'MAE': 5.293712923551139, 'SMAPE': 0.2525, 'ErrorMean': -5.293712923551139, 'ErrorStdDev': 4.290348385347629, 'R2': -9.44377447489641, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 3, 'MAPE': 37.9478, 'RMSE': 125.19028294641357, 'MAE': 98.75079083850481, 'SMAPE': 1.3932, 'ErrorMean': 98.75079083850481, 'ErrorStdDev': 76.94730828932852, 'R2': -228.1948903026122, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 2, 'MAPE': 8.1819, 'RMSE': 248.23838350673773, 'MAE': 204.45693673307989, 'SMAPE': 1.4667, 'ErrorMean': 204.45693673307989, 'ErrorStdDev': 140.78230026449904, 'R2': -13859.922219759077, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 3, 'MAPE': 3.2619, 'RMSE': 17.394843036456045, 'MAE': 17.04012586843329, 'SMAPE': 1.8637, 'ErrorMean': -7.152661853499441, 'ErrorStdDev': 15.856228828836864, 'R2': -3.424912810028623, 'Pearson': 0.0} +INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 2, 'MAPE': 0.5112, 'RMSE': 15.232331477230026, 'MAE': 11.05518657080111, 'SMAPE': 1.0113, 'ErrorMean': -11.05518657080111, 'ErrorStdDev': 10.478872654870441, 'R2': -51.18996690696763, 'Pearson': 0.0} +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 42.26661801338196 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_W_start' TimeMin=2001-01-28T00:00:00.000000 TimeMax=2006-01-08T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_W' Length=360 Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_W' Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 @@ -90,20 +78,29 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Signal_W_ConstantTrend_residue_zeroCycle_residue_ INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3608 MAPE_Forecast=0.0933 MAPE_Test=0.0727 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1559 SMAPE_Forecast=0.1004 SMAPE_Test=0.0754 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5456 MASE_Forecast=0.6072 MASE_Test=0.5062 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.2707655069523887 L1_Forecast=1.3119893773007625 L1_Test=1.269372776026798 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.7464635036586067 L2_Test=1.4940617790422326 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.270765506952389 L1_Forecast=1.3119893773007616 L1_Test=1.269372776026798 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.8075756006784895 L2_Forecast=1.746463503658606 L2_Test=1.4940617790422324 INFO:pyaf.std:MODEL_COMPLEXITY 64 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12.516770159495985 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_W_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529058 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969627 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813124 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630132 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067375 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684192 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395425 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.1522696318163203 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070928 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag56 0.14777440880242368 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag21 0.5597208683529054 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5009964302969634 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag42 0.34488407178813113 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.2770936589630128 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.24858732249067456 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag55 -0.16334446749684117 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag2 0.15927613077395358 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag6 -0.15226963181632097 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.14807146265070792 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_W_ConstantTrend_residue_zeroCycle_residue_Lag56 0.1477744088024236 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_Q_start' TimeMin=2001-01-28T00:00:00.000000 TimeMax=2007-04-29T00:00:00.000000 TimeDelta= Horizon=2 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_Q' Length=6 Min=39.15353109322899 Max=272.5452226620775 Mean=192.85929432770362 StdDev=76.93391233657083 @@ -116,9 +113,18 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Signal_Q_PolyTrend_residue_Seasonal_DayOfMont INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1297 MAPE_Forecast=0.1297 MAPE_Test=0.1297 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1492 SMAPE_Forecast=0.1492 SMAPE_Test=0.1492 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2207 MASE_Forecast=0.2207 MASE_Test=0.2207 -INFO:pyaf.std:MODEL_L1 L1_Fit=16.062475432536434 L1_Forecast=16.062475432536434 L1_Test=16.062475432536434 +INFO:pyaf.std:MODEL_L1 L1_Fit=16.062475432536427 L1_Forecast=16.062475432536427 L1_Test=16.062475432536427 INFO:pyaf.std:MODEL_L2 L2_Fit=18.456837369965267 L2_Forecast=18.456837369965267 L2_Test=18.456837369965267 INFO:pyaf.std:MODEL_COMPLEXITY 52 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 39.15353109322899 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (59.631883409618226, array([-11.54796233, -13.6024255 , -12.5591831 ])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES Diff_Signal_Q_PolyTrend_residue_Seasonal_DayOfMonth 9.59353799860087 {28: -74.90707229101167, 29: 65.3135342924108, 30: 9.59353799860087} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_A_start' TimeMin=2001-01-28T00:00:00.000000 TimeMax=2007-01-28T00:00:00.000000 TimeDelta= Horizon=1 @@ -135,10 +141,19 @@ INFO:pyaf.std:MODEL_MASE MASE_Fit=0.1667 MASE_Forecast=0.1667 MASE_Test=0.1667 INFO:pyaf.std:MODEL_L1 L1_Fit=75.60252704741382 L1_Forecast=75.60252704741382 L1_Test=75.60252704741382 INFO:pyaf.std:MODEL_L2 L2_Fit=106.91811910013136 L2_Forecast=106.91811910013136 L2_Test=106.91811910013136 INFO:pyaf.std:MODEL_COMPLEXITY 48 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 448.5551485960038 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (151.20505409482766, array([151.20505409])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_Signal_A_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_HIERARCHICAL_FORECASTING -INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODELS_LEVEL_SIGNAL [(0, 'Signal_W'), (1, 'Signal_Q'), (2, 'Signal_A')] +INFO:pyaf.std:START_FORECASTING '['Signal_W', 'Signal_Q', 'Signal_A']' RangeIndex: 360 entries, 0 to 359 Data columns (total 2 columns): @@ -148,62 +163,56 @@ Data columns (total 2 columns): 1 Signal 360 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 5.8 KB -INFO:pyaf.std:START_FORECASTING 'Signal_W' -INFO:pyaf.std:START_FORECASTING 'Signal_Q' -INFO:pyaf.std:START_FORECASTING 'Signal_A' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_A' 0.874798059463501 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_Q' 1.161147117614746 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Signal_W' 2.375995635986328 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_Q', 'Signal_A']' 5.498276472091675 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.6111862659454346 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 5.682374000549316 Int64Index: 396 entries, 0 to 395 -Data columns (total 31 columns): +Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 TH_W_start 396 non-null datetime64[ns] - 1 Signal_W 360 non-null float64 - 2 Signal_W_Forecast 396 non-null float64 - 3 Signal_W_Forecast_Lower_Bound 36 non-null float64 - 4 Signal_W_Forecast_Upper_Bound 36 non-null float64 - 5 Date 396 non-null datetime64[ns] - 6 TH_Q_start 6 non-null datetime64[ns] - 7 Signal_Q 361 non-null float64 - 8 Signal_Q_Forecast 396 non-null float64 + 1 TH_Q_start 6 non-null datetime64[ns] + 2 TH_A_start 2 non-null datetime64[ns] + 3 Signal_W 360 non-null float64 + 4 Signal_W_Forecast 396 non-null float64 + 5 Signal_W_Forecast_Lower_Bound 36 non-null float64 + 6 Signal_W_Forecast_Upper_Bound 36 non-null float64 + 7 Signal_Q 6 non-null float64 + 8 Signal_Q_Forecast 6 non-null float64 9 Signal_Q_Forecast_Lower_Bound 0 non-null float64 10 Signal_Q_Forecast_Upper_Bound 0 non-null float64 - 11 TH_A_start 2 non-null datetime64[ns] - 12 Signal_A 361 non-null float64 - 13 Signal_A_Forecast 396 non-null float64 - 14 Signal_A_Forecast_Lower_Bound 0 non-null float64 - 15 Signal_A_Forecast_Upper_Bound 0 non-null float64 - 16 Signal_W_BU_Forecast 396 non-null float64 - 17 Signal_Q_BU_Forecast 396 non-null float64 - 18 Signal_A_BU_Forecast 396 non-null float64 - 19 Signal_A_AHP_TD_Forecast 396 non-null float64 - 20 Signal_Q_AHP_TD_Forecast 396 non-null float64 - 21 Signal_W_AHP_TD_Forecast 396 non-null float64 - 22 Signal_A_PHA_TD_Forecast 396 non-null float64 - 23 Signal_Q_PHA_TD_Forecast 396 non-null float64 - 24 Signal_W_PHA_TD_Forecast 396 non-null float64 - 25 Signal_Q_MO_Forecast 396 non-null float64 - 26 Signal_W_MO_Forecast 396 non-null float64 - 27 Signal_A_MO_Forecast 396 non-null float64 - 28 Signal_W_OC_Forecast 396 non-null float64 - 29 Signal_Q_OC_Forecast 396 non-null float64 - 30 Signal_A_OC_Forecast 396 non-null float64 -dtypes: datetime64[ns](4), float64(27) -memory usage: 119.0 KB - TH_W_start Signal_W ... Signal_Q_OC_Forecast Signal_A_OC_Forecast -391 2008-07-27 NaN ... 6.048980 6.048980 -392 2008-08-03 NaN ... 6.285242 6.285242 -393 2008-08-10 NaN ... 7.150714 7.150714 -394 2008-08-17 NaN ... 7.336162 7.336162 -395 2008-08-24 NaN ... 7.699144 7.699144 + 11 Signal_A 361 non-null float64 + 12 Signal_A_Forecast 396 non-null float64 + 13 Signal_A_Forecast_Lower_Bound 0 non-null float64 + 14 Signal_A_Forecast_Upper_Bound 0 non-null float64 + 15 Signal_W_BU_Forecast 396 non-null float64 + 16 Signal_Q_BU_Forecast 396 non-null float64 + 17 Signal_A_BU_Forecast 6 non-null float64 + 18 Signal_A_AHP_TD_Forecast 396 non-null float64 + 19 Signal_Q_AHP_TD_Forecast 396 non-null float64 + 20 Signal_W_AHP_TD_Forecast 396 non-null float64 + 21 Signal_A_PHA_TD_Forecast 396 non-null float64 + 22 Signal_Q_PHA_TD_Forecast 396 non-null float64 + 23 Signal_W_PHA_TD_Forecast 396 non-null float64 + 24 Signal_Q_MO_Forecast 6 non-null float64 + 25 Signal_W_MO_Forecast 6 non-null float64 + 26 Signal_A_MO_Forecast 6 non-null float64 + 27 Signal_W_OC_Forecast 6 non-null float64 + 28 Signal_Q_OC_Forecast 6 non-null float64 + 29 Signal_A_OC_Forecast 6 non-null float64 +dtypes: datetime64[ns](3), float64(27) +memory usage: 115.9 KB + TH_W_start TH_Q_start ... Signal_Q_OC_Forecast Signal_A_OC_Forecast +391 2008-07-27 NaT ... NaN NaN +392 2008-08-03 NaT ... NaN NaN +393 2008-08-10 NaT ... NaN NaN +394 2008-08-17 NaT ... NaN NaN +395 2008-08-24 NaT ... NaN NaN -[5 rows x 31 columns] +[5 rows x 30 columns] diff --git a/tests/references/time_res_test_ozone_Daily.log b/tests/references/time_res_test_ozone_Daily.log index 2375bcb2d..7e8466f55 100644 --- a/tests/references/time_res_test_ozone_Daily.log +++ b/tests/references/time_res_test_ozone_Daily.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 2.6954452991485596 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 3.4527783393859863 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_1_Daily' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-06-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=782.4999999999999 Mean=434.11372549019586 StdDev=222.55463397945354 @@ -31,8 +31,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.16367530822753906 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4597811698913574 INFO:pyaf.std:START_TRAINING 'Ozone' Split Transformation ... ForecastMAPE TestMAPE 0 None CumSum_Ozone ... 0.2515 0.1962 @@ -84,31 +84,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-07-22 00:00:00" - ], - "TimeVariable": "Time_1_Daily" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-07-22 00:00:00" + ], + "TimeVariable": "Time_1_Daily" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "Integration", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.7628205128205187", - "MAPE": "0.2515", - "MASE": "0.9826", - "RMSE": "0.9164801675082231" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "Integration", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.7628205128205187", + "MAPE": "0.2515", + "MASE": "0.9826", + "RMSE": "0.9164801675082231" + } } } @@ -121,7 +123,7 @@ Forecasts -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 3.7808501720428467 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.10690450668335 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_5_Daily' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-01-30T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=782.4999999999999 Mean=434.11372549019586 StdDev=222.55463397945354 @@ -147,8 +149,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.16302227973937988 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.47591352462768555 Split Transformation ... ForecastMAPE TestMAPE 0 None CumSum_Ozone ... 0.2515 0.1962 1 None _Ozone ... 0.2778 0.2132 @@ -199,31 +201,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2002-10-12 00:00:00" - ], - "TimeVariable": "Time_5_Daily" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-10-12 00:00:00" + ], + "TimeVariable": "Time_5_Daily" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "Integration", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "Integration", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.7628205128205187", - "MAPE": "0.2515", - "MASE": "0.9826", - "RMSE": "0.9164801675082231" + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.7628205128205187", + "MAPE": "0.2515", + "MASE": "0.9826", + "RMSE": "0.9164801675082231" + } } } diff --git a/tests/references/time_res_test_ozone_Hourly.log b/tests/references/time_res_test_ozone_Hourly.log index 8c7e81316..82ea6a7bd 100644 --- a/tests/references/time_res_test_ozone_Hourly.log +++ b/tests/references/time_res_test_ozone_Hourly.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 2.176426887512207 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 3.031771421432495 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_1_Hourly' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-07T08:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=782.4999999999999 Mean=434.11372549019586 StdDev=222.55463397945354 @@ -31,8 +31,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.18077683448791504 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.6417899131774902 INFO:pyaf.std:START_TRAINING 'Ozone' Split Transformation ... ForecastMAPE TestMAPE 0 None CumSum_Ozone ... 0.2515 0.1962 @@ -84,31 +84,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-01-09 11:00:00" - ], - "TimeVariable": "Time_1_Hourly" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-01-09 11:00:00" + ], + "TimeVariable": "Time_1_Hourly" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "Integration", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.7628205128205187", - "MAPE": "0.2515", - "MASE": "0.9826", - "RMSE": "0.9164801675082231" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "Integration", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.7628205128205187", + "MAPE": "0.2515", + "MASE": "0.9826", + "RMSE": "0.9164801675082231" + } } } @@ -121,7 +123,7 @@ Forecasts -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 2.5904667377471924 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 3.1279196739196777 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_5_Hourly' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-02-01T16:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -133,22 +135,22 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_Hour_residue_N INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1717 MAPE_Forecast=0.1793 MAPE_Test=0.2716 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1658 SMAPE_Forecast=0.1994 SMAPE_Test=0.2581 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7499 MASE_Forecast=0.7235 MASE_Test=1.322 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.659154485497276 L1_Forecast=0.5616930304599017 L1_Test=0.6249469765763973 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.9095172659707329 L2_Forecast=0.6833286820031184 L2_Test=0.7379453952439594 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6591544854972761 L1_Forecast=0.5616930304599015 L1_Test=0.6249469765763971 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9095172659707328 L2_Forecast=0.6833286820031181 L2_Test=0.7379453952439595 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.0224568737130095, array([-1.82661309])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022456873713009, array([-1.82661309])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_Hour 0.013594700507344015 {0: -1.7340442799501812, 5: -1.3220270885433965, 10: -0.8563595220852989, 15: -0.19799270572982763, 20: -0.39756292056021447, 1: 0.5796920521350541, 6: 1.203296649779011, 11: 1.8921390287114521, 16: 0.9505058450669228, 21: 0.9683167395922938, 2: 0.21295282164332074, 7: -1.4424110963056527, 12: -1.6246002017802819, 17: -1.5009956041363257, 22: -1.0389784127295416, 3: -0.4095801119669993, 8: -0.10335662367880061, 13: 0.7702479739651551, 18: 1.38226516537194, 23: 1.2326949505415532, 4: 1.0831247357111655, 9: 0.8125230364737077, 14: -0.9544282877124368, 19: -1.3924110963056526} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_Hour 0.013594700507344903 {0: -1.7340442799501803, 5: -1.3220270885433956, 10: -0.856359522085298, 15: -0.19799270572982675, 20: -0.39756292056021447, 1: 0.5796920521350546, 6: 1.2032966497790114, 11: 1.8921390287114521, 16: 0.9505058450669237, 21: 0.9683167395922947, 2: 0.2129528216433214, 7: -1.442411096305652, 12: -1.6246002017802814, 17: -1.500995604136325, 22: -1.0389784127295407, 3: -0.4095801119669984, 8: -0.10335662367879994, 13: 0.770247973965156, 18: 1.3822651653719409, 23: 1.2326949505415539, 4: 1.0831247357111664, 9: 0.8125230364737084, 14: -0.9544282877124366, 19: -1.392411096305652} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.1741030216217041 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5119469165802002 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1793 0.2716 1 None _Ozone ... 0.2120 0.6080 @@ -198,31 +200,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-02-12 07:00:00" - ], - "TimeVariable": "Time_5_Hourly" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-02-12 07:00:00" + ], + "TimeVariable": "Time_5_Hourly" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_Hour_residue_NoAR", + "Cycle": "Seasonal_Hour", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_Hour_residue_NoAR", - "Cycle": "Seasonal_Hour", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.5616930304599017", - "MAPE": "0.1793", - "MASE": "0.7235", - "RMSE": "0.6833286820031184" + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5616930304599015", + "MAPE": "0.1793", + "MASE": "0.7235", + "RMSE": "0.6833286820031181" + } } } diff --git a/tests/references/time_res_test_ozone_Minutely.log b/tests/references/time_res_test_ozone_Minutely.log index a85f00c74..edc638395 100644 --- a/tests/references/time_res_test_ozone_Minutely.log +++ b/tests/references/time_res_test_ozone_Minutely.log @@ -5,6 +5,38 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.4635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0936 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6521 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6521 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2925 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2866 @@ -37,7 +69,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lin INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4271 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7646 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7646 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 2.6359376907348633 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 3.832629680633545 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_1_PerMinute' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-01T02:32:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=782.4999999999999 Mean=434.11372549019586 StdDev=222.55463397945354 @@ -63,15 +95,11 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.1841416358947754 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5944559574127197 INFO:pyaf.std:START_TRAINING 'Ozone' Split Transformation ... TestMAPE TestMASE 0 None CumSum_Ozone ... 0.1962 0.9343 - -[1 rows x 20 columns] - Split Transformation ... TestMAPE TestMASE -0 None CumSum_Ozone ... 0.1962 0.9343 1 None _Ozone ... 0.2132 1.0048 2 None CumSum_Ozone ... 0.2132 1.0048 3 None Diff_Ozone ... 0.2132 1.0048 @@ -120,31 +148,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-01-01 03:23:00" - ], - "TimeVariable": "Time_1_PerMinute" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-01-01 03:23:00" + ], + "TimeVariable": "Time_1_PerMinute" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "Integration", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "Integration", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.7628205128205187", - "MAPE": "0.2515", - "MASE": "0.9826", - "RMSE": "0.9164801675082231" + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.7628205128205187", + "MAPE": "0.2515", + "MASE": "0.9826", + "RMSE": "0.9164801675082231" + } } } @@ -157,6 +187,54 @@ Forecasts +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_Minute_residue_NoAR 20 0.2208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_Minute_residue_NoAR 20 0.5739 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_Minute_residue_NoAR 52 2.6133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_Minute_residue_NoAR 52 5.5226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.4635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0936 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_Minute_residue_NoAR 52 13.387 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_Minute_residue_NoAR 52 72757.4852 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_Minute_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_Minute_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_Minute_residue_NoAR 52 0.5694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6521 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6521 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_Minute_residue_NoAR 52 1.1913 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2925 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_Minute_NoAR 4 0.2685 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 @@ -205,7 +283,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lin INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_Minute_NoAR 52 0.6681 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7646 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7646 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 3.0202572345733643 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.130349159240723 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_5_PerMinute' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-01T12:40:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -217,26 +295,22 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_Minute_residue INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1761 MAPE_Forecast=0.1764 MAPE_Test=0.2208 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1712 SMAPE_Forecast=0.194 SMAPE_Test=0.2249 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7728 MASE_Forecast=0.715 MASE_Test=1.0917 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.679250445363017 L1_Forecast=0.555079260429495 L1_Test=0.5160945527373147 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.9118592072313588 L2_Forecast=0.6628235013753501 L2_Test=0.5961519352473634 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6792504453630172 L1_Forecast=0.5550792604294948 L1_Test=0.5160945527373147 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9118592072313588 L2_Forecast=0.6628235013753497 L2_Test=0.5961519352473635 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.0224568737130095, array([-1.82661309])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022456873713009, array([-1.82661309])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_Minute 0.013594700507344015 {0: -1.6361876080174536, 5: -1.477820791661982, 10: -0.87739100649237, 15: -0.2653738150855851, 20: -0.17653143615314404, 25: 0.7586605677279836, 30: 1.2706777591347684, 35: 1.3595201380672095, 40: 0.9715373294739935, 45: 0.834626184914415, 50: -0.4428408814752649, 55: -1.4424110963056527} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_Minute 0.013594700507344903 {0: -1.6361876080174536, 5: -1.4778207916619812, 10: -0.8773910064923691, 15: -0.2653738150855842, 20: -0.1765314361531436, 25: 0.7586605677279845, 30: 1.2706777591347693, 35: 1.35952013806721, 40: 0.9715373294739944, 45: 0.8346261849144156, 50: -0.4428408814752647, 55: -1.442411096305652} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.17418289184570312 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2208 1.0917 - -[1 rows x 20 columns] +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.40421557426452637 Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.2208 1.0917 1 None _Ozone ... 0.5739 2.8035 @@ -287,31 +361,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-01-01 16:55:00" - ], - "TimeVariable": "Time_5_PerMinute" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-01-01 16:55:00" + ], + "TimeVariable": "Time_5_PerMinute" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_Minute_residue_NoAR", + "Cycle": "Seasonal_Minute", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_Minute_residue_NoAR", - "Cycle": "Seasonal_Minute", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "20", - "MAE": "0.555079260429495", - "MAPE": "0.1764", - "MASE": "0.715", - "RMSE": "0.6628235013753501" + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5550792604294948", + "MAPE": "0.1764", + "MASE": "0.715", + "RMSE": "0.6628235013753497" + } } } diff --git a/tests/references/time_res_test_ozone_Secondly.log b/tests/references/time_res_test_ozone_Secondly.log index 0d8db93b9..fb895468e 100644 --- a/tests/references/time_res_test_ozone_Secondly.log +++ b/tests/references/time_res_test_ozone_Secondly.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 2.084252119064331 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 3.558257579803467 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_1_PerSecond' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-01T00:02:32.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=782.4999999999999 Mean=434.11372549019586 StdDev=222.55463397945354 @@ -31,8 +31,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.16620278358459473 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.555957555770874 INFO:pyaf.std:START_TRAINING 'Ozone' Split Transformation ... ForecastMAPE TestMAPE 0 None CumSum_Ozone ... 0.2515 0.1962 @@ -84,31 +84,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-01-01 00:03:23" - ], - "TimeVariable": "Time_1_PerSecond" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-01-01 00:03:23" + ], + "TimeVariable": "Time_1_PerSecond" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "Integration", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.7628205128205187", - "MAPE": "0.2515", - "MASE": "0.9826", - "RMSE": "0.9164801675082231" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "Integration", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.7628205128205187", + "MAPE": "0.2515", + "MASE": "0.9826", + "RMSE": "0.9164801675082231" + } } } @@ -121,7 +123,7 @@ Forecasts -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 1.9601516723632812 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 3.1856307983398438 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_5_PerSecond' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-01-01T00:12:40.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=782.4999999999999 Mean=434.11372549019586 StdDev=222.55463397945354 @@ -147,8 +149,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.16483759880065918 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.43766355514526367 Split Transformation ... ForecastMAPE TestMAPE 0 None CumSum_Ozone ... 0.2515 0.1962 1 None _Ozone ... 0.2778 0.2132 @@ -199,31 +201,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2000-01-01 00:16:55" - ], - "TimeVariable": "Time_5_PerSecond" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-01-01 00:16:55" + ], + "TimeVariable": "Time_5_PerSecond" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "Integration", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "Integration", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.7628205128205187", - "MAPE": "0.2515", - "MASE": "0.9826", - "RMSE": "0.9164801675082231" + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.7628205128205187", + "MAPE": "0.2515", + "MASE": "0.9826", + "RMSE": "0.9164801675082231" + } } } diff --git a/tests/references/time_res_test_ozone_Weekly.log b/tests/references/time_res_test_ozone_Weekly.log index a43c597a6..0057a3d13 100644 --- a/tests/references/time_res_test_ozone_Weekly.log +++ b/tests/references/time_res_test_ozone_Weekly.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.1603875160217285 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 6.3367297649383545 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_1_Weekly' TimeMin=2000-01-02T00:00:00.000000 TimeMax=2002-12-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -31,8 +31,8 @@ INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_Lag1Trend_residue_Seasonal_DayOfNthWe INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.18581795692443848 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.8567783832550049 INFO:pyaf.std:START_TRAINING 'Ozone' Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.2482 0.2319 @@ -83,31 +83,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-02 00:00:00", - "2003-11-23 00:00:00" - ], - "TimeVariable": "Time_1_Weekly" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-02 00:00:00", + "2003-11-23 00:00:00" + ], + "TimeVariable": "Time_1_Weekly" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR", - "Cycle": "Seasonal_DayOfNthWeekOfMonth", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "36", - "MAE": "0.7205128205128207", - "MAPE": "0.2482", - "MASE": "0.9281", - "RMSE": "0.8979320972557177" + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR", + "Cycle": "Seasonal_DayOfNthWeekOfMonth", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "36", + "MAE": "0.7205128205128207", + "MAPE": "0.2482", + "MASE": "0.9281", + "RMSE": "0.8979320972557177" + } } } @@ -120,7 +122,7 @@ Forecasts -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.806647539138794 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.3391783237457275 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time_5_Weekly' TimeMin=2000-01-02T00:00:00.000000 TimeMax=2014-07-27T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=782.4999999999999 Mean=434.11372549019586 StdDev=222.55463397945354 @@ -146,8 +148,8 @@ INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.17473578453063965 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.6257996559143066 Split Transformation ... ForecastMAPE TestMAPE 0 None CumSum_Ozone ... 0.2515 0.1962 1 None _Ozone ... 0.2764 0.2582 @@ -198,31 +200,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-02 00:00:00", - "2019-06-16 00:00:00" - ], - "TimeVariable": "Time_5_Weekly" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "2000-01-02 00:00:00", + "2019-06-16 00:00:00" + ], + "TimeVariable": "Time_5_Weekly" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "Integration", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "Integration", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "72", - "MAE": "0.7628205128205187", - "MAPE": "0.2515", - "MASE": "0.9826", - "RMSE": "0.9164801675082231" + "Model_Performance": { + "COMPLEXITY": "72", + "MAE": "0.7628205128205187", + "MAPE": "0.2515", + "MASE": "0.9826", + "RMSE": "0.9164801675082231" + } } } diff --git a/tests/references/transformations_test_ozone_transf_anscombe.log b/tests/references/transformations_test_ozone_transf_anscombe.log index 55c1ac847..a1ba0ffba 100644 --- a/tests/references/transformations_test_ozone_transf_anscombe.log +++ b/tests/references/transformations_test_ozone_transf_anscombe.log @@ -1,10 +1,28 @@ INFO:pyaf.std:START_TRAINING 'Ozone' - Month Ozone Time -0 1955-01 2.7 1955-01-01 -1 1955-02 2.0 1955-02-01 -2 1955-03 3.6 1955-03-01 -3 1955-04 5.0 1955-04-01 -4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.1746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.5855 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.294 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.2896 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7918 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2206 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.1688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.2047 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.1408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.3234 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.1408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.3234 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.368 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6712 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.6712 INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.1822 INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.2682 INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Cycle_AR 78 0.1869 @@ -29,7 +47,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Poly INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_Cycle_None_NoAR 56 0.389 INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_NoCycle_AR 86 0.1595 INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_NoCycle_NoAR 48 0.389 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 2.215708017349243 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 10.227491617202759 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_Ozone' Min=1.224744871391589 Max=2.345207879911715 Mean=1.6888656389128833 StdDev=0.23126713490313816 @@ -41,39 +59,50 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_resid INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1642 MAPE_Forecast=0.1384 MAPE_Test=0.1408 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1559 SMAPE_Forecast=0.1512 SMAPE_Test=0.1438 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7168 MASE_Forecast=0.6055 MASE_Test=0.7524 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.62999952384404 L1_Forecast=0.4700858180949578 L1_Test=0.35565668910694587 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8311449866422521 L2_Forecast=0.6550475624599844 L2_Test=0.434669671258875 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.62999952384404 L1_Forecast=0.47008581809495725 L1_Test=0.35565668910694576 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8311449866422522 L2_Forecast=0.6550475624599839 L2_Test=0.4346696712588745 INFO:pyaf.std:MODEL_COMPLEXITY 86 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.869444283726548, array([-0.27591835])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.8694442837265477, array([-0.27591835])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Anscombe_Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.32099982115026565 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.3209998211502658 INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.11917290422114361 -INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag51 -0.10966014115710265 -INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.10811218454630091 -INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.08843747251747294 -INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.08841281287576909 -INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.08727106698203295 -INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.08115916216732101 -INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.08114662860251169 -INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.07382216509764587 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag51 -0.10966014115710274 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.10811218454630063 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.08843747251747286 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.088412812875769 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.08727106698203252 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.08115916216732097 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.0811466286025117 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.07382216509764589 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.496832847595215 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3674626350402832 - Split Transformation ... TestMAPE TestMASE -0 None Anscombe_Ozone ... 0.1408 0.7524 -1 None Anscombe_Ozone ... 0.1408 0.7524 - -[2 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 26.438716411590576 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.5571320056915283 + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE 0 None Anscombe_Ozone ... 0.1408 0.7524 1 None Anscombe_Ozone ... 0.1408 0.7524 @@ -124,31 +153,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Anscombe", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "Anscombe", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "86", - "MAE": "0.4700858180949578", - "MAPE": "0.1384", - "MASE": "0.6055", - "RMSE": "0.6550475624599844" + "Model_Performance": { + "COMPLEXITY": "86", + "MAE": "0.47008581809495725", + "MAPE": "0.1384", + "MASE": "0.6055", + "RMSE": "0.6550475624599839" + } } } diff --git a/tests/references/transformations_test_ozone_transf_boxcox.log b/tests/references/transformations_test_ozone_transf_boxcox.log index 23204d35b..c09084280 100644 --- a/tests/references/transformations_test_ozone_transf_boxcox.log +++ b/tests/references/transformations_test_ozone_transf_boxcox.log @@ -5,6 +5,246 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 914548936.3549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5793 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 914548936.3549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.5793 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 457905282.46 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 212981745.0977 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 207927941.024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 207927941.0278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 894333719.8679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 970906509.5874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 970906509.5883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 894333719.866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 970906509.5929 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 970906509.5642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.2904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 399250525.3599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 151614123.7003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 151614123.7003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.4113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.5899 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 189517656.7698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 189517658.1386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 543119858.539 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 485887167.3919 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 485887167.3919 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 543119858.4785 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 485887167.3955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 485887167.3955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.2731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.2887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5826 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.4802 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.5073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.4802 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.5073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1708 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.5117 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.5253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 1.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.5783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.5783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 1.555 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2215 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.7647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.7888 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.5_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.3051 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.2723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5829 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.4264 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.5432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.4264 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.5432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.5654 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.492 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.4056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3166 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.3992 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2865 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.4303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.4414 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.333 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.4414 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.33_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.333 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.4212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.5597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.4212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.5597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.4374 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 1.0923 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.3879 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2532 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1739 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.3635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2892 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2781 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.4125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3511 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.4125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-0.25_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.3511 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.2354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.6216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.3499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.6216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.2472 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.2787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.3774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.2815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.3607 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.3285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.3285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5882 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.6824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.6574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 1.0024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.2261 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.2082 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.2082 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2396 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2713 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.2396 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2713 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.6192 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.675 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.7045 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.8013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.7216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.9564 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5848 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.7365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.2669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.7365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.1677 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.2087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1662 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3147 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.1662 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.3147 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3315 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.49 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.5634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.5_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.5634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1901 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.6991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.2813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.6991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.2012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.2483 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2194 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.2449 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1654 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.1866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.3099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.4519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.5091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.33_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.5091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5843 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.2917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.1917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.2445 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.2409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1906 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.2046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.3072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.4315 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.297 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.4839 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.297 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.4839 INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone BoxCox(Lambda=-2)_ConstantTrend_Seasonal_MonthOfYear_AR 106 577899864.0212 INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone BoxCox(Lambda=-2)_ConstantTrend_Seasonal_MonthOfYear_NoAR 68 0.2644 INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2_Ozone BoxCox(Lambda=-2)_ConstantTrend_Cycle_AR 110 577899864.0212 @@ -245,7 +485,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone BoxCox(La INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone BoxCox(Lambda=0.25)_PolyTrend_Cycle_None_NoAR 88 0.3361 INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone BoxCox(Lambda=0.25)_PolyTrend_NoCycle_AR 118 0.1676 INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.25_Ozone BoxCox(Lambda=0.25)_PolyTrend_NoCycle_NoAR 80 0.3361 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 14.605989456176758 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 18.022746801376343 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Box_Cox_0.5_Ozone' Min=-1.9999999998 Max=0.0 Mean=-0.870301741487325 StdDev=0.35990759744739725 @@ -257,39 +497,44 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_re INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1613 MAPE_Forecast=0.1414 MAPE_Test=0.1662 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1554 SMAPE_Forecast=0.1528 SMAPE_Test=0.1605 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7181 MASE_Forecast=0.6203 MASE_Test=0.8999 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.631186300018043 L1_Forecast=0.481560267384624 L1_Test=0.4253842990374162 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8400354866381945 L2_Forecast=0.6861380988752175 L2_Test=0.5250757517407492 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6311863000180432 L1_Forecast=0.48156026738462415 L1_Test=0.42538429903741654 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8400354866381944 L2_Forecast=0.6861380988752172 L2_Test=0.52507575174075 INFO:pyaf.std:MODEL_COMPLEXITY 118 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:BOX_COX_TRANSFORMATION_LAMBDA BoxCox(Lambda=0.5) 0.5 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (-0.5991467217217009, array([-0.4083497])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (-0.5991467217217008, array([-0.4083497])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.35810855048198753 -INFO:pyaf.std:AR_MODEL_COEFF 2 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag51 -0.13381383217774806 -INFO:pyaf.std:AR_MODEL_COEFF 3 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.13095628563575984 -INFO:pyaf.std:AR_MODEL_COEFF 4 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12428040632850303 -INFO:pyaf.std:AR_MODEL_COEFF 5 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.12023157393767711 -INFO:pyaf.std:AR_MODEL_COEFF 6 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.10349248177683121 -INFO:pyaf.std:AR_MODEL_COEFF 7 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag49 -0.09109720478139287 -INFO:pyaf.std:AR_MODEL_COEFF 8 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.08725165024198586 -INFO:pyaf.std:AR_MODEL_COEFF 9 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.0871403983290903 -INFO:pyaf.std:AR_MODEL_COEFF 10 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag35 -0.08353023150972809 +INFO:pyaf.std:AR_MODEL_COEFF 1 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.3581085504819871 +INFO:pyaf.std:AR_MODEL_COEFF 2 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag51 -0.13381383217774848 +INFO:pyaf.std:AR_MODEL_COEFF 3 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.13095628563575992 +INFO:pyaf.std:AR_MODEL_COEFF 4 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12428040632850273 +INFO:pyaf.std:AR_MODEL_COEFF 5 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.12023157393767712 +INFO:pyaf.std:AR_MODEL_COEFF 6 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.10349248177683106 +INFO:pyaf.std:AR_MODEL_COEFF 7 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag49 -0.09109720478139267 +INFO:pyaf.std:AR_MODEL_COEFF 8 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.0872516502419856 +INFO:pyaf.std:AR_MODEL_COEFF 9 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.08714039832909046 +INFO:pyaf.std:AR_MODEL_COEFF 10 Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_Lag35 -0.08353023150972826 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.524091720581055 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3905069828033447 - Split Transformation ... TestMAPE TestMASE -0 None Box_Cox_0.5_Ozone ... 0.1662 0.8999 -1 None Box_Cox_0.5_Ozone ... 0.1662 0.8999 - -[2 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 27.743903160095215 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.2384703159332275 Split Transformation ... TestMAPE TestMASE 0 None Box_Cox_0.5_Ozone ... 0.1662 0.8999 1 None Box_Cox_0.5_Ozone ... 0.1662 0.8999 @@ -341,31 +586,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "BoxCox(Lambda=0.5)", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Box_Cox_0.5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "BoxCox(Lambda=0.5)", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "118", - "MAE": "0.481560267384624", - "MAPE": "0.1414", - "MASE": "0.6203", - "RMSE": "0.6861380988752175" + "Model_Performance": { + "COMPLEXITY": "118", + "MAE": "0.48156026738462415", + "MAPE": "0.1414", + "MASE": "0.6203", + "RMSE": "0.6861380988752172" + } } } diff --git a/tests/references/transformations_test_ozone_transf_cumsum.log b/tests/references/transformations_test_ozone_transf_cumsum.log index de8a9b66c..5fdd1f5e3 100644 --- a/tests/references/transformations_test_ozone_transf_cumsum.log +++ b/tests/references/transformations_test_ozone_transf_cumsum.log @@ -1,10 +1,28 @@ INFO:pyaf.std:START_TRAINING 'Ozone' - Month Ozone Time -0 1955-01 2.7 1955-01-01 -1 1955-02 2.0 1955-02-01 -2 1955-03 3.6 1955-03-01 -3 1955-04 5.0 1955-04-01 -4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3897 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3548 @@ -29,7 +47,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Pol INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 1.8270666599273682 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 6.9502739906311035 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=782.4999999999999 Mean=434.11372549019586 StdDev=222.55463397945354 @@ -56,14 +74,25 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.162798166275024 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.16202712059020996 - Split Transformation ... TestMAPE TestMASE -0 None CumSum_Ozone ... 0.1962 0.9343 -1 None CumSum_Ozone ... 0.1962 0.9343 - -[2 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.877013683319092 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.7451670169830322 + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE 0 None CumSum_Ozone ... 0.1962 0.9343 1 None CumSum_Ozone ... 0.1962 0.9343 @@ -113,31 +142,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "Integration", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "Integration", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "68", - "MAE": "0.7628205128205187", - "MAPE": "0.2515", - "MASE": "0.9826", - "RMSE": "0.9164801675082231" + "Model_Performance": { + "COMPLEXITY": "68", + "MAE": "0.7628205128205187", + "MAPE": "0.2515", + "MASE": "0.9826", + "RMSE": "0.9164801675082231" + } } } diff --git a/tests/references/transformations_test_ozone_transf_difference.log b/tests/references/transformations_test_ozone_transf_difference.log index 086c5f296..336c7e231 100644 --- a/tests/references/transformations_test_ozone_transf_difference.log +++ b/tests/references/transformations_test_ozone_transf_difference.log @@ -1,10 +1,28 @@ INFO:pyaf.std:START_TRAINING 'Ozone' - Month Ozone Time -0 1955-01 2.7 1955-01-01 -1 1955-02 2.0 1955-02-01 -2 1955-03 3.6 1955-03-01 -3 1955-04 5.0 1955-04-01 -4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.4619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 9.105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.9761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 10.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2323 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.2424 @@ -29,7 +47,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTr INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 6.5775 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 0.4357 INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 2.07145619392395 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.702120065689087 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_Ozone' Min=-4.299999999999999 Max=3.5 Mean=-0.007352941176470595 StdDev=1.103112731755888 @@ -41,8 +59,8 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR( INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2233 MAPE_Forecast=0.18 MAPE_Test=0.2262 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2107 SMAPE_Forecast=0.1812 SMAPE_Test=0.2485 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9072 MASE_Forecast=0.7251 MASE_Test=1.0525 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7974125243979913 L1_Forecast=0.5628756708886004 L1_Test=0.49755304755854635 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.0351679787899466 L2_Forecast=0.8261753731796114 L2_Test=0.5501555212590603 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7974125243979923 L1_Forecast=0.5628756708885972 L1_Test=0.49755304755854696 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.0351679787899466 L2_Forecast=0.8261753731796115 L2_Test=0.5501555212590609 INFO:pyaf.std:MODEL_COMPLEXITY 102 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 2.7 @@ -54,26 +72,37 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_Ozone_Lag1Trend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.5696406962680688 -INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag12 0.4604431736911735 -INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag4 0.42522014354355286 -INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag6 0.3498704857030744 -INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag13 0.346038702989171 -INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag11 0.343692795208757 -INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag5 0.3361876903164095 -INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag37 0.3233912594991725 -INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag10 0.2949049813046008 -INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag36 0.2892045740082262 +INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag1 -0.5696406962680699 +INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag12 0.4604431736911741 +INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag4 0.425220143543553 +INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag6 0.3498704857030755 +INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag13 0.3460387029891714 +INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag11 0.343692795208758 +INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag5 0.3361876903164098 +INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag37 0.32339125949917424 +INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag10 0.2949049813046014 +INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_Lag36 0.2892045740082275 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.3928844928741455 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3347506523132324 - Split Transformation ... TestMAPE TestMASE -0 None Diff_Ozone ... 0.2262 1.0525 -1 None Diff_Ozone ... 0.2262 1.0525 - -[2 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 28.994693517684937 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.3711254596710205 + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE 0 None Diff_Ozone ... 0.2262 1.0525 1 None Diff_Ozone ... 0.2262 1.0525 @@ -123,31 +152,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Difference", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "Difference", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "102", - "MAE": "0.5628756708886004", - "MAPE": "0.18", - "MASE": "0.7251", - "RMSE": "0.8261753731796114" + "Model_Performance": { + "COMPLEXITY": "102", + "MAE": "0.5628756708885972", + "MAPE": "0.18", + "MASE": "0.7251", + "RMSE": "0.8261753731796115" + } } } diff --git a/tests/references/transformations_test_ozone_transf_fisher.log b/tests/references/transformations_test_ozone_transf_fisher.log index 75023650b..636d5b3ea 100644 --- a/tests/references/transformations_test_ozone_transf_fisher.log +++ b/tests/references/transformations_test_ozone_transf_fisher.log @@ -1,10 +1,28 @@ INFO:pyaf.std:START_TRAINING 'Ozone' - Month Ozone Time -0 1955-01 2.7 1955-01-01 -1 1955-02 2.0 1955-02-01 -2 1955-03 3.6 1955-03-01 -3 1955-04 5.0 1955-04-01 -4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.5863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.7609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0701 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.7609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0701 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.1839 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.1939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.6898 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.6497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.3437 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.6497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.3437 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.7714 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.6036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.7282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.9415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.7282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.9415 INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2846 INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.2686 INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Cycle_None_AR 78 0.4596 @@ -29,7 +47,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTren INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_Cycle_None_NoAR 56 0.4408 INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_NoCycle_AR 86 0.2388 INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_NoCycle_NoAR 48 0.4408 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 1.9667456150054932 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.152033090591431 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Fisher_Ozone' Min=0.0 Max=9.556913957243776 Mean=0.43398413362015165 StdDev=0.6923238716460366 @@ -41,8 +59,8 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYea INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2081 MAPE_Forecast=0.1754 MAPE_Test=0.1376 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1878 SMAPE_Forecast=0.1652 SMAPE_Test=0.1273 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8935 MASE_Forecast=0.6558 MASE_Test=0.7555 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7853255022456975 L1_Forecast=0.5090869547534583 L1_Test=0.3571242469544766 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.0852787638085175 L2_Forecast=0.6110280316443443 L2_Test=0.4637533924008258 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7853255022456977 L1_Forecast=0.5090869547534579 L1_Test=0.3571242469544765 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.0852787638085177 L2_Forecast=0.611028031644344 L2_Test=0.46375339240082575 INFO:pyaf.std:MODEL_COMPLEXITY 106 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:FISCHER_TRANSFORMATION Fisher @@ -54,25 +72,37 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:SEASONAL_MODEL_VALUES Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear 0.014527460023849637 {1: -0.01343444600648351, 2: 0.04096922141921777, 3: 0.027369108269639536, 4: 0.09008116078415807, 5: 0.015584058066350215, 6: 0.11861022293715512, 7: 0.09835514712302729, 8: -0.03525067544271432, 9: 0.017472078193010665, 10: -0.043725923603387395, 11: -0.18521315810119632, 12: -0.05891818402654648} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag1 -0.8965046088209871 -INFO:pyaf.std:AR_MODEL_COEFF 2 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag2 -0.7662621355859593 -INFO:pyaf.std:AR_MODEL_COEFF 3 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag3 -0.6449791035479122 +INFO:pyaf.std:AR_MODEL_COEFF 1 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag1 -0.8965046088209867 +INFO:pyaf.std:AR_MODEL_COEFF 2 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag2 -0.766262135585958 +INFO:pyaf.std:AR_MODEL_COEFF 3 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag3 -0.6449791035479128 INFO:pyaf.std:AR_MODEL_COEFF 4 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag4 -0.6007285558996915 -INFO:pyaf.std:AR_MODEL_COEFF 5 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag5 -0.5747442937289653 -INFO:pyaf.std:AR_MODEL_COEFF 6 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag6 -0.48014745122434976 -INFO:pyaf.std:AR_MODEL_COEFF 7 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag7 -0.42847961516940336 -INFO:pyaf.std:AR_MODEL_COEFF 8 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag8 -0.4050976894209489 -INFO:pyaf.std:AR_MODEL_COEFF 9 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag9 -0.3756039602548654 -INFO:pyaf.std:AR_MODEL_COEFF 10 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag10 -0.2061140557240248 +INFO:pyaf.std:AR_MODEL_COEFF 5 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag5 -0.5747442937289651 +INFO:pyaf.std:AR_MODEL_COEFF 6 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag6 -0.4801474512243504 +INFO:pyaf.std:AR_MODEL_COEFF 7 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag7 -0.4284796151694037 +INFO:pyaf.std:AR_MODEL_COEFF 8 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag8 -0.40509768942094987 +INFO:pyaf.std:AR_MODEL_COEFF 9 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag9 -0.37560396025486575 +INFO:pyaf.std:AR_MODEL_COEFF 10 Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_Lag10 -0.20611405572402564 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.307206630706787 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.38785767555236816 - Split Transformation ... TestMAPE TestMASE -0 None Fisher_Ozone ... 0.1376 0.7555 - -[1 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 29.717612504959106 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.7772376537322998 + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE 0 None Fisher_Ozone ... 0.1376 0.7555 1 None Fisher_Ozone ... 0.6036 2.8964 @@ -122,31 +152,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51)", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "Fisher", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51)", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "Fisher", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "106", - "MAE": "0.5090869547534583", - "MAPE": "0.1754", - "MASE": "0.6558", - "RMSE": "0.6110280316443443" + "Model_Performance": { + "COMPLEXITY": "106", + "MAE": "0.5090869547534579", + "MAPE": "0.1754", + "MASE": "0.6558", + "RMSE": "0.611028031644344" + } } } diff --git a/tests/references/transformations_test_ozone_transf_logit.log b/tests/references/transformations_test_ozone_transf_logit.log index a26271f6a..d2016adad 100644 --- a/tests/references/transformations_test_ozone_transf_logit.log +++ b/tests/references/transformations_test_ozone_transf_logit.log @@ -1,10 +1,28 @@ INFO:pyaf.std:START_TRAINING 'Ozone' - Month Ozone Time -0 1955-01 2.7 1955-01-01 -1 1955-02 2.0 1955-02-01 -2 1955-03 3.6 1955-03-01 -3 1955-04 5.0 1955-04-01 -4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.5844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.8228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.8362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.1805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.1851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.1653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.1698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.1446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.1446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3928 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.4365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.5685 INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2037 INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.2675 INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Cycle_AR 78 0.2469 @@ -29,7 +47,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_ INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_Cycle_None_NoAR 56 0.3443 INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_NoCycle_AR 86 0.1676 INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_NoCycle_NoAR 48 0.3443 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 2.25596284866333 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.450144290924072 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Logit_Ozone' Min=-18.420680733952366 Max=18.420680728927607 Mean=-0.7577986766139785 StdDev=2.1128151505874566 @@ -41,39 +59,50 @@ INFO:pyaf.std:AUTOREG_DETAIL 'Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2046 MAPE_Forecast=0.1676 MAPE_Test=0.3468 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1896 SMAPE_Forecast=0.1696 SMAPE_Test=0.2819 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8972 MASE_Forecast=0.6483 MASE_Test=1.7761 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7885805207550324 L1_Forecast=0.5032826557074455 L1_Test=0.8396136811440966 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.0618144370973117 L2_Forecast=0.7132428843745128 L2_Test=0.9939582459820111 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.788580520755032 L1_Forecast=0.5032826557074461 L1_Test=0.8396136811441082 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.0618144370973115 L2_Forecast=0.7132428843745129 L2_Test=0.9939582459820223 INFO:pyaf.std:MODEL_COMPLEXITY 86 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:LOGIT_TRANSFORMATION Logit INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (0.5095325104061865, array([-2.14277109, -0.44089955, 1.00367229])) +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (0.5095325104061885, array([-2.14277109, -0.44089955, 1.00367229])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Logit_Ozone_PolyTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag12 0.16358005051351368 -INFO:pyaf.std:AR_MODEL_COEFF 2 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag10 0.14490946176213404 -INFO:pyaf.std:AR_MODEL_COEFF 3 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag11 0.12973182123610008 -INFO:pyaf.std:AR_MODEL_COEFF 4 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag34 0.10515555748415556 -INFO:pyaf.std:AR_MODEL_COEFF 5 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag19 -0.10480329887141261 -INFO:pyaf.std:AR_MODEL_COEFF 6 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag23 -0.09396856522120103 -INFO:pyaf.std:AR_MODEL_COEFF 7 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag20 -0.09284133154226264 -INFO:pyaf.std:AR_MODEL_COEFF 8 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag40 -0.07569390793388837 -INFO:pyaf.std:AR_MODEL_COEFF 9 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag35 0.0711867173549641 -INFO:pyaf.std:AR_MODEL_COEFF 10 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag18 -0.07058820739983523 +INFO:pyaf.std:AR_MODEL_COEFF 1 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag12 0.1635800505135136 +INFO:pyaf.std:AR_MODEL_COEFF 2 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag10 0.14490946176213398 +INFO:pyaf.std:AR_MODEL_COEFF 3 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag11 0.12973182123610016 +INFO:pyaf.std:AR_MODEL_COEFF 4 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag34 0.1051555574841555 +INFO:pyaf.std:AR_MODEL_COEFF 5 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag19 -0.10480329887141256 +INFO:pyaf.std:AR_MODEL_COEFF 6 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag23 -0.09396856522120106 +INFO:pyaf.std:AR_MODEL_COEFF 7 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag20 -0.09284133154226261 +INFO:pyaf.std:AR_MODEL_COEFF 8 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag40 -0.0756939079338883 +INFO:pyaf.std:AR_MODEL_COEFF 9 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag35 0.07118671735496414 +INFO:pyaf.std:AR_MODEL_COEFF 10 Logit_Ozone_PolyTrend_residue_zeroCycle_residue_Lag18 -0.07058820739983519 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.513185262680054 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.39492321014404297 - Split Transformation ... TestMAPE TestMASE -0 None Logit_Ozone ... 0.3468 1.7761 -1 None Logit_Ozone ... 0.3468 1.7761 - -[2 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.214606523513794 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.581178903579712 + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE 0 None Logit_Ozone ... 0.3468 1.7761 1 None Logit_Ozone ... 0.3468 1.7761 @@ -124,31 +153,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Logit", + "Trend": "PolyTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "Logit", - "Trend": "PolyTrend" - }, - "Model_Performance": { - "COMPLEXITY": "86", - "MAE": "0.5032826557074455", - "MAPE": "0.1676", - "MASE": "0.6483", - "RMSE": "0.7132428843745128" + "Model_Performance": { + "COMPLEXITY": "86", + "MAE": "0.5032826557074461", + "MAPE": "0.1676", + "MASE": "0.6483", + "RMSE": "0.7132428843745129" + } } } diff --git a/tests/references/transformations_test_ozone_transf_none.log b/tests/references/transformations_test_ozone_transf_none.log index 5bb0d4e2e..38dd90826 100644 --- a/tests/references/transformations_test_ozone_transf_none.log +++ b/tests/references/transformations_test_ozone_transf_none.log @@ -1,10 +1,28 @@ INFO:pyaf.std:START_TRAINING 'Ozone' - Month Ozone Time -0 1955-01 2.7 1955-01-01 -1 1955-02 2.0 1955-02-01 -2 1955-03 3.6 1955-03-01 -3 1955-04 5.0 1955-04-01 -4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.2081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 @@ -29,7 +47,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cy INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.1657 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 1.9724540710449219 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.3405561447143555 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -41,8 +59,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6135978624272591 L1_Forecast=0.5265316758013037 L1_Test=0.42992050508902385 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507152 L2_Test=0.551943269559555 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -54,28 +72,37 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033754 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533328 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225498 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.1612820579538937 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046368 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481863 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102252 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045825 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947768 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.3249053955078125 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3377659320831299 - Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.174 0.9094 -2 None _Ozone ... 0.343 1.6728 -1 None _Ozone ... 0.174 0.9094 -3 None _Ozone ... 0.343 1.6728 - -[4 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.26435351371765 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.8915731906890869 + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.1740 0.9094 1 None _Ozone ... 0.1740 0.9094 @@ -125,31 +152,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013037", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507152" + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/transformations_test_ozone_transf_quantization.log b/tests/references/transformations_test_ozone_transf_quantization.log index d41a314f2..8e2f19e7b 100644 --- a/tests/references/transformations_test_ozone_transf_quantization.log +++ b/tests/references/transformations_test_ozone_transf_quantization.log @@ -5,6 +5,78 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.2021 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.2153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.2347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1622 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2127 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2127 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1722 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.1988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.194 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.329 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.2482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.552 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.8118 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.1956 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.1177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.1522 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.1522 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1048 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.16 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.3126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.4954 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.1833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.7431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.7431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.1359 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.166 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.166 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1754 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1582 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.1582 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.3362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.5295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.676 INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_AR 106 0.2924 INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_NoAR 68 0.2777 INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Cycle_None_AR 110 0.2458 @@ -77,7 +149,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantizat INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_Cycle_None_NoAR 88 0.3985 INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_NoCycle_AR 118 0.1753 INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_NoCycle_NoAR 80 0.3985 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.486119747161865 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 9.026864528656006 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Quantized_10_Ozone' Min=0 Max=9 Mean=4.96078431372549 StdDev=2.838535830205739 @@ -102,28 +174,31 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Quantized_10_Ozone_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5179386631617309 -INFO:pyaf.std:AR_MODEL_COEFF 2 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag36 0.323493367286425 -INFO:pyaf.std:AR_MODEL_COEFF 3 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag35 -0.20994310628571544 -INFO:pyaf.std:AR_MODEL_COEFF 4 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.19300251058817475 -INFO:pyaf.std:AR_MODEL_COEFF 5 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag3 0.1763236798209317 -INFO:pyaf.std:AR_MODEL_COEFF 6 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.14444731256046386 -INFO:pyaf.std:AR_MODEL_COEFF 7 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.1350651506781613 -INFO:pyaf.std:AR_MODEL_COEFF 8 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag6 0.1343716865865286 -INFO:pyaf.std:AR_MODEL_COEFF 9 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.12727227701890842 -INFO:pyaf.std:AR_MODEL_COEFF 10 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag44 0.12674362266822553 +INFO:pyaf.std:AR_MODEL_COEFF 1 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5179386631617311 +INFO:pyaf.std:AR_MODEL_COEFF 2 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag36 0.3234933672864252 +INFO:pyaf.std:AR_MODEL_COEFF 3 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag35 -0.2099431062857161 +INFO:pyaf.std:AR_MODEL_COEFF 4 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag43 -0.19300251058817494 +INFO:pyaf.std:AR_MODEL_COEFF 5 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag3 0.17632367982093128 +INFO:pyaf.std:AR_MODEL_COEFF 6 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag51 -0.14444731256046375 +INFO:pyaf.std:AR_MODEL_COEFF 7 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.13506515067816172 +INFO:pyaf.std:AR_MODEL_COEFF 8 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag6 0.13437168658652898 +INFO:pyaf.std:AR_MODEL_COEFF 9 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.12727227701890848 +INFO:pyaf.std:AR_MODEL_COEFF 10 Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_Lag44 0.12674362266822545 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.341250658035278 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.34891390800476074 - Split Transformation ... TestMAPE TestMASE -3 None Quantized_10_Ozone ... 0.1956 1.0207 -2 None Quantized_10_Ozone ... 0.2001 1.0365 -0 None Quantized_20_Ozone ... 0.2120 1.1723 -1 None Quantized_20_Ozone ... 0.2120 1.1723 - -[4 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 23.262348651885986 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.9138641357421875 Split Transformation ... TestMAPE TestMASE 0 None Quantized_20_Ozone ... 0.2120 1.1723 1 None Quantized_20_Ozone ... 0.2120 1.1723 @@ -175,31 +250,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Quantization", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "Quantization", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "102", - "MAE": "0.5961538461538463", - "MAPE": "0.185", - "MASE": "0.7679", - "RMSE": "0.7479116223671048" + "Model_Performance": { + "COMPLEXITY": "102", + "MAE": "0.5961538461538463", + "MAPE": "0.185", + "MASE": "0.7679", + "RMSE": "0.7479116223671048" + } } } diff --git a/tests/references/transformations_test_ozone_transf_relative_difference.log b/tests/references/transformations_test_ozone_transf_relative_difference.log index 864622d9e..e39cd2cdb 100644 --- a/tests/references/transformations_test_ozone_transf_relative_difference.log +++ b/tests/references/transformations_test_ozone_transf_relative_difference.log @@ -1,10 +1,28 @@ INFO:pyaf.std:START_TRAINING 'Ozone' - Month Ozone Time -0 1955-01 2.7 1955-01-01 -1 1955-02 2.0 1955-02-01 -2 1955-03 3.6 1955-03-01 -3 1955-04 5.0 1955-04-01 -4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 360.8514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 3289621.0774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 16702.3246 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 13195.3873 @@ -29,7 +47,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDiffer INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 5103920.0962 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 24525015.6551 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 1.9094679355621338 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 6.249514102935791 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='RelDiff_Ozone' Min=-0.9999998124999999 Max=9.99999999999999 Mean=0.20126946254526706 StdDev=1.0261138315148053 @@ -56,13 +74,25 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.193049907684326 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.19238972663879395 - Split Transformation ... TestMAPE TestMASE -0 None RelDiff_Ozone ... 0.2132 1.0048 - -[1 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 23.5058376789093 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4690079689025879 + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE 0 None RelDiff_Ozone ... 0.2132 1.0048 1 None RelDiff_Ozone ... 0.4135 2.4045 @@ -113,31 +143,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "RelativeDifference", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "RelativeDifference", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "64", - "MAE": "0.7999999999999998", - "MAPE": "0.2778", - "MASE": "1.0305", - "RMSE": "0.9658263367813654" + "Model_Performance": { + "COMPLEXITY": "64", + "MAE": "0.7999999999999998", + "MAPE": "0.2778", + "MASE": "1.0305", + "RMSE": "0.9658263367813654" + } } } diff --git a/tests/references/transformations_test_ozone_transf_relative_difference_1.log b/tests/references/transformations_test_ozone_transf_relative_difference_1.log index 32239ec35..2af13d6ff 100644 --- a/tests/references/transformations_test_ozone_transf_relative_difference_1.log +++ b/tests/references/transformations_test_ozone_transf_relative_difference_1.log @@ -1,19 +1,12 @@ INFO:pyaf.std:START_TRAINING 'signal2' - -RangeIndex: 1200 entries, 0 to 1199 -Data columns (total 2 columns): - # Column Non-Null Count Dtype ---- ------ -------------- ----- - 0 time 1200 non-null int64 - 1 rate 1200 non-null float64 -dtypes: float64(1), int64(1) -memory usage: 18.9 KB - time rate signal2 -0 0 0.2 1.0000 -1 1 0.2 1.2000 -2 2 0.2 1.4400 -3 3 0.2 1.7280 -4 4 0.2 2.0736 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_Lag1Trend_residue_zeroCycle_residue_NoAR 64 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2 RelativeDifference_ConstantTrend_Cycle_NoAR 40 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2 RelativeDifference_ConstantTrend_NoCycle_NoAR 32 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2 RelativeDifference_Lag1Trend_Cycle_NoAR 72 1.0 @@ -22,7 +15,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2 RelativeDiff INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2 RelativeDifference_LinearTrend_NoCycle_NoAR 48 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2 RelativeDifference_PolyTrend_Cycle_NoAR 56 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2 RelativeDifference_PolyTrend_NoCycle_NoAR 48 1.0 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'signal2' 1.1143507957458496 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['signal2']' 3.0696237087249756 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=0 TimeMax=949 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='signal2' Length=1200 Min=1.0 Max=8.675889481882477e+94 Mean=4.337944740941239e+92 StdDev=4.510021374694525e+93 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='RelDiff_signal2' Min=0.0 Max=0.20000000000000018 Mean=0.01683851899113984 StdDev=0.055526238969589435 @@ -49,18 +42,35 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.178597450256348 -INFO:pyaf.std:START_FORECASTING 'signal2' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'signal2' 0.21614551544189453 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.907304525375366 +INFO:pyaf.std:START_FORECASTING '['signal2']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['signal2']' 0.7352561950683594 INFO:pyaf.std:START_TRAINING 'signal2' - Split Transformation ... TestMAPE TestMASE -0 None RelDiff_signal2 ... 1.0 5.6426 -1 None RelDiff_signal2 ... 1.0 5.6426 -2 None RelDiff_signal2 ... 1.0 5.6426 -3 None RelDiff_signal2 ... 1.0 5.6426 -4 None RelDiff_signal2 ... 1.0 5.6426 - -[5 rows x 20 columns] + +RangeIndex: 1200 entries, 0 to 1199 +Data columns (total 2 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 1200 non-null int64 + 1 rate 1200 non-null float64 +dtypes: float64(1), int64(1) +memory usage: 18.9 KB + time rate signal2 +0 0 0.2 1.0000 +1 1 0.2 1.2000 +2 2 0.2 1.4400 +3 3 0.2 1.7280 +4 4 0.2 2.0736 Split Transformation ... TestMAPE TestMASE 0 None RelDiff_signal2 ... 1.0 5.6426 1 None RelDiff_signal2 ... 1.0 5.6426 @@ -115,31 +125,33 @@ Forecasts { - "Dataset": { - "Signal": "signal2", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "0", - "1199" - ], - "TimeVariable": "time" + "signal2": { + "Dataset": { + "Signal": "signal2", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "0", + "1199" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 1200 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "RelDiff_signal2_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "RelativeDifference", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 1200 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "RelDiff_signal2_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "RelativeDifference", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "32", - "MAE": "2.453089264459579e+92", - "MAPE": "1.0", - "MASE": "5.9748", - "RMSE": "1.1410521347259163e+93" + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "2.453089264459579e+92", + "MAPE": "1.0", + "MASE": "5.9748", + "RMSE": "1.1410521347259163e+93" + } } } @@ -167,36 +179,92 @@ memory usage: 18.9 KB 2 2 0.2 1.4400 3 3 0.2 1.7280 4 4 0.2 2.0736 -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/scipy/stats/stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined. +/usr/lib/python3/dist-packages/scipy/stats/stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined. warnings.warn(PearsonRConstantInputWarning()) -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/scipy/stats/stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined. +/usr/lib/python3/dist-packages/scipy/stats/stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined. warnings.warn(PearsonRConstantInputWarning()) -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/scipy/stats/stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined. +/usr/lib/python3/dist-packages/scipy/stats/stats.py:3508: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined. warnings.warn(PearsonRConstantInputWarning()) -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom -/home/travis/virtualenv/python3.7.1/lib/python3.7/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide +/home/antoine/.local/lib/python3.8/site-packages/sklearn/feature_selection/_univariate_selection.py:307: RuntimeWarning: divide by zero encountered in true_divide F = corr ** 2 / (1 - corr ** 2) * degrees_of_freedom +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 72 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_ConstantTrend_residue_zeroCycle_residue_AR(64) 64 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_ConstantTrend_residue_zeroCycle_residue_NoAR 0 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_Lag1Trend_residue_zeroCycle_residue_AR(64) 96 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_LinearTrend_residue_zeroCycle_residue_AR(64) 80 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_LinearTrend_residue_zeroCycle_residue_NoAR 16 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 88 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_PolyTrend_residue_zeroCycle_residue_AR(64) 80 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2_PolyTrend_residue_zeroCycle_residue_NoAR 16 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_signal2_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_Lag1Trend_residue_zeroCycle_residue_NoAR 64 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_signal2_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64) 104 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_ConstantTrend_residue_zeroCycle_residue_AR(64) 96 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(64) 136 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_Lag1Trend_residue_zeroCycle_residue_AR(64) 128 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_LinearTrend_residue_zeroCycle_residue_AR(64) 112 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_LinearTrend_residue_zeroCycle_residue_NoAR 48 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_PolyTrend_residue_bestCycle_byMAPE_residue_AR(64) 120 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_PolyTrend_residue_zeroCycle_residue_AR(64) 112 0.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2 NoTransf_ConstantTrend_Cycle_None_AR 72 0.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2 NoTransf_ConstantTrend_Cycle_None_NoAR 8 0.9999 INFO:pyaf.std:collectPerformanceIndices : MAPE None _signal2 NoTransf_ConstantTrend_NoCycle_AR 64 0.0 @@ -253,7 +321,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2 Integration_P INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2 Integration_PolyTrend_Cycle_NoAR 56 1.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2 Integration_PolyTrend_NoCycle_AR 112 0.0 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_signal2 Integration_PolyTrend_NoCycle_NoAR 48 1.0 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'signal2' 6.445068836212158 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['signal2']' 6.34796142578125 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=0 TimeMax=949 TimeDelta=1 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='signal2' Length=1200 Min=1.0 Max=8.675889481882477e+94 Mean=4.337944740941239e+92 StdDev=4.510021374694525e+93 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_signal2' Min=1.0 Max=8.675889481882477e+94 Mean=4.337944740941239e+92 StdDev=4.510021374694525e+93 @@ -262,11 +330,11 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_signal2_ConstantTrend_residue_zeroCycle_resi INFO:pyaf.std:TREND_DETAIL '_signal2_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_signal2_ConstantTrend_residue_zeroCycle' [NoCycle] INFO:pyaf.std:AUTOREG_DETAIL '_signal2_ConstantTrend_residue_zeroCycle_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=7.928970612687529e+55 MAPE_Forecast=0.0 MAPE_Test=0.0 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.5519 SMAPE_Forecast=0.0 SMAPE_Test=0.0 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=3.964485306343764e+56 MAPE_Forecast=0.0 MAPE_Test=0.0 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.5727 SMAPE_Forecast=0.0 SMAPE_Test=0.0 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0 MASE_Forecast=0.0 MASE_Test=0.0 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.8130711286210687e+58 L1_Forecast=1.3043673197343275e+77 L1_Test=1.9761849896501963e+79 -INFO:pyaf.std:MODEL_L2 L2_Fit=5.103024688716559e+58 L2_Forecast=6.131656722290081e+77 L2_Test=2.3917911106063396e+79 +INFO:pyaf.std:MODEL_L1 L1_Fit=7.035175700142112e+58 L1_Forecast=3.423470427508168e+77 L1_Test=3.303934279571422e+79 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.3918081196822119e+59 L2_Forecast=1.6791091357026567e+78 L2_Test=3.9929303231653496e+79 INFO:pyaf.std:MODEL_COMPLEXITY 64 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -278,29 +346,21 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _signal2_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag8 -2.6105515134028723 -INFO:pyaf.std:AR_MODEL_COEFF 2 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag4 1.8302024330845996 -INFO:pyaf.std:AR_MODEL_COEFF 3 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag9 -1.306273076365426 -INFO:pyaf.std:AR_MODEL_COEFF 4 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag2 1.2667182484590191 -INFO:pyaf.std:AR_MODEL_COEFF 5 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag7 0.9707621578489987 -INFO:pyaf.std:AR_MODEL_COEFF 6 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag5 0.8444827144374366 -INFO:pyaf.std:AR_MODEL_COEFF 7 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag10 -0.7785541102280658 -INFO:pyaf.std:AR_MODEL_COEFF 8 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag63 -0.7350977005186192 -INFO:pyaf.std:AR_MODEL_COEFF 9 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag20 -0.7010855202400273 -INFO:pyaf.std:AR_MODEL_COEFF 10 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.6517983351438073 +INFO:pyaf.std:AR_MODEL_COEFF 1 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag8 3.0786202220473324 +INFO:pyaf.std:AR_MODEL_COEFF 2 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag3 -1.9373612855883922 +INFO:pyaf.std:AR_MODEL_COEFF 3 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag6 1.8935090410020272 +INFO:pyaf.std:AR_MODEL_COEFF 4 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag26 -1.7474064133346323 +INFO:pyaf.std:AR_MODEL_COEFF 5 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag18 -1.7323020529197488 +INFO:pyaf.std:AR_MODEL_COEFF 6 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag5 1.6814388357912398 +INFO:pyaf.std:AR_MODEL_COEFF 7 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag17 -1.5095594353069306 +INFO:pyaf.std:AR_MODEL_COEFF 8 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag11 -1.5031099600327844 +INFO:pyaf.std:AR_MODEL_COEFF 9 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag25 1.4134970150156743 +INFO:pyaf.std:AR_MODEL_COEFF 10 _signal2_ConstantTrend_residue_zeroCycle_residue_Lag20 -1.3087152583144221 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.442875623703003 -INFO:pyaf.std:START_FORECASTING 'signal2' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'signal2' 0.6613953113555908 - Split Transformation ... TestMAPE TestMASE -0 None _signal2 ... 0.0 0.0 -1 None _signal2 ... 0.0 0.0 -2 None _signal2 ... 0.0 0.0 -3 None _signal2 ... 0.0 0.0 -4 None _signal2 ... 0.0 0.0 - -[5 rows x 20 columns] +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 6.498476982116699 +INFO:pyaf.std:START_FORECASTING '['signal2']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['signal2']' 1.44425630569458 Split Transformation ... TestMAPE TestMASE 0 None _signal2 ... 0.0000 0.0000 1 None _signal2 ... 0.0000 0.0000 @@ -401,31 +461,33 @@ Forecasts { - "Dataset": { - "Signal": "signal2", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "0", - "1199" - ], - "TimeVariable": "time" + "signal2": { + "Dataset": { + "Signal": "signal2", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "0", + "1199" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 1200 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_signal2_ConstantTrend_residue_zeroCycle_residue_AR(64)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 1200 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_signal2_ConstantTrend_residue_zeroCycle_residue_AR(64)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "64", - "MAE": "1.3043673197343275e+77", - "MAPE": "0.0", - "MASE": "0.0", - "RMSE": "6.131656722290081e+77" + "Model_Performance": { + "COMPLEXITY": "64", + "MAE": "3.423470427508168e+77", + "MAPE": "0.0", + "MASE": "0.0", + "RMSE": "1.6791091357026567e+78" + } } } diff --git a/tests/references/xeon-phi-parallel_test_ozone_too_many_threads.log b/tests/references/xeon-phi-parallel_test_ozone_too_many_threads.log index e585a4e11..6cbf3a4e8 100644 --- a/tests/references/xeon-phi-parallel_test_ozone_too_many_threads.log +++ b/tests/references/xeon-phi-parallel_test_ozone_too_many_threads.log @@ -1,5 +1,4 @@ INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.5, 0.1, 0.0) Month Ozone Time 0 1955-01 2.7 1955-01-01 1 1955-02 2.0 1955-02-01 @@ -22,7967 +21,3423 @@ Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 33 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 8 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 33 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 33 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 25 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 25 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 25 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 49 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 49 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 49 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 41 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 41 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 41 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 49 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 49 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 49 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 41 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 41 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 41 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 137 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 121 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 57 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 89 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 73 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 105 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.5, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 105 None -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.5, 0.1, 0.0) 36.28922891616821 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.6000000000000001, 0.1, 0.0) -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 34 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 34 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 34 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 38 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 8 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 38 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 38 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 30 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 30 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 30 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 50 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 50 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 50 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 54 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 54 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 54 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 46 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 46 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 46 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 50 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 50 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 50 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 54 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 54 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 54 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 46 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 46 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 46 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 146 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 150 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 142 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 130 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 134 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 126 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 66 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 70 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 62 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 98 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 102 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 94 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 82 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 86 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 78 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 114 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 118 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 110 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.6000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 110 None -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.6000000000000001, 0.1, 0.0) 49.319419384002686 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.7000000000000001, 0.1, 0.0) -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 39 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 39 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 39 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 43 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 8 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 43 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 43 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 35 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 35 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 35 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 55 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 55 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 55 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 59 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 59 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 59 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 51 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 51 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 51 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 55 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 55 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 55 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 59 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 59 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 59 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 51 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 51 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 51 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 151 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 155 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 147 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 135 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 139 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 131 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 71 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 75 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 67 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 103 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 107 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 99 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 87 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 91 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 83 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 119 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 123 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 115 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.7000000000000001, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 115 None -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.7000000000000001, 0.1, 0.0) 33.72835969924927 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.8, 0.1, 0.0) -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 44 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 44 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 44 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 8 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 60 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 60 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 60 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 60 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 60 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 60 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 156 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 160 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 152 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 140 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 144 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 136 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 76 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 108 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 92 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 124 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 128 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.8, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 120 None -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.8, 0.1, 0.0) 29.684478282928467 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SIGNAL_SPLIT 'Ozone' (0.9, 0.1, 0.0) -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -Using TensorFlow backend. -WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 49 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 49 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 49 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 53 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 8 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 53 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 53 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 45 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 45 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 45 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 69 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 69 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 69 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 61 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 61 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 61 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 65 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 69 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 24 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 69 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 69 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 61 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 61 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) _Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 61 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Diff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 161 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 120 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 165 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 157 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 145 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 104 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 149 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 141 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Logit_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 81 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_AR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_NoAR 40 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_SVR(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_bestCycle_byL2_residue_XGB(51) 85 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 77 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 113 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_AR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_NoAR 72 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_SVR(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_bestCycle_byL2_residue_XGB(51) 117 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 109 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 97 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_AR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_NoAR 56 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_SVR(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_bestCycle_byL2_residue_XGB(51) 101 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 93 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 129 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_AR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_NoAR 88 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_SVR(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_bestCycle_byL2_residue_XGB(51) 133 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_AR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 125 None -INFO:pyaf.std:collectPerformanceIndices : MAPE (0.9, 0.1, 0.0) MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 125 None -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_SPLIT_TIME_IN_SECONDS 'Ozone' (0.9, 0.1, 0.0) 32.19541311264038 -INFO:pyaf.std:CROSS_VALIDATION_TRAINING_TIME_IN_SECONDS _Ozone 181.25722694396973 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 181.81562447547913 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1963-06-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.2108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.1981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.2292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.207 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 94 0.1647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 56 0.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.2073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.1805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 86 0.1647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 48 0.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 86 0.2073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 86 0.1805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.2429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.2169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.2179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 94 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 56 0.2429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.2169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.2179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 86 0.1631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 48 0.4078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 86 0.2447 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 86 0.2358 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.2277 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.2169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.1992 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 94 0.2277 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 56 0.2169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.1992 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 86 0.1614 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 48 0.4304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 86 0.2426 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 86 0.2451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.1916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.2206 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.1685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 94 0.1825 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 56 0.3329 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.2125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.2181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 86 0.1825 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 48 0.3329 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 86 0.2125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 86 0.2181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.2601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.3307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.2851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.2865 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 94 0.2133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 56 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.3046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.2624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 86 0.2133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 48 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 86 0.3046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 86 0.2624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.2145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.29 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.1734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.1513 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 94 0.2145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 56 0.29 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.1734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.1513 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 86 0.2128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 48 0.4347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 86 0.2423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 86 0.2573 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 42 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 4 0.586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 42 0.5203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 42 0.433 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 46 0.283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 8 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 46 0.4442 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 46 0.2798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 38 0.2837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 0 0.8365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 38 0.417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 38 0.3048 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.1221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 0.2223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 0.183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2085 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 40 0.1734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 78 0.187 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 78 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 70 0.2499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 32 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 70 0.2475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 70 0.2188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 58 0.2247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 58 0.2117 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.1713 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.3186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 62 0.2001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 62 0.211 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 54 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 16 0.3197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 54 0.2062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 54 0.1939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 58 0.3617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 20 0.5734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 58 0.3931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 58 0.39 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 62 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 24 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 62 0.4099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 62 0.3659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 54 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 16 0.7356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 54 0.4099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 54 0.3659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 3.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 0.9543 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 3.7028 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.294 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 5.4652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 78 0.9263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 78 1.1787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 70 2.4717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 70 0.9679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.7846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 2.8451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 1.0249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.5787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.4184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 8.9026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 110 2.7822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 110 1.3667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 102 2.8234 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 102 0.5081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 1.4061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 2.6097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 1.799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.8198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 1.2403 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 5.2465 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.2129 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.6365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3558 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2664 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 86 1.9046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 86 0.268 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 2.8925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 5.5115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 1.1919 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 4.3395 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 2.3745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 7.494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 2.6277 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 2.092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 1.6806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 2.0824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 86 4.3211 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 86 3.148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 6.1169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.7659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 4.1196 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 2.672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 1.8625 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 118 0.2256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 0.3282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 118 2.672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 118 1.8625 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 6.8094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 3.3694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 3.1732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1829 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 88 11.064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 9.7853 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 3.3226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 118 0.2014 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 1.6535 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 118 8.0688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 118 1.248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.6337 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 5.8595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 4.2368 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 1.3625 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 88 4.5326 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 5.4995 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 2.5561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 118 0.1877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 1.0276 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 118 6.2517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 118 2.3951 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 1.1431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 5.171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 4.6782 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 2.8046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.8602 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 88 11.7188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 3.8723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 5.9583 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 118 0.8602 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 11.7188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 118 3.8723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 118 5.9583 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 1.1652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.9205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.8639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 2.3138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.449 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 88 6.8016 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 6.8249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 1.3608 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 118 0.449 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 6.8016 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 118 6.8249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 118 1.3608 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.6095 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 1.4695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 4.4111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 2.9351 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.3189 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 88 5.7498 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 1.5963 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.5476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 118 0.3189 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 5.7498 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 118 1.5963 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 118 0.8257 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 124677.8973 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 14177.6669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 199557.3139 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 14040.2425 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 88 1533256.147 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 34002.7223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 118 3693.8728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 118 51881.9481 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 118 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 10555.2316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 29072.7136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 1.501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 10.5658 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 88 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 13.8379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 118 10.5658 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 118 13.8379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 118 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 71253.6949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 12.6158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 3720.4827 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 438354.7117 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 88 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 4569.3655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 118 438354.7117 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 118 4569.3655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 118 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 5239.9759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.3459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 222.1797 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 647.5876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 88 78.1279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 3051.3952 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 118 12479.7613 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 12867.5636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 118 1723.7178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 118 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.474 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.4442 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 57.6897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 9.1525 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 88 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.3145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 118 298.6809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 175.132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 118 1.8989 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 118 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 93.447 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.4577 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 1509.4687 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 211959.4844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 88 423.2483 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 285.8372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 118 211959.4844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 423.2483 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 118 285.8372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 118 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 2317.8767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.4135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 473.0097 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 47144203.9182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 78 3293.1119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 78 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 59.9208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 70 3293.1119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 70 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 674296.3114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 346920.1778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 141.7514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.4634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 110 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 301.0833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 102 0.4634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 102 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 28146.7091 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 13.3501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 2756.6141 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 21963.1328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 8115.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 86 21963.1328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 86 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 67040795.1914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 73316.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 2054099.7495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 45302841.8874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 51906819.6619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 12287028.0714 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 52181505.5431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 86 7377037.7134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 86 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.3598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 1.633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.2536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0031 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 78 1.187 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 78 1.0031 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 70 1.1841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 70 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.2581 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3828 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.2581 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 102 0.2217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 102 0.1844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.4864 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 1.375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.5152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.3321 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.6493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 86 0.5152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 86 0.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 1.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 1.2452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 86 1.2452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 86 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.4281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.374 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.373 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 1.6026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.3238 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 118 0.2636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 0.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 118 0.2591 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 118 0.5125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.3063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.3063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.3254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.3063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.3254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 118 0.3261 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 0.4078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 118 0.1247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 118 0.4213 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3387 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.3313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.3329 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 1.0284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.3387 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.3329 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 1.0284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 118 0.2295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 0.4304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 118 0.149 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 118 0.5726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.4657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.3878 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.3609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 1.9994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.4657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3878 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.3609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 1.9994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 118 0.2516 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 0.615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 118 0.3099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 118 1.5986 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.4926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.4146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.3349 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.4926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 88 0.4146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.3349 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 118 0.4507 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 0.4611 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 118 0.1649 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 118 0.423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.3998 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.3912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.7256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.3912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3998 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.3912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.7256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 118 0.2551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 0.6158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 118 0.2259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 118 0.897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 914548936.3549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5793 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.4385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 86636642.1693 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 914548936.3549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.5793 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.4385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 110 86636642.1693 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 457905282.46 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.2866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 102 0.6257 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 102 151614123.7189 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 212981745.0977 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 138 0.1979 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 138 263920881.8353 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 207927941.024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 142 0.2256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 142 151614123.6719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 207927941.0278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 134 0.227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 134 364595868.5756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 894333719.8679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 1062793857.1291 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2758 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2708 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 970906509.5883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 118 0.2722 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 118 0.2486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 894333719.866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2954 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 1062793857.1176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2853 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2757 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 970906509.5642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.2904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 118 0.2756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 118 0.2482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 285580042.2831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.3648 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.3633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.3605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 285580042.2831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3648 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.3633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.3605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 150 263920881.9438 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 0.3661 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 150 0.363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 150 0.3588 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.3381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 151614123.8475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.8011 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 91887347.8914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 150 151614123.8475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 0.3745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 150 0.8011 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 150 91887347.8914 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 212981745.1877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.3728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.4693 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 91887347.8721 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 212981745.1877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.4693 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 91887347.8721 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 150 0.6515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 0.4074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 150 310808953.6082 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 150 91887347.8698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 903318260.5948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.2309 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1903 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 441365559.8336 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.3427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1861 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 150 567710662.7396 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 0.338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 150 0.3416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 150 0.2069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 1097448514.0189 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.3154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.3263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2588 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 567710662.7553 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.4497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 86636642.1638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 150 567710662.7553 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 150 0.4497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 150 86636642.1638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 701370290.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.3145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.3128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2369 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 701370290.0416 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.3128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2369 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 150 441365559.9316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 0.4347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 150 0.4726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 150 112306758.4705 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 399250525.3599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.4372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 151614123.7003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.4113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.4731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.2135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 151614123.7003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.4113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 102 0.4731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 102 0.2135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.5899 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 138 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 138 0.2095 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 189517656.7698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 142 0.2402 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 142 0.2348 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 189517658.1386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 134 0.2424 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 134 0.1972 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 543119858.539 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.1995 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 485887167.3919 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.3111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 485887167.3919 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 118 0.3111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 118 0.1738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 543119858.4785 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.1829 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 485887167.3955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.3111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 485887167.3955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.2731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 118 0.3111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 118 0.169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.8489 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.2596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.7336 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.2902 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.2709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 150 0.4874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 0.2969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 150 0.2714 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 150 0.226 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2918 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.3494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.2918 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.3494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 150 0.4245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 0.3678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 150 0.4671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 150 0.2429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 121291299.1017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.4102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2699 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 121291299.1017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.4102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2699 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 150 0.4253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 0.3786 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 150 0.521 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 150 0.2531 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 304869093.0175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.2012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1742 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 277959228.3121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.3216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 150 277959228.3121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 0.3364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 150 0.3216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 150 0.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 639543231.9691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.3007 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 126345104.1538 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.4453 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1928 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 150 126345104.1538 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 150 0.4453 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 150 0.1928 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 126345103.3012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.2827 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 126345103.3012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.2792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.2827 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 150 151614123.7651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 0.4347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 150 0.4395 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 150 0.1695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.2354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.5539 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.3499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.6216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.3515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.1709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.3499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.6216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 102 0.3515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 102 0.1709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.2472 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 138 0.1486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 138 0.1805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.2787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 142 0.1817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 142 0.2108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.3774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 134 0.173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 134 0.2167 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1584 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1712 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.2815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 118 0.1712 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 118 0.1323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.3607 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.3033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.3608 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.3285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.3285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 118 0.2953 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 118 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1593 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1496 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1388 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1489 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2956 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.2839 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1275 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 150 0.2908 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 0.2722 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 150 0.1293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 150 0.1269 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1909 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1438 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.1909 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1438 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 150 0.2738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 0.3592 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 150 0.1534 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 150 0.1288 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2035 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.2035 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 150 0.2874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 0.3665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 150 0.15 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 150 0.1516 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1715 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1473 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1692 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.3049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 150 0.3049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 0.3347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 150 0.1456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 150 0.1459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.185 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1588 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 150 0.2479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 150 0.1727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 150 0.1815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.2013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 150 0.3434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 0.4347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 150 0.1555 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 150 0.1601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.3231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5882 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.6736 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.6136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.6824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.9695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.5646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.6574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 1.0024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 102 0.9212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 102 0.6008 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.2261 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 138 0.5481 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 138 0.2935 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.1461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 142 0.4919 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 142 0.3151 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.2082 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 134 0.5059 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 134 0.296 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.3668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2539 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2396 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2713 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.513 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.2396 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2713 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 118 0.513 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 118 0.2491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.6192 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.675 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.5931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.722 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.7993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 1.1805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.6357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.7216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.9564 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 118 1.1799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 118 0.6015 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.3029 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.5187 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.3283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3892 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.7699 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.4706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 150 0.1562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 0.3892 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 150 0.7699 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 150 0.4706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.6962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.3471 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1403 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.45 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.3718 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 150 0.1458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 0.4446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 150 0.3913 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 150 0.4 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2977 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.3474 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.3448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 150 0.1412 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 0.4721 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 150 0.3831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 150 0.3409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.3017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.6959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2037 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.7868 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2161 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 150 0.2037 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 0.3312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 150 0.7868 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 150 0.2161 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2721 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2564 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.436 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.3455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.6041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 150 0.2256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 150 0.6041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 150 0.2789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2791 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.593 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.3978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2324 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4643 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.4141 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.4051 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 150 0.1913 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 0.4347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 150 0.4466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 150 0.3154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1879 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1882 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2707 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.2884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.2019 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2421 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 150 0.2707 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 0.2884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 150 0.2019 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 150 0.2421 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1547 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2707 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1963 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 150 0.2707 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 0.4309 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 150 0.1963 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 150 0.217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1882 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.1925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1882 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 150 0.2707 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 0.3978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 150 0.1384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 150 0.2383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1622 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2807 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4204 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.2308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2573 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 150 0.2807 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 0.4204 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 150 0.2308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 150 0.2573 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.2436 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.3155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 150 0.3155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 0.4672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 150 0.2313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 150 0.218 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.2266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.2034 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.3164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.2229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2167 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 150 0.3164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 0.4466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 150 0.2229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 150 0.2167 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.2021 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.3463 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.2752 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.3545 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.2153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 102 0.3545 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 102 0.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.2347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1622 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 138 0.2379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 138 0.2053 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2127 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 142 0.219 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 142 0.2978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2127 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 134 0.219 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 134 0.2978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1722 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.1925 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.2884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.277 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.1988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.2884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 118 0.2299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 118 0.277 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.194 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.329 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2565 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.2482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 118 0.2544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 118 0.2565 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1392 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 150 0.1415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 0.3455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 150 0.1544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 150 0.1783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1359 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1367 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4238 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.2011 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1613 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 150 0.1518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 0.3963 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 150 0.1456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 150 0.1651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1457 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1457 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.1603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 150 0.1518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 0.394 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 150 0.1659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 150 0.2061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1992 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1581 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.3455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 150 0.1398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 0.3455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 150 0.1303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 150 0.2154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.2102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1722 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2029 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4039 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1928 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 150 0.2134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 0.4239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 150 0.1738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 150 0.2126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1747 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.137 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1747 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.1603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.137 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 150 0.1958 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 0.4328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 150 0.2076 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 150 0.1691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.552 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.4594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.3592 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.8118 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.3406 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.2842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.1956 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 102 0.3616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 102 0.1886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.1177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 138 0.1877 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 138 0.1467 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.1522 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 142 0.1531 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 142 0.2138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.1522 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 134 0.1531 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 134 0.2138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1048 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.1553 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1892 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2353 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.16 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.3126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 118 0.1636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 118 0.2511 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.4954 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.4335 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.4235 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.3725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.3472 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.1833 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 118 0.3725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 118 0.3472 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1237 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1593 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 150 0.1624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 112 0.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 150 0.1338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 150 0.1593 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.0847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.0847 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.1728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 150 0.1727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 112 0.4069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 150 0.1466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 150 0.1402 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1382 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1335 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4161 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 150 0.1667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 112 0.4161 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 150 0.1681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 150 0.1988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1552 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1531 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1988 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 120 0.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 150 0.1405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 112 0.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 150 0.138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 150 0.1834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1839 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1818 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1507 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.2013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.2291 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 120 0.4375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.2108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 150 0.2378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 112 0.4243 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 150 0.1663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 150 0.2314 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 154 0.1242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 116 0.1957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 154 0.1646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 154 0.1601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 158 0.1242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 120 0.1957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 158 0.1646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 158 0.1601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 150 0.2032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 112 0.4421 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 150 0.1556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 150 0.182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.5586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.4494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.3984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 72 0.7431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.4383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.2727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 102 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 64 0.7431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 102 0.4383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 102 0.2727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 138 0.1359 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 100 0.1201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 138 0.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 138 0.1726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 142 0.1697 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 104 0.2072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 142 0.1723 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 142 0.1536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 134 0.166 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 96 0.2266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 134 0.1584 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 134 0.2204 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1754 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1843 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1582 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.3362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1504 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1906 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 118 0.1582 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 80 0.3362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 118 0.1504 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 118 0.1906 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.5295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.4641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.3949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 126 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 88 0.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.4702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2858 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 118 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 80 0.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 118 0.4702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 118 0.2858 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.2106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.5844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 0.3978 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 0.3865 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.3834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.8228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 78 0.3982 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 78 0.3325 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.3668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.8362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 70 0.3915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 70 0.2518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.15 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.2483 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.1805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.2214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.2798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.1851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 102 0.1966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 102 0.3053 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.1653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.1698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.13 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.2476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.1446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.2746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.1214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.2248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.1446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.2746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 86 0.1214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 86 0.2248 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.3928 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.4365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.3599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.4912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.3468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.5685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.2679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.3179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.3468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.5685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 86 0.2679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 86 0.3179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.1923 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1315 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 88 0.2821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 118 0.1315 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 0.2821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 118 0.1361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 118 0.2631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 88 0.1706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 118 0.1407 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 0.3655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 118 0.1527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 118 0.1932 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1371 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2554 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 88 0.1768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1371 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2554 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 118 0.1399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 0.3768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 118 0.1474 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 118 0.1051 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1721 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1704 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 118 0.1476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 0.3342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 118 0.1397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 118 0.1704 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2039 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2079 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.1993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.166 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 88 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1541 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1696 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 118 0.166 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 118 0.1541 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 118 0.1696 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 88 0.2025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 118 0.1413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 0.4347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 118 0.1378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 118 0.1789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.5863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 0.2692 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 0.5212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.7609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 1.0701 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 78 0.5254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 78 0.4481 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.7609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 1.0701 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 70 0.5254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 70 0.4481 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.1376 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.1663 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.1846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.1672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.1709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.1452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.2346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.1939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 102 0.1788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 102 0.2516 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.6898 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.3044 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 1.0609 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.6497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.3437 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.2278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.3266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.6497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.3437 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 86 0.2278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 86 0.3266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.7714 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.6036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.4247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.5374 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.7282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.9415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.4698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.7282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.9415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 86 0.4698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 86 0.485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2654 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2442 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.29 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1116 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1714 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 118 0.1116 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 0.3287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 118 0.1714 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 118 0.2451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.3949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.4733 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 88 0.4357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2047 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 118 0.1223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 0.4138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 118 0.1518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 118 0.1555 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.3491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 88 0.342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.3491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 118 0.1179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 0.4378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 118 0.1713 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 118 0.2846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2273 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.2392 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1524 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 118 0.2392 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 0.3328 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 118 0.1524 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 118 0.2408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.4136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.3476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.3249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 88 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 118 0.3249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 118 0.181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 118 0.2493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.3023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.3303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.4051 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.3023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3303 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.4051 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 118 0.2003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 0.4347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 118 0.206 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 118 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 74 0.1746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR 36 0.5855 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 74 0.5605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 0.4752 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(51) 78 0.294 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR 40 0.8229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_SVR(51) 78 0.3389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 78 0.333 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(51) 70 0.2896 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR 32 0.7918 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_SVR(51) 70 0.3451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 70 0.3239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(51) 106 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR 68 0.1201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_SVR(51) 106 0.1635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.165 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(51) 110 0.2068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR 72 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_SVR(51) 110 0.1804 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.1443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(51) 102 0.2206 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR 64 0.2132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_SVR(51) 102 0.2158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 102 0.2121 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.1688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.2047 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.1652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.1787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.1382 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.336 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.1853 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.1525 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51) 86 0.1408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR 48 0.3234 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_SVR(51) 86 0.1871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 86 0.1893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(51) 90 0.368 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR 52 0.5468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_SVR(51) 90 0.4373 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.4431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(51) 94 0.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR 56 0.6712 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_SVR(51) 94 0.4399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.3163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_AR(51) 86 0.331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR 48 0.6712 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_SVR(51) 86 0.4399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 86 0.3163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2015 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2151 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_AR(51) 118 0.1298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_NoAR 80 0.3177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_SVR(51) 118 0.2025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(12)_residue_zeroCycle_residue_XGB(51) 118 0.1883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.1895 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_NoAR 88 0.1745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1895 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_AR(51) 118 0.1312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_NoAR 80 0.399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_SVR(51) 118 0.2326 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(5)_residue_zeroCycle_residue_XGB(51) 118 0.1515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1369 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1621 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1542 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.1886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1369 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_NoAR 88 0.1621 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1542 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_AR(51) 118 0.1287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_NoAR 80 0.4199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_SVR(51) 118 0.2292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingAverage(7)_residue_zeroCycle_residue_XGB(51) 118 0.1484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1785 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.1785 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1626 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.144 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_NoAR 88 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_AR(51) 118 0.1267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_NoAR 80 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_SVR(51) 118 0.1984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(12)_residue_zeroCycle_residue_XGB(51) 118 0.1886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.2054 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.2395 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.2192 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1708 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_NoAR 88 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.2479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.2063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_AR(51) 118 0.1708 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_NoAR 80 0.4242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_SVR(51) 118 0.2479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(5)_residue_zeroCycle_residue_XGB(51) 118 0.2063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_AR(51) 122 0.1568 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_NoAR 84 0.2242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_SVR(51) 122 0.1561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_Seasonal_MonthOfYear_residue_XGB(51) 122 0.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_AR(51) 126 0.1568 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_NoAR 88 0.2242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_SVR(51) 126 0.1561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_bestCycle_byMAPE_residue_XGB(51) 126 0.1308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_AR(51) 118 0.1636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_NoAR 80 0.4347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_SVR(51) 118 0.2124 +INFO:pyaf.std:collectPerformanceIndices : MAPE None MovingMedian(7)_residue_zeroCycle_residue_XGB(51) 118 0.1841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_Seasonal_MonthOfYear_AR 90 0.1615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 52 0.1714 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_Seasonal_MonthOfYear_SVR 90 0.1746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_Seasonal_MonthOfYear_XGB 90 0.1519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_Cycle_None_AR 94 0.1809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_Cycle_None_NoAR 56 0.3364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_Cycle_None_SVR 94 0.1453 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_Cycle_None_XGB 94 0.1439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_NoCycle_AR 86 0.1809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_NoCycle_NoAR 48 0.3364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_NoCycle_SVR 86 0.1453 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(12)_NoCycle_XGB 86 0.1439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_Seasonal_MonthOfYear_AR 90 0.1977 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 52 0.1772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_Seasonal_MonthOfYear_SVR 90 0.1813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_Seasonal_MonthOfYear_XGB 90 0.188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_Cycle_AR 94 0.1977 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_Cycle_NoAR 56 0.1772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_Cycle_SVR 94 0.1813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_Cycle_XGB 94 0.188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_NoCycle_AR 86 0.163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_NoCycle_NoAR 48 0.4655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_NoCycle_SVR 86 0.1398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(5)_NoCycle_XGB 86 0.1726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_Seasonal_MonthOfYear_AR 90 0.1649 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 52 0.1597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_Seasonal_MonthOfYear_SVR 90 0.1735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_Seasonal_MonthOfYear_XGB 90 0.1755 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_Cycle_AR 94 0.1649 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_Cycle_NoAR 56 0.1597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_Cycle_SVR 94 0.1735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_Cycle_XGB 94 0.1755 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_NoCycle_AR 86 0.1696 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_NoCycle_NoAR 48 0.4724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_NoCycle_SVR 86 0.1388 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingAverage(7)_NoCycle_XGB 86 0.1708 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_Seasonal_MonthOfYear_AR 90 0.1857 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 52 0.1593 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_Seasonal_MonthOfYear_SVR 90 0.1672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_Seasonal_MonthOfYear_XGB 90 0.1603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_Cycle_None_AR 94 0.1746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_Cycle_None_NoAR 56 0.339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_Cycle_None_SVR 94 0.1556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_Cycle_None_XGB 94 0.1854 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_NoCycle_AR 86 0.1746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_NoCycle_NoAR 48 0.339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_NoCycle_SVR 86 0.1556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(12)_NoCycle_XGB 86 0.1854 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_Seasonal_MonthOfYear_AR 90 0.2292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 52 0.1903 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_Seasonal_MonthOfYear_SVR 90 0.2254 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_Seasonal_MonthOfYear_XGB 90 0.2181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_Cycle_None_AR 94 0.1636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_Cycle_None_NoAR 56 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_Cycle_None_SVR 94 0.164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_Cycle_None_XGB 94 0.1883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_NoCycle_AR 86 0.1636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_NoCycle_NoAR 48 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_NoCycle_SVR 86 0.164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(5)_NoCycle_XGB 86 0.1883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_Seasonal_MonthOfYear_AR 90 0.2106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 52 0.1768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_Seasonal_MonthOfYear_SVR 90 0.2049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_Seasonal_MonthOfYear_XGB 90 0.1728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_Cycle_AR 94 0.2106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_Cycle_NoAR 56 0.1768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_Cycle_SVR 94 0.2049 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_Cycle_XGB 94 0.1728 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_NoCycle_AR 86 0.1789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_NoCycle_NoAR 48 0.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_NoCycle_SVR 86 0.1514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_MovingMedian(7)_NoCycle_XGB 86 0.1534 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_AR 42 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_NoAR 4 0.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_SVR 42 0.2478 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_XGB 42 0.2212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_AR 46 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_NoAR 8 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_SVR 46 0.1716 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_XGB 46 0.1794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_AR 38 0.1949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_NoAR 0 0.5344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_SVR 38 0.1691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_XGB 38 0.1804 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_AR 74 0.2113 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_NoAR 36 0.2146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_SVR 74 0.204 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_XGB 74 0.224 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_AR 78 0.197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_NoAR 40 0.2579 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_SVR 78 0.2046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_XGB 78 0.2542 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_AR 70 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_NoAR 32 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_SVR 70 0.2029 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_XGB 70 0.227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_AR 58 0.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_NoAR 20 0.1765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_SVR 58 0.219 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_XGB 58 0.209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_AR 62 0.1602 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_NoAR 24 0.3179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_SVR 62 0.1934 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_XGB 62 0.1791 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_AR 54 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_NoAR 16 0.3191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_SVR 54 0.1933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_XGB 54 0.1853 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_AR 58 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_NoAR 20 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_SVR 58 0.195 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_XGB 58 0.1961 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_AR 62 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_NoAR 24 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_SVR 62 0.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_XGB 62 0.1706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_AR 54 0.1657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_NoAR 16 0.4087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_SVR 54 0.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_XGB 54 0.1706 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 2.0497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_SVR 74 0.9205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_XGB 74 1.8164 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_AR 78 0.2498 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_NoAR 40 3.6763 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_SVR 78 0.5571 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_XGB 78 0.4505 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_AR 70 0.362 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_NoAR 32 0.4615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_SVR 70 1.5899 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_XGB 70 1.0649 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.4322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 2.0668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_SVR 106 0.6815 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_XGB 106 0.5466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_AR 110 0.1784 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_NoAR 72 6.3103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_SVR 110 1.9111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_XGB 110 0.7871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_AR 102 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_SVR 102 2.0104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_XGB 102 0.4181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_AR 90 0.7301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 2.0072 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_SVR 90 1.1979 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_XGB 90 0.57 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_AR 94 0.662 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_NoAR 56 3.8527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_SVR 94 0.3316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_XGB 94 0.7339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_AR 86 0.3766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_NoAR 48 0.3302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_SVR 86 1.4464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_XGB 86 0.7178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_AR 90 0.5794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 3.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_SVR 90 0.325 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_XGB 90 1.5201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_AR 94 0.4874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_NoAR 56 4.4995 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_SVR 94 1.0936 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_XGB 94 0.9686 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_AR 86 0.4357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_NoAR 48 0.7285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_SVR 86 2.1981 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_XGB 86 1.4795 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_Seasonal_MonthOfYear_AR 122 0.1862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 84 4.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_Seasonal_MonthOfYear_SVR 122 0.3773 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_Seasonal_MonthOfYear_XGB 122 2.0887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_Cycle_None_AR 126 0.279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_Cycle_None_NoAR 88 0.3521 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_Cycle_None_SVR 126 1.7188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_Cycle_None_XGB 126 1.2838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_NoCycle_AR 118 0.279 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_NoCycle_NoAR 80 0.3521 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_NoCycle_SVR 118 1.7188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(12)_NoCycle_XGB 118 1.2838 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_Seasonal_MonthOfYear_AR 122 0.2989 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 84 5.0557 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_Seasonal_MonthOfYear_SVR 122 2.5459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_Seasonal_MonthOfYear_XGB 122 1.6999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_Cycle_AR 126 0.1762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_Cycle_NoAR 88 8.0208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_Cycle_SVR 126 7.3947 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_Cycle_XGB 126 1.843 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_NoCycle_AR 118 0.1887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_NoCycle_NoAR 80 1.4009 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_NoCycle_SVR 118 6.2183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(5)_NoCycle_XGB 118 0.9255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_Seasonal_MonthOfYear_AR 122 0.2756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 84 4.2921 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_Seasonal_MonthOfYear_SVR 122 3.1906 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_Seasonal_MonthOfYear_XGB 122 1.3176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_Cycle_AR 126 0.2147 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_Cycle_NoAR 88 3.3425 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_Cycle_SVR 126 4.3316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_Cycle_XGB 126 1.939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_NoCycle_AR 118 0.2393 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_NoCycle_NoAR 80 0.8858 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_NoCycle_SVR 118 4.9151 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingAverage(7)_NoCycle_XGB 118 1.4325 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_Seasonal_MonthOfYear_AR 122 0.4519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 84 3.479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_Seasonal_MonthOfYear_SVR 122 2.7293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_Seasonal_MonthOfYear_XGB 122 1.4644 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_Cycle_None_AR 126 0.3488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_Cycle_None_NoAR 88 8.0741 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_Cycle_None_SVR 126 2.4587 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_Cycle_None_XGB 126 2.7527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_NoCycle_AR 118 0.3488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_NoCycle_NoAR 80 8.0741 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_NoCycle_SVR 118 2.4587 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(12)_NoCycle_XGB 118 2.7527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_Seasonal_MonthOfYear_AR 122 0.5428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 84 0.4186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_Seasonal_MonthOfYear_SVR 122 0.7423 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_Seasonal_MonthOfYear_XGB 122 1.3167 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_Cycle_None_AR 126 0.2544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_Cycle_None_NoAR 88 5.3781 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_Cycle_None_SVR 126 4.9232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_Cycle_None_XGB 126 1.0304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_NoCycle_AR 118 0.2544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_NoCycle_NoAR 80 5.3781 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_NoCycle_SVR 118 4.9232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(5)_NoCycle_XGB 118 1.0304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_Seasonal_MonthOfYear_AR 122 0.5889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 84 0.8698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_Seasonal_MonthOfYear_SVR 122 3.0382 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_Seasonal_MonthOfYear_XGB 122 1.4216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_Cycle_AR 126 0.3149 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_Cycle_NoAR 88 4.1802 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_Cycle_SVR 126 1.1836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_Cycle_XGB 126 1.1051 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_NoCycle_AR 118 0.3149 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_NoCycle_NoAR 80 4.1802 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_NoCycle_SVR 118 1.1836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_MovingMedian(7)_NoCycle_XGB 118 1.0148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_Seasonal_MonthOfYear_AR 122 1780.3819 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 84 65.1127 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_Seasonal_MonthOfYear_SVR 122 925.638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_Seasonal_MonthOfYear_XGB 122 18760802.0493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_Cycle_AR 126 10933.4926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_Cycle_NoAR 88 6789.3618 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_Cycle_SVR 126 158.8378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_Cycle_XGB 126 45302163.3527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_NoCycle_AR 118 5389.9274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_NoCycle_NoAR 80 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_NoCycle_SVR 118 243.5125 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(12)_NoCycle_XGB 118 49255320.0992 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_Seasonal_MonthOfYear_AR 122 1148.2454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 84 1057.446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_Seasonal_MonthOfYear_SVR 122 0.5118 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_Seasonal_MonthOfYear_XGB 122 28762824.2581 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_Cycle_None_AR 126 857.8697 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_Cycle_None_NoAR 88 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_Cycle_None_SVR 126 1.0637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_Cycle_None_XGB 126 46839838.1225 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_NoCycle_AR 118 857.8697 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_NoCycle_NoAR 80 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_NoCycle_SVR 118 1.0637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(5)_NoCycle_XGB 118 46839838.1225 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_Seasonal_MonthOfYear_AR 122 985.3355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 84 0.7984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_Seasonal_MonthOfYear_SVR 122 142.8879 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_Seasonal_MonthOfYear_XGB 122 33695098.6536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_Cycle_None_AR 126 46705.7495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_Cycle_None_NoAR 88 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_Cycle_None_SVR 126 105.8258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_Cycle_None_XGB 126 47101143.3495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_NoCycle_AR 118 46705.7495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_NoCycle_NoAR 80 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_NoCycle_SVR 118 105.8258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingAverage(7)_NoCycle_XGB 118 47101143.3495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_Seasonal_MonthOfYear_AR 122 24630.5347 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 84 0.343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_Seasonal_MonthOfYear_SVR 122 30.6727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_Seasonal_MonthOfYear_XGB 122 21579862.0992 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_Cycle_AR 126 7972.3986 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_Cycle_NoAR 88 15.813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_Cycle_SVR 126 80.8812 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_Cycle_XGB 126 39801960.4683 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_NoCycle_AR 118 15476.2541 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_NoCycle_NoAR 80 1618.1842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_NoCycle_SVR 118 54.7786 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(12)_NoCycle_XGB 118 44335491.1062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_Seasonal_MonthOfYear_AR 122 12.9676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 84 0.4541 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_Seasonal_MonthOfYear_SVR 122 17.2403 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_Seasonal_MonthOfYear_XGB 122 20487427.0351 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_Cycle_AR 126 6.0225 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_Cycle_NoAR 88 0.5681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_Cycle_SVR 126 0.3823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_Cycle_XGB 126 45627352.047 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_NoCycle_AR 118 915.3874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_NoCycle_NoAR 80 231.2083 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_NoCycle_SVR 118 1.2064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(5)_NoCycle_XGB 118 45879145.9343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_Seasonal_MonthOfYear_AR 122 588.5199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 84 0.5599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_Seasonal_MonthOfYear_SVR 122 175.5903 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_Seasonal_MonthOfYear_XGB 122 13651138.3214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_Cycle_None_AR 126 85624.0388 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_Cycle_None_NoAR 88 129.0178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_Cycle_None_SVR 126 22.7814 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_Cycle_None_XGB 126 44312808.0141 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_NoCycle_AR 118 85624.0388 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_NoCycle_NoAR 80 129.0178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_NoCycle_SVR 118 22.7814 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_MovingMedian(7)_NoCycle_XGB 118 44312808.0141 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_AR 74 16702.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.4994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_SVR 74 68.7345 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_XGB 74 1352485.1774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_AR 78 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_NoAR 40 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_SVR 78 149.1849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_XGB 78 36357258.108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_AR 70 13195.3873 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_NoAR 32 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_SVR 70 149.1849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_XGB 70 36357258.108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_AR 106 58064.6971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_SVR 106 0.5516 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_XGB 106 4311073.9417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_AR 110 489.3382 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_NoAR 72 0.5674 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_SVR 110 0.5001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_XGB 110 19528269.5745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_AR 102 1338.131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_SVR 102 0.5273 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_XGB 102 29407402.092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_AR 90 168397.0695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.8574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_SVR 90 187.3636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_XGB 90 14778594.808 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_AR 94 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_NoAR 56 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_SVR 94 641.9201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_XGB 94 40388161.7717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_AR 86 113192.3779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_SVR 86 641.9201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_XGB 86 40388161.7717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_AR 90 16375730.725 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_NoAR 52 381.2939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_SVR 90 13473.2622 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_XGB 90 33015903.3314 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_AR 94 24582296.0807 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_NoAR 56 1030944.8615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_SVR 94 38632.0282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_XGB 94 44003001.6135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_AR 86 24525015.6551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_NoAR 48 53955112.5074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_SVR 86 23782.5319 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_XGB 86 49387732.6506 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.3897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_SVR 74 1.6587 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_XGB 74 1.5228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_AR 78 0.3094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_NoAR 40 0.9887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_SVR 78 1.2572 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_XGB 78 0.9887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_AR 70 0.338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_NoAR 32 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_SVR 70 1.2685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_XGB 70 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_SVR 106 0.2604 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_XGB 106 0.2811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_AR 110 0.2801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_NoAR 72 0.2515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_SVR 110 0.2604 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_XGB 110 0.2811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_AR 102 0.305 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_SVR 102 0.2076 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_XGB 102 0.2135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_AR 90 0.3245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_SVR 90 0.2485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_XGB 90 0.3537 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_AR 94 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_NoAR 56 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_SVR 94 0.3927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_XGB 94 0.3811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_AR 86 0.4065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_NoAR 48 0.4272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_SVR 86 0.3927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_XGB 86 0.3811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_AR 90 0.3024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.6702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_SVR 90 0.6068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_XGB 90 0.9191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_AR 94 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_NoAR 56 0.7655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_SVR 94 0.694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_XGB 94 0.983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_AR 86 0.4148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_NoAR 48 0.7655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_SVR 86 0.694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_XGB 86 0.983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_Seasonal_MonthOfYear_AR 122 0.337 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 84 0.2345 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_Seasonal_MonthOfYear_SVR 122 0.2386 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_Seasonal_MonthOfYear_XGB 122 0.2276 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_Cycle_AR 126 0.3287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_Cycle_NoAR 88 0.3358 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_Cycle_SVR 126 0.2745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_Cycle_XGB 126 0.2412 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_NoCycle_AR 118 0.332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_NoCycle_NoAR 80 0.3364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_NoCycle_SVR 118 0.2773 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(12)_NoCycle_XGB 118 0.2932 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_Seasonal_MonthOfYear_AR 122 0.3185 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 84 0.2098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_Seasonal_MonthOfYear_SVR 122 0.2117 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_Seasonal_MonthOfYear_XGB 122 0.3518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_Cycle_AR 126 0.3185 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_Cycle_NoAR 88 0.2098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_Cycle_SVR 126 0.2117 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_Cycle_XGB 126 0.3518 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_NoCycle_AR 118 0.2952 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_NoCycle_NoAR 80 0.4655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_NoCycle_SVR 118 0.1785 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(5)_NoCycle_XGB 118 0.3363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_Seasonal_MonthOfYear_AR 122 0.3306 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 84 0.2009 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_Seasonal_MonthOfYear_SVR 122 0.204 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_Seasonal_MonthOfYear_XGB 122 0.5312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_Cycle_AR 126 0.3306 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_Cycle_NoAR 88 0.2009 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_Cycle_SVR 126 0.204 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_Cycle_XGB 126 0.5312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_NoCycle_AR 118 0.2781 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_NoCycle_NoAR 80 0.4724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_NoCycle_SVR 118 0.2046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingAverage(7)_NoCycle_XGB 118 0.4076 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_Seasonal_MonthOfYear_AR 122 0.3317 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 84 0.2851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_Seasonal_MonthOfYear_SVR 122 0.2848 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_Seasonal_MonthOfYear_XGB 122 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_Cycle_AR 126 0.3317 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_Cycle_NoAR 88 0.2851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_Cycle_SVR 126 0.2848 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_Cycle_XGB 126 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_NoCycle_AR 118 0.325 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_NoCycle_NoAR 80 0.6029 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_NoCycle_SVR 118 0.3058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(12)_NoCycle_XGB 118 0.4154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_Seasonal_MonthOfYear_AR 122 0.3267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 84 0.3012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_Seasonal_MonthOfYear_SVR 122 0.3027 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_Seasonal_MonthOfYear_XGB 122 0.3568 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_Cycle_AR 126 0.3267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_Cycle_NoAR 88 0.3012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_Cycle_SVR 126 0.3027 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_Cycle_XGB 126 0.3568 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_NoCycle_AR 118 0.4492 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_NoCycle_NoAR 80 0.5606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_NoCycle_SVR 118 0.2405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(5)_NoCycle_XGB 118 0.5519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_Seasonal_MonthOfYear_AR 122 0.2961 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 84 0.2504 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_Seasonal_MonthOfYear_SVR 122 0.2446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_Seasonal_MonthOfYear_XGB 122 0.3983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_Cycle_AR 126 0.2961 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_Cycle_NoAR 88 0.2504 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_Cycle_SVR 126 0.2446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_Cycle_XGB 126 0.3983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_NoCycle_AR 118 0.3295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_NoCycle_NoAR 80 0.6216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_NoCycle_SVR 118 0.2667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_MovingMedian(7)_NoCycle_XGB 118 0.4382 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_Seasonal_MonthOfYear_AR 106 577899864.0212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_Seasonal_MonthOfYear_NoAR 68 0.2644 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_Seasonal_MonthOfYear_SVR 106 0.2056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_Seasonal_MonthOfYear_XGB 106 87368524.4093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_Cycle_AR 110 577899864.0212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_Cycle_NoAR 72 0.2644 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_Cycle_SVR 110 0.2056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_Cycle_XGB 110 87368524.4093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_NoCycle_AR 102 680024886.2381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_NoCycle_NoAR 64 0.3246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_NoCycle_SVR 102 0.3956 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_ConstantTrend_NoCycle_XGB 102 73014239.0238 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_Seasonal_MonthOfYear_AR 138 562574086.8594 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_Seasonal_MonthOfYear_NoAR 100 0.2263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_Seasonal_MonthOfYear_SVR 138 0.2307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_Seasonal_MonthOfYear_XGB 138 412794093.8597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_Cycle_AR 142 544177199.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_Cycle_NoAR 104 0.2863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_Cycle_SVR 142 0.3076 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_Cycle_XGB 142 403985471.8463 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_NoCycle_AR 134 544177199.0292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_NoCycle_NoAR 96 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_NoCycle_SVR 134 0.3108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_Lag1Trend_NoCycle_XGB 134 323381039.1801 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_Seasonal_MonthOfYear_AR 122 552237248.7441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_Seasonal_MonthOfYear_NoAR 84 0.3326 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_Seasonal_MonthOfYear_SVR 122 0.2879 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_Seasonal_MonthOfYear_XGB 122 0.3893 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_Cycle_AR 126 515533864.412 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_Cycle_NoAR 88 0.3473 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_Cycle_SVR 126 0.3215 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_Cycle_XGB 126 23923333.4515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_NoCycle_AR 118 583548543.0209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_NoCycle_NoAR 80 0.3627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_NoCycle_SVR 118 0.3287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_LinearTrend_NoCycle_XGB 118 16963818.3353 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_Seasonal_MonthOfYear_AR 122 485014168.676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_Seasonal_MonthOfYear_NoAR 84 0.3536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_Seasonal_MonthOfYear_SVR 122 0.3201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_Seasonal_MonthOfYear_XGB 122 0.3627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_Cycle_AR 126 545379952.5167 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_Cycle_NoAR 88 0.3623 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_Cycle_SVR 126 0.3445 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_Cycle_XGB 126 0.3676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_NoCycle_AR 118 545379952.5258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_NoCycle_NoAR 80 0.372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_NoCycle_SVR 118 0.3446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_PolyTrend_NoCycle_XGB 118 0.3333 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_Seasonal_MonthOfYear_AR 154 469831627.1716 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 116 0.2841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_Seasonal_MonthOfYear_SVR 154 139638163.2666 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_Seasonal_MonthOfYear_XGB 154 0.4188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_Cycle_AR 158 469831627.1716 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_Cycle_NoAR 120 0.2841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_Cycle_SVR 158 139638163.2666 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_Cycle_XGB 158 0.4188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_NoCycle_AR 150 624479335.578 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_NoCycle_NoAR 112 0.36 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_NoCycle_SVR 150 230443791.9787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(12)_NoCycle_XGB 150 35885000.0278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_Seasonal_MonthOfYear_AR 154 445827444.6176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 116 0.2776 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_Seasonal_MonthOfYear_SVR 154 45698448.8754 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_Seasonal_MonthOfYear_XGB 154 190570500.6842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_Cycle_None_AR 158 502636343.6171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_Cycle_None_NoAR 120 0.4203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_Cycle_None_SVR 158 359763522.1841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_Cycle_None_XGB 158 231527042.8432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_NoCycle_AR 150 502636343.6171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_NoCycle_NoAR 112 0.4203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_NoCycle_SVR 150 359763522.1841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(5)_NoCycle_XGB 150 231527042.8432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_Seasonal_MonthOfYear_AR 154 522016384.8644 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 116 0.3115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_Seasonal_MonthOfYear_SVR 154 196033823.5966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_Seasonal_MonthOfYear_XGB 154 141646541.5671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_Cycle_AR 158 522016384.8644 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_Cycle_NoAR 120 0.3115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_Cycle_SVR 158 196033823.5966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_Cycle_XGB 158 141646541.5671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_NoCycle_AR 150 551112955.2004 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_NoCycle_NoAR 112 0.4592 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_NoCycle_SVR 150 399725633.1506 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingAverage(7)_NoCycle_XGB 150 144594336.0772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_Seasonal_MonthOfYear_AR 154 527264024.0917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 116 0.1816 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_Seasonal_MonthOfYear_SVR 154 0.1797 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_Seasonal_MonthOfYear_XGB 154 76892191.651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_Cycle_AR 158 609624885.4419 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_Cycle_NoAR 120 0.3306 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_Cycle_SVR 158 0.3132 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_Cycle_XGB 158 90263382.5892 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_NoCycle_AR 150 621882243.7013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_NoCycle_NoAR 112 0.3349 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_NoCycle_SVR 150 0.3161 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(12)_NoCycle_XGB 150 115157753.5339 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_Seasonal_MonthOfYear_AR 154 657479252.5498 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 116 0.233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_Seasonal_MonthOfYear_SVR 154 0.2231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_Seasonal_MonthOfYear_XGB 154 138908688.5369 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_Cycle_None_AR 158 634110937.2167 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_Cycle_None_NoAR 120 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_Cycle_None_SVR 158 0.6128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_Cycle_None_XGB 158 158920727.9432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_NoCycle_AR 150 634110937.2167 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_NoCycle_NoAR 112 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_NoCycle_SVR 150 0.6128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(5)_NoCycle_XGB 150 158920727.9432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_Seasonal_MonthOfYear_AR 154 582021101.3136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 116 0.2255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_Seasonal_MonthOfYear_SVR 154 0.2156 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_Seasonal_MonthOfYear_XGB 154 115422377.8836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_Cycle_AR 158 582021101.3136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_Cycle_NoAR 120 0.2255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_Cycle_SVR 158 0.2156 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_Cycle_XGB 158 115422377.8836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_NoCycle_AR 150 511082674.515 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_NoCycle_NoAR 112 0.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_NoCycle_SVR 150 0.4975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-2.0_Ozone BoxCox(Lambda=-2.0)_MovingMedian(7)_NoCycle_XGB 150 70987419.6987 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_Seasonal_MonthOfYear_AR 106 194591710.0619 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_Seasonal_MonthOfYear_NoAR 68 0.2657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_Seasonal_MonthOfYear_SVR 106 0.1963 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_Seasonal_MonthOfYear_XGB 106 0.2377 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_Cycle_None_AR 110 193296828.064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_Cycle_None_NoAR 72 0.3241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_Cycle_None_SVR 110 0.2915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_Cycle_None_XGB 110 0.2392 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_NoCycle_AR 102 193296828.064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_NoCycle_NoAR 64 0.3241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_NoCycle_SVR 102 0.2915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_ConstantTrend_NoCycle_XGB 102 0.2392 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_Seasonal_MonthOfYear_AR 138 284358457.7701 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_Seasonal_MonthOfYear_NoAR 100 0.2193 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_Seasonal_MonthOfYear_SVR 138 0.2307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_Seasonal_MonthOfYear_XGB 138 0.3428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_Cycle_AR 142 411961718.8007 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_Cycle_NoAR 104 0.2862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_Cycle_SVR 142 0.293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_Cycle_XGB 142 0.4 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_NoCycle_AR 134 411961718.8062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_NoCycle_NoAR 96 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_NoCycle_SVR 134 0.2955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_Lag1Trend_NoCycle_XGB 134 0.5559 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_Seasonal_MonthOfYear_AR 122 226936282.6191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_Seasonal_MonthOfYear_NoAR 84 0.2516 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_Seasonal_MonthOfYear_SVR 122 0.1906 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_Seasonal_MonthOfYear_XGB 122 0.2875 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_Cycle_None_AR 126 123675068.1378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_Cycle_None_NoAR 88 0.327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_Cycle_None_SVR 126 0.2744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_Cycle_None_XGB 126 0.2575 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_NoCycle_AR 118 123675068.1378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_NoCycle_NoAR 80 0.327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_NoCycle_SVR 118 0.2744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_LinearTrend_NoCycle_XGB 118 0.2575 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_Seasonal_MonthOfYear_AR 122 226936282.6012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_Seasonal_MonthOfYear_NoAR 84 0.2569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_Seasonal_MonthOfYear_SVR 122 0.193 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_Seasonal_MonthOfYear_XGB 122 0.2759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_Cycle_None_AR 126 123675048.3145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_Cycle_None_NoAR 88 0.3282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_Cycle_None_SVR 126 0.276 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_Cycle_None_XGB 126 0.2427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_NoCycle_AR 118 123675048.3145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_NoCycle_NoAR 80 0.3282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_NoCycle_SVR 118 0.276 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_PolyTrend_NoCycle_XGB 118 0.2427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_Seasonal_MonthOfYear_AR 154 184147915.9908 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 116 0.2135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_Seasonal_MonthOfYear_SVR 154 0.2776 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_Seasonal_MonthOfYear_XGB 154 0.2836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_Cycle_AR 158 199482107.965 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_Cycle_NoAR 120 0.3255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_Cycle_SVR 158 0.3247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_Cycle_XGB 158 0.4076 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_NoCycle_AR 150 152831608.5361 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_NoCycle_NoAR 112 0.3299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_NoCycle_SVR 150 0.3165 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(12)_NoCycle_XGB 150 0.2524 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_Seasonal_MonthOfYear_AR 154 101317465.9198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 116 0.2485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_Seasonal_MonthOfYear_SVR 154 0.3724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_Seasonal_MonthOfYear_XGB 154 26657428.6246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_Cycle_AR 158 101317465.9198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_Cycle_NoAR 120 0.2485 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_Cycle_SVR 158 0.3724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_Cycle_XGB 158 26657428.6246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_NoCycle_AR 150 168875431.5527 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_NoCycle_NoAR 112 0.4341 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_NoCycle_SVR 150 0.5879 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(5)_NoCycle_XGB 150 0.3293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_Seasonal_MonthOfYear_AR 154 120358486.5229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 116 0.27 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_Seasonal_MonthOfYear_SVR 154 0.4086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_Seasonal_MonthOfYear_XGB 154 0.2692 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_Cycle_AR 158 120358486.5229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_Cycle_NoAR 120 0.27 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_Cycle_SVR 158 0.4086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_Cycle_XGB 158 0.2692 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_NoCycle_AR 150 268618530.2652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_NoCycle_NoAR 112 0.4574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_NoCycle_SVR 150 0.707 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingAverage(7)_NoCycle_XGB 150 0.2694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_Seasonal_MonthOfYear_AR 154 99749381.8689 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 116 0.163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_Seasonal_MonthOfYear_SVR 154 0.1742 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_Seasonal_MonthOfYear_XGB 154 0.1813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_Cycle_None_AR 158 210589370.0461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_Cycle_None_NoAR 120 0.3355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_Cycle_None_SVR 158 0.2617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_Cycle_None_XGB 158 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_NoCycle_AR 150 210589370.0461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_NoCycle_NoAR 112 0.3355 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_NoCycle_SVR 150 0.2617 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(12)_NoCycle_XGB 150 0.1959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_Seasonal_MonthOfYear_AR 154 283585613.052 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 116 0.2061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_Seasonal_MonthOfYear_SVR 154 0.222 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_Seasonal_MonthOfYear_XGB 154 0.2228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_Cycle_None_AR 158 234094674.6772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_Cycle_None_NoAR 120 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_Cycle_None_SVR 158 0.4482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_Cycle_None_XGB 158 0.2653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_NoCycle_AR 150 234094674.6772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_NoCycle_NoAR 112 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_NoCycle_SVR 150 0.4482 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(5)_NoCycle_XGB 150 0.2653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_Seasonal_MonthOfYear_AR 154 177253519.3383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 116 0.1931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_Seasonal_MonthOfYear_SVR 154 0.1916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_Seasonal_MonthOfYear_XGB 154 0.201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_Cycle_AR 158 177253519.3383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_Cycle_NoAR 120 0.1931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_Cycle_SVR 158 0.1916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_Cycle_XGB 158 0.201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_NoCycle_AR 150 283491838.6946 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_NoCycle_NoAR 112 0.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_NoCycle_SVR 150 0.3608 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_-1.0_Ozone BoxCox(Lambda=-1.0)_MovingMedian(7)_NoCycle_XGB 150 0.1931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_Seasonal_MonthOfYear_AR 106 0.2055 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_Seasonal_MonthOfYear_NoAR 68 0.2671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_Seasonal_MonthOfYear_SVR 106 0.2267 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_Seasonal_MonthOfYear_XGB 106 0.1822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_Cycle_None_AR 110 0.2497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_Cycle_None_NoAR 72 0.4131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_Cycle_None_SVR 110 0.183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_Cycle_None_XGB 110 0.1583 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_NoCycle_AR 102 0.2497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_NoCycle_NoAR 64 0.4131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_NoCycle_SVR 102 0.183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_ConstantTrend_NoCycle_XGB 102 0.1583 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_Seasonal_MonthOfYear_AR 138 0.2693 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_Seasonal_MonthOfYear_NoAR 100 0.2145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_Seasonal_MonthOfYear_SVR 138 0.2137 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_Seasonal_MonthOfYear_XGB 138 0.203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_Cycle_AR 142 0.3222 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_Cycle_NoAR 104 0.285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_Cycle_SVR 142 0.2319 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_Cycle_XGB 142 0.2444 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_NoCycle_AR 134 0.3381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_NoCycle_NoAR 96 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_NoCycle_SVR 134 0.2269 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_Lag1Trend_NoCycle_XGB 134 0.2549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_Seasonal_MonthOfYear_AR 122 0.2341 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_Seasonal_MonthOfYear_NoAR 84 0.1631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_Seasonal_MonthOfYear_SVR 122 0.1719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_Seasonal_MonthOfYear_XGB 122 0.1741 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_Cycle_None_AR 126 0.1868 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_Cycle_None_NoAR 88 0.3126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_Cycle_None_SVR 126 0.1508 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_Cycle_None_XGB 126 0.1592 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_NoCycle_AR 118 0.1868 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_NoCycle_NoAR 80 0.3126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_NoCycle_SVR 118 0.1508 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_LinearTrend_NoCycle_XGB 118 0.1592 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_Seasonal_MonthOfYear_AR 122 0.2587 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_Seasonal_MonthOfYear_NoAR 84 0.1655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_Seasonal_MonthOfYear_SVR 122 0.1739 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_Seasonal_MonthOfYear_XGB 122 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_Cycle_None_AR 126 0.1964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_Cycle_None_NoAR 88 0.3213 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_Cycle_None_SVR 126 0.1629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_Cycle_None_XGB 126 0.1504 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_NoCycle_AR 118 0.1964 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_NoCycle_NoAR 80 0.3213 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_NoCycle_SVR 118 0.1629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_PolyTrend_NoCycle_XGB 118 0.1504 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_Seasonal_MonthOfYear_AR 154 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 116 0.1655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_Seasonal_MonthOfYear_SVR 154 0.1788 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_Seasonal_MonthOfYear_XGB 154 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_Cycle_AR 158 0.2318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_Cycle_NoAR 120 0.3087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_Cycle_SVR 158 0.1572 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_Cycle_XGB 158 0.1791 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_NoCycle_AR 150 0.2255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_NoCycle_NoAR 112 0.3079 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_NoCycle_SVR 150 0.1556 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(12)_NoCycle_XGB 150 0.1733 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_Seasonal_MonthOfYear_AR 154 0.2014 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 116 0.1933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_Seasonal_MonthOfYear_SVR 154 0.1784 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_Seasonal_MonthOfYear_XGB 154 0.1858 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_Cycle_AR 158 0.2014 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_Cycle_NoAR 120 0.1933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_Cycle_SVR 158 0.1784 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_Cycle_XGB 158 0.1858 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_NoCycle_AR 150 0.2176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_NoCycle_NoAR 112 0.4441 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_NoCycle_SVR 150 0.1775 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(5)_NoCycle_XGB 150 0.1685 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_Seasonal_MonthOfYear_AR 154 0.1951 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 116 0.1842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_Seasonal_MonthOfYear_SVR 154 0.1746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_Seasonal_MonthOfYear_XGB 154 0.1842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_Cycle_AR 158 0.1951 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_Cycle_NoAR 120 0.1842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_Cycle_SVR 158 0.1746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_Cycle_XGB 158 0.1842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_NoCycle_AR 150 0.2202 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_NoCycle_NoAR 112 0.453 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_NoCycle_SVR 150 0.1713 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingAverage(7)_NoCycle_XGB 150 0.1622 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_Seasonal_MonthOfYear_AR 154 0.2739 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 116 0.1634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_Seasonal_MonthOfYear_SVR 154 0.1702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_Seasonal_MonthOfYear_XGB 154 0.1772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_Cycle_None_AR 158 0.2342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_Cycle_None_NoAR 120 0.3372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_Cycle_None_SVR 158 0.1511 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_Cycle_None_XGB 158 0.166 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_NoCycle_AR 150 0.2342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_NoCycle_NoAR 112 0.3372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_NoCycle_SVR 150 0.1511 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(12)_NoCycle_XGB 150 0.166 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_Seasonal_MonthOfYear_AR 154 0.2173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 116 0.1899 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_Seasonal_MonthOfYear_SVR 154 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_Seasonal_MonthOfYear_XGB 154 0.1757 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_Cycle_None_AR 158 0.2524 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_Cycle_None_NoAR 120 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_Cycle_None_SVR 158 0.2034 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_Cycle_None_XGB 158 0.1896 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_NoCycle_AR 150 0.2524 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_NoCycle_NoAR 112 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_NoCycle_SVR 150 0.2034 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(5)_NoCycle_XGB 150 0.1896 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_Seasonal_MonthOfYear_AR 154 0.2056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 116 0.1747 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_Seasonal_MonthOfYear_SVR 154 0.1922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_Seasonal_MonthOfYear_XGB 154 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_Cycle_AR 158 0.2056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_Cycle_NoAR 120 0.1747 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_Cycle_SVR 158 0.1922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_Cycle_XGB 158 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_NoCycle_AR 150 0.2289 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_NoCycle_NoAR 112 0.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_NoCycle_SVR 150 0.1648 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_0.0_Ozone BoxCox(Lambda=0.0)_MovingMedian(7)_NoCycle_XGB 150 0.1684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_Seasonal_MonthOfYear_AR 106 0.1977 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_Seasonal_MonthOfYear_NoAR 68 0.2698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_Seasonal_MonthOfYear_SVR 106 0.3871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_Seasonal_MonthOfYear_XGB 106 0.3127 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_Cycle_AR 110 0.3642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_Cycle_NoAR 72 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_Cycle_SVR 110 0.6111 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_Cycle_XGB 110 0.3417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_NoCycle_AR 102 0.3734 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_NoCycle_NoAR 64 0.6403 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_NoCycle_SVR 102 0.6083 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_ConstantTrend_NoCycle_XGB 102 0.3427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_Seasonal_MonthOfYear_AR 138 0.2402 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_Seasonal_MonthOfYear_NoAR 100 0.2486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_Seasonal_MonthOfYear_SVR 138 0.4092 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_Seasonal_MonthOfYear_XGB 138 0.3771 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_Cycle_AR 142 0.2309 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_Cycle_NoAR 104 0.25 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_Cycle_SVR 142 0.3888 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_Cycle_XGB 142 0.3141 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_NoCycle_AR 134 0.2364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_NoCycle_NoAR 96 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_NoCycle_SVR 134 0.3919 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_Lag1Trend_NoCycle_XGB 134 0.3559 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_Seasonal_MonthOfYear_AR 122 0.2915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_Seasonal_MonthOfYear_NoAR 84 0.3051 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_Seasonal_MonthOfYear_SVR 122 0.2253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_Seasonal_MonthOfYear_XGB 122 0.2774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_Cycle_None_AR 126 0.2417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_Cycle_None_NoAR 88 0.3183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_Cycle_None_SVR 126 0.356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_Cycle_None_XGB 126 0.2476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_NoCycle_AR 118 0.2417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_NoCycle_NoAR 80 0.3183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_NoCycle_SVR 118 0.356 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_LinearTrend_NoCycle_XGB 118 0.2476 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_Seasonal_MonthOfYear_AR 122 0.2462 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_Seasonal_MonthOfYear_NoAR 84 0.2627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_Seasonal_MonthOfYear_SVR 122 0.529 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_Seasonal_MonthOfYear_XGB 122 0.2576 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_Cycle_AR 126 0.2665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_Cycle_NoAR 88 0.4104 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_Cycle_SVR 126 0.6661 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_Cycle_XGB 126 0.3463 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_NoCycle_AR 118 0.2634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_NoCycle_NoAR 80 0.5088 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_NoCycle_SVR 118 0.6648 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_PolyTrend_NoCycle_XGB 118 0.3266 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_Seasonal_MonthOfYear_AR 154 0.1854 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 116 0.2205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_Seasonal_MonthOfYear_SVR 154 0.3862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_Seasonal_MonthOfYear_XGB 154 0.2157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_Cycle_None_AR 158 0.1802 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_Cycle_None_NoAR 120 0.3887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_Cycle_None_SVR 158 0.5044 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_Cycle_None_XGB 158 0.3272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_NoCycle_AR 150 0.1802 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_NoCycle_NoAR 112 0.3887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_NoCycle_SVR 150 0.5044 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(12)_NoCycle_XGB 150 0.3272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_Seasonal_MonthOfYear_AR 154 0.2196 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 116 0.2422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_Seasonal_MonthOfYear_SVR 154 0.4738 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_Seasonal_MonthOfYear_XGB 154 0.2998 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_Cycle_AR 158 0.2017 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_Cycle_NoAR 120 0.4949 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_Cycle_SVR 158 0.3067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_Cycle_XGB 158 0.2209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_NoCycle_AR 150 0.1948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_NoCycle_NoAR 112 0.492 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_NoCycle_SVR 150 0.3007 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(5)_NoCycle_XGB 150 0.247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_Seasonal_MonthOfYear_AR 154 0.212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 116 0.2487 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_Seasonal_MonthOfYear_SVR 154 0.3834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_Seasonal_MonthOfYear_XGB 154 0.2491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_Cycle_AR 158 0.1973 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_Cycle_NoAR 120 0.4885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_Cycle_SVR 158 0.2636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_Cycle_XGB 158 0.2757 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_NoCycle_AR 150 0.1821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_NoCycle_NoAR 112 0.4974 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_NoCycle_SVR 150 0.2502 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingAverage(7)_NoCycle_XGB 150 0.2136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_Seasonal_MonthOfYear_AR 154 0.2026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 116 0.1969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_Seasonal_MonthOfYear_SVR 154 0.4601 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_Seasonal_MonthOfYear_XGB 154 0.2526 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_Cycle_None_AR 158 0.1871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_Cycle_None_NoAR 120 0.3408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_Cycle_None_SVR 158 0.5006 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_Cycle_None_XGB 158 0.2477 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_NoCycle_AR 150 0.1871 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_NoCycle_NoAR 112 0.3408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_NoCycle_SVR 150 0.5006 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(12)_NoCycle_XGB 150 0.2477 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_Seasonal_MonthOfYear_AR 154 0.2281 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 116 0.2207 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_Seasonal_MonthOfYear_SVR 154 0.2936 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_Seasonal_MonthOfYear_XGB 154 0.2415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_Cycle_None_AR 158 0.2497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_Cycle_None_NoAR 120 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_Cycle_None_SVR 158 0.4074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_Cycle_None_XGB 158 0.2359 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_NoCycle_AR 150 0.2497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_NoCycle_NoAR 112 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_NoCycle_SVR 150 0.4074 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(5)_NoCycle_XGB 150 0.2359 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_Seasonal_MonthOfYear_AR 154 0.2247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 116 0.2175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_Seasonal_MonthOfYear_SVR 154 0.3637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_Seasonal_MonthOfYear_XGB 154 0.2561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_Cycle_AR 158 0.2203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_Cycle_NoAR 120 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_Cycle_SVR 158 0.3294 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_Cycle_XGB 158 0.2541 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_NoCycle_AR 150 0.2131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_NoCycle_NoAR 112 0.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_NoCycle_SVR 150 0.332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Box_Cox_2.0_Ozone BoxCox(Lambda=2.0)_MovingMedian(7)_NoCycle_XGB 150 0.2603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_AR 154 0.2354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 116 0.2529 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_SVR 154 0.2479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_XGB 154 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_Cycle_None_AR 158 0.2331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_Cycle_None_NoAR 120 0.3042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_Cycle_None_SVR 158 0.2299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_Cycle_None_XGB 158 0.2116 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_NoCycle_AR 150 0.2331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_NoCycle_NoAR 112 0.3042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_NoCycle_SVR 150 0.2299 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(12)_NoCycle_XGB 150 0.2116 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_AR 154 0.2338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 116 0.2639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_SVR 154 0.2399 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_XGB 154 0.26 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_Cycle_AR 158 0.2349 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_Cycle_NoAR 120 0.493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_Cycle_SVR 158 0.2148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_Cycle_XGB 158 0.2245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_NoCycle_AR 150 0.236 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_NoCycle_NoAR 112 0.4793 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_NoCycle_SVR 150 0.2083 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(5)_NoCycle_XGB 150 0.2099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_AR 154 0.2329 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 116 0.2439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_SVR 154 0.2302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_XGB 154 0.2606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_Cycle_AR 158 0.2329 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_Cycle_NoAR 120 0.2439 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_Cycle_SVR 158 0.2302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_Cycle_XGB 158 0.2606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_NoCycle_AR 150 0.2311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_NoCycle_NoAR 112 0.429 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_NoCycle_SVR 150 0.2002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingAverage(7)_NoCycle_XGB 150 0.2304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_AR 154 0.2654 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 116 0.214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_SVR 154 0.2702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_XGB 154 0.2099 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_Cycle_AR 158 0.27 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_Cycle_NoAR 120 0.3621 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_Cycle_SVR 158 0.2333 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_Cycle_XGB 158 0.2629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_NoCycle_AR 150 0.27 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_NoCycle_NoAR 112 0.3621 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_NoCycle_SVR 150 0.2333 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(12)_NoCycle_XGB 150 0.2629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_AR 154 0.2742 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 116 0.1942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_SVR 154 0.2447 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_XGB 154 0.2311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_Cycle_AR 158 0.2748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_Cycle_NoAR 120 0.5216 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_Cycle_SVR 158 0.2787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_Cycle_XGB 158 0.2733 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_NoCycle_AR 150 0.244 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_NoCycle_NoAR 112 0.5094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_NoCycle_SVR 150 0.2116 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(5)_NoCycle_XGB 150 0.2463 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_AR 154 0.2731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 116 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_SVR 154 0.2491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_XGB 154 0.2742 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_Cycle_AR 158 0.2606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_Cycle_NoAR 120 0.4432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_Cycle_SVR 158 0.2639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_Cycle_XGB 158 0.2517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_NoCycle_AR 150 0.2606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_NoCycle_NoAR 112 0.4432 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_NoCycle_SVR 150 0.2639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_MovingMedian(7)_NoCycle_XGB 150 0.2517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_AR 106 0.2924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_NoAR 68 0.2777 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_SVR 106 0.2717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_XGB 106 0.24 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Cycle_None_AR 110 0.2458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Cycle_None_NoAR 72 0.3561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Cycle_None_SVR 110 0.2004 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_Cycle_None_XGB 110 0.1922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_NoCycle_AR 102 0.2458 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_NoCycle_NoAR 64 0.3561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_NoCycle_SVR 102 0.2004 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_ConstantTrend_NoCycle_XGB 102 0.1922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_AR 138 0.2915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_NoAR 100 0.2824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_SVR 138 0.2896 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_XGB 138 0.2924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_Cycle_AR 142 0.2274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_Cycle_NoAR 104 0.3177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_Cycle_SVR 142 0.2534 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_Cycle_XGB 142 0.2742 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_NoCycle_AR 134 0.2274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_NoCycle_NoAR 96 0.3177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_NoCycle_SVR 134 0.2534 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_Lag1Trend_NoCycle_XGB 134 0.2742 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_AR 122 0.2742 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_NoAR 84 0.2652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_SVR 122 0.2711 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_XGB 122 0.2673 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_Cycle_None_AR 126 0.2385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_Cycle_None_NoAR 88 0.3239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_Cycle_None_SVR 126 0.2364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_Cycle_None_XGB 126 0.2532 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_NoCycle_AR 118 0.2385 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_NoCycle_NoAR 80 0.3239 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_NoCycle_SVR 118 0.2364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_LinearTrend_NoCycle_XGB 118 0.2532 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_AR 122 0.2904 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_NoAR 84 0.2252 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_SVR 122 0.2688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_XGB 122 0.2247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_Cycle_None_AR 126 0.2418 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_Cycle_None_NoAR 88 0.3561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_Cycle_None_SVR 126 0.2069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_Cycle_None_XGB 126 0.2501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_NoCycle_AR 118 0.2418 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_NoCycle_NoAR 80 0.3561 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_NoCycle_SVR 118 0.2069 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_5_Ozone Quantization_PolyTrend_NoCycle_XGB 118 0.2501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_AR 154 0.2136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 116 0.1821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_SVR 154 0.1647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_XGB 154 0.1636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_Cycle_None_AR 158 0.1993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_Cycle_None_NoAR 120 0.3291 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_Cycle_None_SVR 158 0.1852 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_Cycle_None_XGB 158 0.1464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_NoCycle_AR 150 0.1993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_NoCycle_NoAR 112 0.3291 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_NoCycle_SVR 150 0.1852 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(12)_NoCycle_XGB 150 0.1464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_AR 154 0.2018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 116 0.1743 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_SVR 154 0.1948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_XGB 154 0.1934 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_Cycle_AR 158 0.1991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_Cycle_NoAR 120 0.4631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_Cycle_SVR 158 0.1749 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_Cycle_XGB 158 0.2096 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_NoCycle_AR 150 0.2089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_NoCycle_NoAR 112 0.4506 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_NoCycle_SVR 150 0.158 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(5)_NoCycle_XGB 150 0.1764 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_AR 154 0.2015 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 116 0.1627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_SVR 154 0.1703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_XGB 154 0.1711 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_Cycle_AR 158 0.2015 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_Cycle_NoAR 120 0.1627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_Cycle_SVR 158 0.1703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_Cycle_XGB 158 0.1711 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_NoCycle_AR 150 0.2151 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_NoCycle_NoAR 112 0.4499 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_NoCycle_SVR 150 0.1596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingAverage(7)_NoCycle_XGB 150 0.1612 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_AR 154 0.1791 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 116 0.1636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_SVR 154 0.1787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_XGB 154 0.1803 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_Cycle_None_AR 158 0.1942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_Cycle_None_NoAR 120 0.3454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_Cycle_None_SVR 158 0.1533 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_Cycle_None_XGB 158 0.1757 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_NoCycle_AR 150 0.1942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_NoCycle_NoAR 112 0.3454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_NoCycle_SVR 150 0.1533 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(12)_NoCycle_XGB 150 0.1757 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_AR 154 0.2405 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 116 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_SVR 154 0.213 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_XGB 154 0.2137 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_Cycle_AR 158 0.2413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_Cycle_NoAR 120 0.4908 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_Cycle_SVR 158 0.2237 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_Cycle_XGB 158 0.2162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_NoCycle_AR 150 0.2215 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_NoCycle_NoAR 112 0.4806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_NoCycle_SVR 150 0.2135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(5)_NoCycle_XGB 150 0.2039 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_AR 154 0.2593 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 116 0.1997 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_SVR 154 0.2344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_XGB 154 0.2333 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_Cycle_AR 158 0.2593 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_Cycle_NoAR 120 0.1997 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_Cycle_SVR 158 0.2344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_Cycle_XGB 158 0.2333 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_NoCycle_AR 150 0.2243 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_NoCycle_NoAR 112 0.4591 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_NoCycle_SVR 150 0.1692 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_MovingMedian(7)_NoCycle_XGB 150 0.1923 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_AR 106 0.2024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_NoAR 68 0.2514 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_SVR 106 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_XGB 106 0.1867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_Cycle_AR 110 0.1829 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_Cycle_NoAR 72 0.4969 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_Cycle_SVR 110 0.1687 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_Cycle_XGB 110 0.1902 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_NoCycle_AR 102 0.185 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_NoCycle_NoAR 64 0.4395 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_NoCycle_SVR 102 0.1721 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_ConstantTrend_NoCycle_XGB 102 0.1542 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_AR 138 0.2027 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_NoAR 100 0.2364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_SVR 138 0.2456 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_XGB 138 0.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_Cycle_AR 142 0.256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_Cycle_NoAR 104 0.2824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_Cycle_SVR 142 0.237 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_Cycle_XGB 142 0.2093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_NoCycle_AR 134 0.256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_NoCycle_NoAR 96 0.2824 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_NoCycle_SVR 134 0.237 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_Lag1Trend_NoCycle_XGB 134 0.2093 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_AR 122 0.2249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_NoAR 84 0.2018 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_SVR 122 0.2138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_XGB 122 0.2159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_Cycle_AR 126 0.2138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_Cycle_NoAR 88 0.3311 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_Cycle_SVR 126 0.2054 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_Cycle_XGB 126 0.2001 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_NoCycle_AR 118 0.2022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_NoCycle_NoAR 80 0.3169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_NoCycle_SVR 118 0.1857 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_LinearTrend_NoCycle_XGB 118 0.1623 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_AR 122 0.2257 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_NoAR 84 0.1994 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_SVR 122 0.1935 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_XGB 122 0.1884 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_Cycle_None_AR 126 0.2057 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_Cycle_None_NoAR 88 0.3673 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_Cycle_None_SVR 126 0.1872 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_Cycle_None_XGB 126 0.1867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_NoCycle_AR 118 0.2057 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_NoCycle_NoAR 80 0.3673 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_NoCycle_SVR 118 0.1872 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_10_Ozone Quantization_PolyTrend_NoCycle_XGB 118 0.1867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_AR 154 0.2138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 116 0.1967 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_SVR 154 0.1781 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_Seasonal_MonthOfYear_XGB 154 0.1889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_Cycle_None_AR 158 0.1876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_Cycle_None_NoAR 120 0.3317 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_Cycle_None_SVR 158 0.1436 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_Cycle_None_XGB 158 0.1528 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_NoCycle_AR 150 0.1876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_NoCycle_NoAR 112 0.3317 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_NoCycle_SVR 150 0.1436 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(12)_NoCycle_XGB 150 0.1528 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_AR 154 0.2005 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 116 0.1811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_SVR 154 0.182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_Seasonal_MonthOfYear_XGB 154 0.1922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_Cycle_AR 158 0.2005 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_Cycle_NoAR 120 0.1811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_Cycle_SVR 158 0.182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_Cycle_XGB 158 0.1922 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_NoCycle_AR 150 0.1839 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_NoCycle_NoAR 112 0.4491 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_NoCycle_SVR 150 0.1562 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(5)_NoCycle_XGB 150 0.1624 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_AR 154 0.1837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 116 0.1802 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_SVR 154 0.1823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_Seasonal_MonthOfYear_XGB 154 0.1816 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_Cycle_None_AR 158 0.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_Cycle_None_NoAR 120 0.4486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_Cycle_None_SVR 158 0.1461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_Cycle_None_XGB 158 0.1632 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_NoCycle_AR 150 0.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_NoCycle_NoAR 112 0.4486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_NoCycle_SVR 150 0.1461 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingAverage(7)_NoCycle_XGB 150 0.1632 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_AR 154 0.1813 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 116 0.1605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_SVR 154 0.1653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_Seasonal_MonthOfYear_XGB 154 0.1465 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_Cycle_None_AR 158 0.1946 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_Cycle_None_NoAR 120 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_Cycle_None_SVR 158 0.1434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_Cycle_None_XGB 158 0.1889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_NoCycle_AR 150 0.1946 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_NoCycle_NoAR 112 0.3332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_NoCycle_SVR 150 0.1434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(12)_NoCycle_XGB 150 0.1889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_AR 154 0.2284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 116 0.1717 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_SVR 154 0.1946 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_Seasonal_MonthOfYear_XGB 154 0.2116 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_Cycle_AR 158 0.2028 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_Cycle_NoAR 120 0.4779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_Cycle_SVR 158 0.2083 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_Cycle_XGB 158 0.2165 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_NoCycle_AR 150 0.1941 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_NoCycle_NoAR 112 0.4764 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_NoCycle_SVR 150 0.2098 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(5)_NoCycle_XGB 150 0.1966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_AR 154 0.2208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 116 0.184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_SVR 154 0.189 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_Seasonal_MonthOfYear_XGB 154 0.2046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_Cycle_AR 158 0.2208 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_Cycle_NoAR 120 0.184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_Cycle_SVR 158 0.189 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_Cycle_XGB 158 0.2046 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_NoCycle_AR 150 0.2029 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_NoCycle_NoAR 112 0.466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_NoCycle_SVR 150 0.1581 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_MovingMedian(7)_NoCycle_XGB 150 0.1745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_AR 106 0.2023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_NoAR 68 0.256 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_SVR 106 0.2153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_Seasonal_MonthOfYear_XGB 106 0.2119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_Cycle_None_AR 110 0.1891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_Cycle_None_NoAR 72 0.4772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_Cycle_None_SVR 110 0.1928 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_Cycle_None_XGB 110 0.1431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_NoCycle_AR 102 0.1891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_NoCycle_NoAR 64 0.4772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_NoCycle_SVR 102 0.1928 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_ConstantTrend_NoCycle_XGB 102 0.1431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_AR 138 0.2276 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_NoAR 100 0.2369 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_SVR 138 0.2294 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_Seasonal_MonthOfYear_XGB 138 0.214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_Cycle_AR 142 0.2431 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_Cycle_NoAR 104 0.2632 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_Cycle_SVR 142 0.2217 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_Cycle_XGB 142 0.2142 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_NoCycle_AR 134 0.2459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_NoCycle_NoAR 96 0.2795 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_NoCycle_SVR 134 0.2435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_Lag1Trend_NoCycle_XGB 134 0.2269 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_AR 122 0.2286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_NoAR 84 0.1885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_SVR 122 0.1936 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_Seasonal_MonthOfYear_XGB 122 0.2068 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_Cycle_None_AR 126 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_Cycle_None_NoAR 88 0.3203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_Cycle_None_SVR 126 0.1574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_Cycle_None_XGB 126 0.1818 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_NoCycle_AR 118 0.2033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_NoCycle_NoAR 80 0.3203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_NoCycle_SVR 118 0.1574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_LinearTrend_NoCycle_XGB 118 0.1818 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_AR 122 0.2272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_NoAR 84 0.2042 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_SVR 122 0.1929 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_Seasonal_MonthOfYear_XGB 122 0.2165 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_Cycle_None_AR 126 0.1753 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_Cycle_None_NoAR 88 0.3985 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_Cycle_None_SVR 126 0.1771 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_Cycle_None_XGB 126 0.1626 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_NoCycle_AR 118 0.1753 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_NoCycle_NoAR 80 0.3985 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_NoCycle_SVR 118 0.1771 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Quantized_20_Ozone Quantization_PolyTrend_NoCycle_XGB 118 0.1626 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2037 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.2675 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Seasonal_MonthOfYear_SVR 74 0.2131 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Seasonal_MonthOfYear_XGB 74 0.1983 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Cycle_AR 78 0.2469 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Cycle_NoAR 40 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Cycle_SVR 78 0.1814 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_Cycle_XGB 78 0.1984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_NoCycle_AR 70 0.2465 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_NoCycle_NoAR 32 0.5342 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_NoCycle_SVR 70 0.1795 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_ConstantTrend_NoCycle_XGB 70 0.1837 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_Seasonal_MonthOfYear_SVR 106 0.2061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_Seasonal_MonthOfYear_XGB 106 0.2827 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_Cycle_AR 110 0.2241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_Cycle_NoAR 72 0.2852 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_Cycle_SVR 110 0.2275 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_Cycle_XGB 110 0.3241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_NoCycle_AR 102 0.2174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_NoCycle_SVR 102 0.2159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_Lag1Trend_NoCycle_XGB 102 0.3135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_Seasonal_MonthOfYear_AR 90 0.2169 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.2118 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_Seasonal_MonthOfYear_SVR 90 0.1597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_Seasonal_MonthOfYear_XGB 90 0.1944 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_Cycle_None_AR 94 0.2153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_Cycle_None_NoAR 56 0.3155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_Cycle_None_SVR 94 0.1719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_Cycle_None_XGB 94 0.2338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_NoCycle_AR 86 0.2153 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_NoCycle_NoAR 48 0.3155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_NoCycle_SVR 86 0.1719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_LinearTrend_NoCycle_XGB 86 0.2338 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_Seasonal_MonthOfYear_AR 90 0.1808 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.1794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_Seasonal_MonthOfYear_SVR 90 0.184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_Seasonal_MonthOfYear_XGB 90 0.1844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_Cycle_None_AR 94 0.1676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_Cycle_None_NoAR 56 0.3443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_Cycle_None_SVR 94 0.1501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_Cycle_None_XGB 94 0.2057 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_NoCycle_AR 86 0.1676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_NoCycle_NoAR 48 0.3443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_NoCycle_SVR 86 0.1501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_PolyTrend_NoCycle_XGB 86 0.2057 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_Seasonal_MonthOfYear_AR 122 0.1691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 84 0.1668 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_Seasonal_MonthOfYear_SVR 122 0.1652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_Seasonal_MonthOfYear_XGB 122 0.2159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_Cycle_None_AR 126 0.1739 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_Cycle_None_NoAR 88 0.3105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_Cycle_None_SVR 126 0.1539 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_Cycle_None_XGB 126 0.201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_NoCycle_AR 118 0.1739 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_NoCycle_NoAR 80 0.3105 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_NoCycle_SVR 118 0.1539 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(12)_NoCycle_XGB 118 0.201 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_Seasonal_MonthOfYear_AR 122 0.1627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 84 0.171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_Seasonal_MonthOfYear_SVR 122 0.1641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_Seasonal_MonthOfYear_XGB 122 0.2062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_Cycle_AR 126 0.1627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_Cycle_NoAR 88 0.171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_Cycle_SVR 126 0.1641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_Cycle_XGB 126 0.2062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_NoCycle_AR 118 0.1843 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_NoCycle_NoAR 80 0.4569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_NoCycle_SVR 118 0.1572 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(5)_NoCycle_XGB 118 0.1936 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_Seasonal_MonthOfYear_AR 122 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 84 0.1666 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_Seasonal_MonthOfYear_SVR 122 0.1776 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_Seasonal_MonthOfYear_XGB 122 0.215 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_Cycle_AR 126 0.174 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_Cycle_NoAR 88 0.1666 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_Cycle_SVR 126 0.1776 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_Cycle_XGB 126 0.215 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_NoCycle_AR 118 0.1784 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_NoCycle_NoAR 80 0.4646 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_NoCycle_SVR 118 0.1555 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingAverage(7)_NoCycle_XGB 118 0.1976 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_Seasonal_MonthOfYear_AR 122 0.2036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 84 0.1679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_Seasonal_MonthOfYear_SVR 122 0.1719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_Seasonal_MonthOfYear_XGB 122 0.1912 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_Cycle_None_AR 126 0.1759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_Cycle_None_NoAR 88 0.3379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_Cycle_None_SVR 126 0.1452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_Cycle_None_XGB 126 0.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_NoCycle_AR 118 0.1759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_NoCycle_NoAR 80 0.3379 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_NoCycle_SVR 118 0.1452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(12)_NoCycle_XGB 118 0.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_Seasonal_MonthOfYear_AR 122 0.2024 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 84 0.1821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_Seasonal_MonthOfYear_SVR 122 0.1942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_Seasonal_MonthOfYear_XGB 122 0.2304 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_Cycle_None_AR 126 0.2309 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_Cycle_None_NoAR 88 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_Cycle_None_SVR 126 0.1726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_Cycle_None_XGB 126 0.2157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_NoCycle_AR 118 0.2309 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_NoCycle_NoAR 80 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_NoCycle_SVR 118 0.1726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(5)_NoCycle_XGB 118 0.2157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_Seasonal_MonthOfYear_AR 122 0.1652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 84 0.1694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_Seasonal_MonthOfYear_SVR 122 0.1688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_Seasonal_MonthOfYear_XGB 122 0.1727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_Cycle_AR 126 0.1652 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_Cycle_NoAR 88 0.1694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_Cycle_SVR 126 0.1688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_Cycle_XGB 126 0.1727 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_NoCycle_AR 118 0.1673 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_NoCycle_NoAR 80 0.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_NoCycle_SVR 118 0.1575 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Logit_Ozone Logit_MovingMedian(7)_NoCycle_XGB 118 0.2522 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.2846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.2686 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Seasonal_MonthOfYear_SVR 74 0.1821 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Seasonal_MonthOfYear_XGB 74 0.2585 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Cycle_None_AR 78 0.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Cycle_None_NoAR 40 0.6857 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Cycle_None_SVR 78 0.2302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_Cycle_None_XGB 78 0.3181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_NoCycle_AR 70 0.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_NoCycle_NoAR 32 0.6857 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_NoCycle_SVR 70 0.2302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_ConstantTrend_NoCycle_XGB 70 0.3181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.1754 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2218 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_Seasonal_MonthOfYear_SVR 106 0.2136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_Seasonal_MonthOfYear_XGB 106 0.4551 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_Cycle_AR 110 0.2065 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_Cycle_NoAR 72 0.2574 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_Cycle_SVR 110 0.222 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_Cycle_XGB 110 0.2836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_NoCycle_AR 102 0.2323 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_NoCycle_SVR 102 0.2417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_Lag1Trend_NoCycle_XGB 102 0.2711 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_Seasonal_MonthOfYear_AR 90 0.5578 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.542 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_Seasonal_MonthOfYear_SVR 90 0.2229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_Seasonal_MonthOfYear_XGB 90 0.5009 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_Cycle_None_AR 94 0.4779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_Cycle_None_NoAR 56 0.3336 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_Cycle_None_SVR 94 0.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_Cycle_None_XGB 94 0.3038 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_NoCycle_AR 86 0.4779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_NoCycle_NoAR 48 0.3336 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_NoCycle_SVR 86 0.1927 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_LinearTrend_NoCycle_XGB 86 0.3038 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_Seasonal_MonthOfYear_AR 90 0.2364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.2176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_Seasonal_MonthOfYear_SVR 90 0.2003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_Seasonal_MonthOfYear_XGB 90 0.2152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_Cycle_None_AR 94 0.2388 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_Cycle_None_NoAR 56 0.4408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_Cycle_None_SVR 94 0.191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_Cycle_None_XGB 94 0.232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_NoCycle_AR 86 0.2388 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_NoCycle_NoAR 48 0.4408 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_NoCycle_SVR 86 0.191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_PolyTrend_NoCycle_XGB 86 0.232 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_Seasonal_MonthOfYear_AR 122 0.1883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 84 0.1844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_Seasonal_MonthOfYear_SVR 122 0.1729 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_Seasonal_MonthOfYear_XGB 122 0.1641 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_Cycle_None_AR 126 0.1782 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_Cycle_None_NoAR 88 0.3452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_Cycle_None_SVR 126 0.1655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_Cycle_None_XGB 126 0.2577 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_NoCycle_AR 118 0.1782 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_NoCycle_NoAR 80 0.3452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_NoCycle_SVR 118 0.1655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(12)_NoCycle_XGB 118 0.2577 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_Seasonal_MonthOfYear_AR 122 0.1805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 84 0.2354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_Seasonal_MonthOfYear_SVR 122 0.1874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_Seasonal_MonthOfYear_XGB 122 0.2452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_Cycle_AR 126 0.2262 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_Cycle_NoAR 88 0.4867 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_Cycle_SVR 126 0.2067 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_Cycle_XGB 126 0.2382 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_NoCycle_AR 118 0.196 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_NoCycle_NoAR 80 0.4748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_NoCycle_SVR 118 0.1712 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(5)_NoCycle_XGB 118 0.2417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_Seasonal_MonthOfYear_AR 122 0.1671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 84 0.2094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_Seasonal_MonthOfYear_SVR 122 0.1671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_Seasonal_MonthOfYear_XGB 122 0.2422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_Cycle_AR 126 0.1671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_Cycle_NoAR 88 0.2094 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_Cycle_SVR 126 0.1671 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_Cycle_XGB 126 0.2422 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_NoCycle_AR 118 0.2028 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_NoCycle_NoAR 80 0.4808 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_NoCycle_SVR 118 0.1696 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingAverage(7)_NoCycle_XGB 118 0.2274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_Seasonal_MonthOfYear_AR 122 0.2425 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 84 0.163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_Seasonal_MonthOfYear_SVR 122 0.173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_Seasonal_MonthOfYear_XGB 122 0.2302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_Cycle_None_AR 126 0.2292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_Cycle_None_NoAR 88 0.3394 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_Cycle_None_SVR 126 0.1651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_Cycle_None_XGB 126 0.2606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_NoCycle_AR 118 0.2292 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_NoCycle_NoAR 80 0.3394 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_NoCycle_SVR 118 0.1651 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(12)_NoCycle_XGB 118 0.2606 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_Seasonal_MonthOfYear_AR 122 0.2284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 84 0.2193 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_Seasonal_MonthOfYear_SVR 122 0.2171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_Seasonal_MonthOfYear_XGB 122 0.3145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_Cycle_None_AR 126 0.3705 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_Cycle_None_NoAR 88 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_Cycle_None_SVR 126 0.1866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_Cycle_None_XGB 126 0.2512 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_NoCycle_AR 118 0.3705 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_NoCycle_NoAR 80 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_NoCycle_SVR 118 0.1866 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(5)_NoCycle_XGB 118 0.2512 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_Seasonal_MonthOfYear_AR 122 0.2459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 84 0.205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_Seasonal_MonthOfYear_SVR 122 0.2089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_Seasonal_MonthOfYear_XGB 122 0.272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_Cycle_AR 126 0.2459 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_Cycle_NoAR 88 0.205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_Cycle_SVR 126 0.2089 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_Cycle_XGB 126 0.272 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_NoCycle_AR 118 0.2592 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_NoCycle_NoAR 80 0.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_NoCycle_SVR 118 0.1798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Fisher_Ozone Fisher_MovingMedian(7)_NoCycle_XGB 118 0.2841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Seasonal_MonthOfYear_AR 74 0.1822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Seasonal_MonthOfYear_NoAR 36 0.2682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Seasonal_MonthOfYear_SVR 74 0.2508 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Seasonal_MonthOfYear_XGB 74 0.2258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Cycle_AR 78 0.1869 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Cycle_NoAR 40 0.5101 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Cycle_SVR 78 0.1775 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_Cycle_XGB 78 0.1946 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_NoCycle_AR 70 0.1845 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_NoCycle_NoAR 32 0.507 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_NoCycle_SVR 70 0.1767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_ConstantTrend_NoCycle_XGB 70 0.1916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_Seasonal_MonthOfYear_AR 106 0.2023 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_Seasonal_MonthOfYear_NoAR 68 0.2159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_Seasonal_MonthOfYear_SVR 106 0.2236 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_Seasonal_MonthOfYear_XGB 106 0.2302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_Cycle_AR 110 0.228 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_Cycle_NoAR 72 0.2859 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_Cycle_SVR 110 0.2327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_Cycle_XGB 110 0.2243 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_NoCycle_AR 102 0.2149 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_NoCycle_NoAR 64 0.2778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_NoCycle_SVR 102 0.229 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_Lag1Trend_NoCycle_XGB 102 0.2575 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_Seasonal_MonthOfYear_AR 90 0.1621 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_Seasonal_MonthOfYear_NoAR 52 0.1585 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_Seasonal_MonthOfYear_SVR 90 0.1735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_Seasonal_MonthOfYear_XGB 90 0.1634 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_Cycle_AR 94 0.1387 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_Cycle_NoAR 56 0.3207 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_Cycle_SVR 94 0.1669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_Cycle_XGB 94 0.1744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_NoCycle_AR 86 0.1384 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_NoCycle_NoAR 48 0.3181 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_NoCycle_SVR 86 0.1669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_LinearTrend_NoCycle_XGB 86 0.1525 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_Seasonal_MonthOfYear_AR 90 0.18 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_Seasonal_MonthOfYear_NoAR 52 0.1966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_Seasonal_MonthOfYear_SVR 90 0.179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_Seasonal_MonthOfYear_XGB 90 0.2013 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_Cycle_None_AR 94 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_Cycle_None_NoAR 56 0.389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_Cycle_None_SVR 94 0.1817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_Cycle_None_XGB 94 0.1778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_NoCycle_AR 86 0.1595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_NoCycle_NoAR 48 0.389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_NoCycle_SVR 86 0.1817 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_PolyTrend_NoCycle_XGB 86 0.1778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_Seasonal_MonthOfYear_AR 122 0.1703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_Seasonal_MonthOfYear_NoAR 84 0.1756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_Seasonal_MonthOfYear_SVR 122 0.1832 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_Seasonal_MonthOfYear_XGB 122 0.1892 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_Cycle_None_AR 126 0.1608 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_Cycle_None_NoAR 88 0.3276 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_Cycle_None_SVR 126 0.1494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_Cycle_None_XGB 126 0.1811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_NoCycle_AR 118 0.1608 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_NoCycle_NoAR 80 0.3276 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_NoCycle_SVR 118 0.1494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(12)_NoCycle_XGB 118 0.1811 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_Seasonal_MonthOfYear_AR 122 0.1752 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_Seasonal_MonthOfYear_NoAR 84 0.1752 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_Seasonal_MonthOfYear_SVR 122 0.184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_Seasonal_MonthOfYear_XGB 122 0.2061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_Cycle_AR 126 0.1752 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_Cycle_NoAR 88 0.1752 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_Cycle_SVR 126 0.184 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_Cycle_XGB 126 0.2061 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_NoCycle_AR 118 0.1519 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_NoCycle_NoAR 80 0.4583 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_NoCycle_SVR 118 0.1475 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(5)_NoCycle_XGB 118 0.176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_Seasonal_MonthOfYear_AR 122 0.1655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_Seasonal_MonthOfYear_NoAR 84 0.1631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_Seasonal_MonthOfYear_SVR 122 0.1693 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_Seasonal_MonthOfYear_XGB 122 0.1748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_Cycle_AR 126 0.1655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_Cycle_NoAR 88 0.1631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_Cycle_SVR 126 0.1693 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_Cycle_XGB 126 0.1748 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_NoCycle_AR 118 0.1583 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_NoCycle_NoAR 80 0.4659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_NoCycle_SVR 118 0.1521 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingAverage(7)_NoCycle_XGB 118 0.145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_Seasonal_MonthOfYear_AR 122 0.1659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_Seasonal_MonthOfYear_NoAR 84 0.1623 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_Seasonal_MonthOfYear_SVR 122 0.1665 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_Seasonal_MonthOfYear_XGB 122 0.1975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_Cycle_None_AR 126 0.1567 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_Cycle_None_NoAR 88 0.3387 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_Cycle_None_SVR 126 0.1481 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_Cycle_None_XGB 126 0.1944 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_NoCycle_AR 118 0.1567 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_NoCycle_NoAR 80 0.3387 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_NoCycle_SVR 118 0.1481 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(12)_NoCycle_XGB 118 0.1944 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_Seasonal_MonthOfYear_AR 122 0.1855 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_Seasonal_MonthOfYear_NoAR 84 0.1771 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_Seasonal_MonthOfYear_SVR 122 0.1856 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_Seasonal_MonthOfYear_XGB 122 0.2032 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_Cycle_None_AR 126 0.1645 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_Cycle_None_NoAR 88 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_Cycle_None_SVR 126 0.1762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_Cycle_None_XGB 126 0.2048 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_NoCycle_AR 118 0.1645 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_NoCycle_NoAR 80 0.4799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_NoCycle_SVR 118 0.1762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(5)_NoCycle_XGB 118 0.2048 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_Seasonal_MonthOfYear_AR 122 0.1792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_Seasonal_MonthOfYear_NoAR 84 0.1669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_Seasonal_MonthOfYear_SVR 122 0.1842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_Seasonal_MonthOfYear_XGB 122 0.1768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_Cycle_AR 126 0.1792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_Cycle_NoAR 88 0.1669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_Cycle_SVR 126 0.1842 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_Cycle_XGB 126 0.1768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_NoCycle_AR 118 0.1666 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_NoCycle_NoAR 80 0.4636 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_NoCycle_SVR 118 0.1544 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Anscombe_Ozone Anscombe_MovingMedian(7)_NoCycle_XGB 118 0.1667 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 77.75982856750488 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [LinearTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1716 MAPE_Forecast=0.1873 MAPE_Test=None -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1613 SMAPE_Forecast=0.1965 SMAPE_Test=None -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7556 MASE_Forecast=0.7838 MASE_Test=None -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6755522441422742 L1_Forecast=0.5734172181704287 L1_Test=None -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8784816458194629 L2_Forecast=0.6795581528639223 L2_Test=None -INFO:pyaf.std:MODEL_COMPLEXITY 41 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Anscombe_Ozone' Min=1.224744871391589 Max=2.345207879911715 Mean=1.6888656389128833 StdDev=0.23126713490313816 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Anscombe_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL 'Anscombe_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Anscombe_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1642 MAPE_Forecast=0.1384 MAPE_Test=0.1408 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1559 SMAPE_Forecast=0.1512 SMAPE_Test=0.1438 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7168 MASE_Forecast=0.6055 MASE_Test=0.7524 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.62999952384404 L1_Forecast=0.47008581809495725 L1_Test=0.35565668910694576 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8311449866422522 L2_Forecast=0.6550475624599839 L2_Test=0.4346696712588745 +INFO:pyaf.std:MODEL_COMPLEXITY 86 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:ANSCOMBE_TRANSFORMATION Anscombe 0.375 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (1.8694442837265477, array([-0.27591835])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Anscombe_Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4817817517029717 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.24078579285082652 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.20479052751799376 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16602142570871645 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.15601493235515343 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.1448045345050922 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag16 -0.1239689031967613 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.10993150122019982 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag8 -0.10687200706183947 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag41 0.1020555297394612 +INFO:pyaf.std:AR_MODEL_COEFF 1 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.3209998211502658 +INFO:pyaf.std:AR_MODEL_COEFF 2 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.11917290422114361 +INFO:pyaf.std:AR_MODEL_COEFF 3 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag51 -0.10966014115710274 +INFO:pyaf.std:AR_MODEL_COEFF 4 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.10811218454630063 +INFO:pyaf.std:AR_MODEL_COEFF 5 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.08843747251747286 +INFO:pyaf.std:AR_MODEL_COEFF 6 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.088412812875769 +INFO:pyaf.std:AR_MODEL_COEFF 7 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.08727106698203252 +INFO:pyaf.std:AR_MODEL_COEFF 8 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.08115916216732097 +INFO:pyaf.std:AR_MODEL_COEFF 9 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.0811466286025117 +INFO:pyaf.std:AR_MODEL_COEFF 10 Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_Lag29 -0.07382216509764589 INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 0.4757804870605469 - Split Transformation ... TestMAPE IC -0 (0.5, 0.1, 0.0) _Ozone ... None 0 -1 (0.5, 0.1, 0.0) Anscombe_Ozone ... None 0 -2 (0.5, 0.1, 0.0) Anscombe_Ozone ... None 0 -3 (0.5, 0.1, 0.0) Logit_Ozone ... None 0 -4 (0.5, 0.1, 0.0) Anscombe_Ozone ... None 0 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.8694138526916504 + Split Transformation ... TestMASE IC +0 None Anscombe_Ozone ... 0.7524 1 +1 None Anscombe_Ozone ... 0.7355 1 +2 None _Ozone ... 1.1671 1 +3 None _Ozone ... 1.1522 1 +4 None Quantized_20_Ozone ... 1.2252 0 -[5 rows x 9 columns] -Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', - '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', - '_Ozone_LinearTrend_residue_zeroCycle', - '_Ozone_LinearTrend_residue_zeroCycle_residue', - '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)', - '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)_residue', - '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', - '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', - '_Ozone_TransformedForecast', 'Ozone_Forecast', - '_Ozone_TransformedResidue', 'Ozone_Residue', +[5 rows x 21 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', 'Anscombe_Ozone', + 'Anscombe_Ozone_LinearTrend', 'Anscombe_Ozone_LinearTrend_residue', + 'Anscombe_Ozone_LinearTrend_residue_zeroCycle', + 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue', + 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)', + 'Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)_residue', + 'Anscombe_Ozone_Trend', 'Anscombe_Ozone_Trend_residue', + 'Anscombe_Ozone_Cycle', 'Anscombe_Ozone_Cycle_residue', + 'Anscombe_Ozone_AR', 'Anscombe_Ozone_AR_residue', + 'Anscombe_Ozone_TransformedForecast', 'Ozone_Forecast', + 'Anscombe_Ozone_TransformedResidue', 'Ozone_Residue', 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], dtype='object') @@ -7998,47 +3453,49 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.285624 -205 1972-02-01 NaN 0.738594 -206 1972-03-01 NaN 0.515769 -207 1972-04-01 NaN 1.123423 -208 1972-05-01 NaN 1.030676 -209 1972-06-01 NaN 1.801763 -210 1972-07-01 NaN 1.610546 -211 1972-08-01 NaN 1.645255 -212 1972-09-01 NaN 1.140365 -213 1972-10-01 NaN 0.941300 -214 1972-11-01 NaN 0.287191 -215 1972-12-01 NaN 0.063853 +204 1972-01-01 NaN 1.024415 +205 1972-02-01 NaN 1.541823 +206 1972-03-01 NaN 2.017001 +207 1972-04-01 NaN 2.430853 +208 1972-05-01 NaN 2.879409 +209 1972-06-01 NaN 3.280126 +210 1972-07-01 NaN 3.414366 +211 1972-08-01 NaN 3.394543 +212 1972-09-01 NaN 3.075428 +213 1972-10-01 NaN 2.214360 +214 1972-11-01 NaN 1.542736 +215 1972-12-01 NaN 1.112522 { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Anscombe_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Anscombe", + "Trend": "LinearTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "41", - "MAE": "0.5734172181704287", - "MAPE": "0.1873", - "MASE": "0.7838", - "RMSE": "0.6795581528639223" + "Model_Performance": { + "COMPLEXITY": "86", + "MAE": "0.47008581809495725", + "MAPE": "0.1384", + "MASE": "0.6055", + "RMSE": "0.6550475624599839" + } } } @@ -8047,7 +3504,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.7374381707,"193":1.1320086299,"194":1.8549839453,"195":1.2883584088,"196":2.5112248895,"197":2.5574173159,"198":3.1928923511,"199":2.5688374073,"200":2.0571623293,"201":1.4624391751,"202":1.1275875442,"203":0.5646769855,"204":0.2856243781,"205":0.7385938903,"206":0.5157685586,"207":1.1234230133,"208":1.0306755197,"209":1.8017628552,"210":1.6105459481,"211":1.6452553303,"212":1.1403645026,"213":0.9413002388,"214":0.287190989,"215":0.0638531478}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.1202220186,"193":1.9384005467,"194":2.4328115966,"195":2.4019066825,"196":3.1709712472,"197":3.1681773447,"198":4.1619230994,"199":3.5832671054,"200":3.0640691022,"201":2.3561708754,"202":1.7120744534,"203":1.2276411329,"204":1.0244151392,"205":1.5418229577,"206":2.0170010596,"207":2.4308531985,"208":2.8794089774,"209":3.2801258093,"210":3.4143660536,"211":3.39454272,"212":3.0754284218,"213":2.2143596364,"214":1.5427356286,"215":1.1125216345}} diff --git a/tests/references/xgb_test_air_passengers_xgb.log b/tests/references/xgb_test_air_passengers_xgb.log index 0b06e0d80..ed8a0bc0b 100644 --- a/tests/references/xgb_test_air_passengers_xgb.log +++ b/tests/references/xgb_test_air_passengers_xgb.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 3.428385019302368 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 6.782058954238892 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -11,34 +11,44 @@ INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle_resid INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0349 MAPE_Forecast=0.0217 MAPE_Test=0.0541 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0346 SMAPE_Forecast=0.022 SMAPE_Test=0.0558 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3563 MASE_Forecast=0.2287 MASE_Test=0.495 -INFO:pyaf.std:MODEL_L1 L1_Fit=6.661608657426588 L1_Forecast=8.471185219451277 L1_Test=22.27708217077865 -INFO:pyaf.std:MODEL_L2 L2_Fit=8.553917012084606 L2_Forecast=11.97176290466267 L2_Test=23.591231598022006 +INFO:pyaf.std:MODEL_L1 L1_Fit=6.6616086574266005 L1_Forecast=8.471185219451263 L1_Test=22.27708217077866 +INFO:pyaf.std:MODEL_L2 L2_Fit=8.553917012084607 L2_Forecast=11.971762904662638 L2_Test=23.59123159802204 INFO:pyaf.std:MODEL_COMPLEXITY 40 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (114.90523344816273, array([197.60619977])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (114.90523344816275, array([197.60619977])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag1 0.7786311442771898 -INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag12 0.739375705368332 -INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag13 -0.5331389013991887 -INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag25 -0.2876891887152456 -INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag24 0.2430278567579437 -INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag33 -0.18406731004063498 -INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag5 0.1716184438514903 -INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag29 -0.16896686902773136 -INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16816626466278783 -INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag16 -0.16495284212978784 +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag1 0.7786311442771912 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag12 0.7393757053683319 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag13 -0.5331389013991886 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag25 -0.2876891887152461 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag24 0.24302785675794508 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag33 -0.18406731004063412 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag5 0.17161844385149028 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag29 -0.16896686902773 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16816626466278906 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag16 -0.16495284212978803 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.252009391784668 -INFO:pyaf.std:START_FORECASTING 'AirPassengers' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'AirPassengers' 0.2751433849334717 +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 28.734424114227295 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 1.1909408569335938 Split Transformation ... ForecastMAPE TestMAPE 0 None Diff_AirPassengers ... 0.0205 0.0402 1 None _AirPassengers ... 0.0217 0.0541 @@ -107,31 +117,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "40", - "MAE": "8.471185219451277", - "MAPE": "0.0217", - "MASE": "0.2287", - "RMSE": "11.97176290466267" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "40", + "MAE": "8.471185219451263", + "MAPE": "0.0217", + "MASE": "0.2287", + "RMSE": "11.971762904662638" + } } } @@ -140,7 +152,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":336.76433931,"121":322.8158768976,"122":370.1970011391,"123":378.032857026,"124":398.9335945668,"125":490.9916896704,"126":527.6050111738,"127":547.2692898967,"128":447.2569355178,"129":389.1498397716,"130":336.5874463251,"131":365.0545119965,"132":404.1927658782,"133":362.2723575753,"134":407.3659989041,"135":392.3773701384,"136":426.0082038065,"137":494.6325800918,"138":561.1241745773,"139":558.3950905274,"140":437.9865222534,"141":374.1309595368,"142":319.9068836751,"143":355.980727},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":380.728110585,"133":325.1207093518,"134":357.9663450975,"135":272.4070322436,"136":98.7776096737,"137":-219.4948761306,"138":-928.2586710993,"139":-2259.4796258236,"140":-4091.7357686501,"141":-5051.1682697063,"142":-4712.3577817419,"143":-7009.1109734824},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":427.6574211713,"133":399.4240057988,"134":456.7656527106,"135":512.3477080331,"136":753.2387979394,"137":1208.7600363143,"138":2050.507020254,"139":3376.2698068784,"140":4967.708813157,"141":5799.4301887799,"142":5352.171549092,"143":7721.0724274824}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":336.76433931,"121":322.8158768976,"122":370.1970011391,"123":378.032857026,"124":398.9335945668,"125":490.9916896704,"126":527.6050111738,"127":547.2692898967,"128":447.2569355178,"129":389.1498397716,"130":336.5874463251,"131":365.0545119965,"132":404.1927658782,"133":362.2723575753,"134":407.3659989041,"135":392.3773701384,"136":426.0082038065,"137":494.6325800918,"138":561.1241745773,"139":558.3950905274,"140":437.9865222534,"141":374.1309595368,"142":319.9068836751,"143":355.980727},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":380.728110585,"133":325.1207093518,"134":357.9663450975,"135":272.4070322436,"136":98.7776096737,"137":-219.4948761306,"138":-928.2586710993,"139":-2259.4796258236,"140":-4091.7357686502,"141":-5051.1682697064,"142":-4712.357781742,"143":-7009.1109734824},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":427.6574211713,"133":399.4240057988,"134":456.7656527106,"135":512.3477080331,"136":753.2387979394,"137":1208.7600363143,"138":2050.507020254,"139":3376.2698068785,"140":4967.708813157,"141":5799.43018878,"142":5352.1715490921,"143":7721.0724274824}} diff --git a/tests/references/xgb_test_air_passengers_xgb_only.log b/tests/references/xgb_test_air_passengers_xgb_only.log index 481e97716..99976b5cd 100644 --- a/tests/references/xgb_test_air_passengers_xgb_only.log +++ b/tests/references/xgb_test_air_passengers_xgb_only.log @@ -1,4 +1,36 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(33) 32 0.163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_XGB(33) 24 0.163 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(33) 64 0.0494 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33) 56 0.0434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(33) 48 0.0809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_XGB(33) 40 0.0809 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(33) 48 0.1586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_XGB(33) 40 0.1586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(33) 64 0.0611 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_XGB(33) 56 0.0611 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(33) 96 0.103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33) 88 0.062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 0.0605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_XGB(33) 72 0.0605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 0.1726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_XGB(33) 72 0.1135 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(33) 64 1910118.6746 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_XGB(33) 56 1910118.9531 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(33) 96 1910118.8073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33) 88 1910118.8073 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_XGB(33) 72 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_XGB(33) 72 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(33) 64 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_XGB(33) 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(33) 96 0.0926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33) 88 0.0957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_XGB(33) 72 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 0.2691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_XGB(33) 72 0.2691 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_XGB 32 0.1051 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_XGB 24 0.1051 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_XGB 64 0.0427 @@ -31,7 +63,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integra INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_XGB 72 0.4856 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_XGB 80 0.1646 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_XGB 72 0.1646 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 3.180375099182129 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 5.516494274139404 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -58,16 +90,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.092921495437622 -INFO:pyaf.std:START_FORECASTING 'AirPassengers' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'AirPassengers' 0.265897274017334 - Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.0434 0.4224 -2 None _AirPassengers ... 0.0494 0.4770 -1 None Diff_AirPassengers ... 0.1135 1.1527 -3 None Diff_AirPassengers ... 0.0620 0.5942 - -[4 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.98460602760315 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.9495983123779297 Split Transformation ... TestMAPE TestMASE 0 None _AirPassengers ... 0.0434 0.4224 1 None Diff_AirPassengers ... 0.1135 1.1527 @@ -136,31 +171,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "XGB", + "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "XGB", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "56", - "MAE": "15.037108848492304", - "MAPE": "0.0388", - "MASE": "0.4059", - "RMSE": "20.538914416977175" + "Model_Performance": { + "COMPLEXITY": "56", + "MAE": "15.037108848492304", + "MAPE": "0.0388", + "MASE": "0.4059", + "RMSE": "20.538914416977175" + } } } diff --git a/tests/references/xgb_test_air_passengers_xgb_only_with_custom_options.log b/tests/references/xgb_test_air_passengers_xgb_only_with_custom_options.log index ad7efcee6..1fc62d0ab 100644 --- a/tests/references/xgb_test_air_passengers_xgb_only_with_custom_options.log +++ b/tests/references/xgb_test_air_passengers_xgb_only_with_custom_options.log @@ -1,4 +1,36 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(33) 32 0.1899 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_XGB(33) 24 0.1899 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(33) 64 0.055 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33) 56 0.0489 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(33) 48 0.0942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_XGB(33) 40 0.0942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(33) 48 0.1583 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_XGB(33) 40 0.1583 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(33) 64 0.0745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_XGB(33) 56 0.0745 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(33) 96 0.0991 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33) 88 0.1875 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 0.0631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_XGB(33) 72 0.0631 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 0.193 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_XGB(33) 72 0.1062 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(33) 64 1910118.8102 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_XGB(33) 56 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(33) 96 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33) 88 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_XGB(33) 72 1910118.6822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_XGB(33) 72 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(33) 64 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_XGB(33) 56 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(33) 96 0.0926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33) 88 0.1 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_XGB(33) 72 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(33) 80 0.2691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_XGB(33) 72 0.2691 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_XGB 32 0.1081 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_XGB 24 0.1081 INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_XGB 64 0.0479 @@ -31,7 +63,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integra INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_XGB 72 0.4856 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_XGB 80 0.1743 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_XGB 72 0.1743 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'AirPassengers' 3.1868789196014404 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.66603684425354 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -58,14 +90,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.112212896347046 -INFO:pyaf.std:START_FORECASTING 'AirPassengers' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'AirPassengers' 0.2517578601837158 - Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.0489 0.4746 -1 None _AirPassengers ... 0.0550 0.5315 - -[2 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.098273992538452 +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.8790526390075684 Split Transformation ... TestMAPE TestMASE 0 None _AirPassengers ... 0.0489 0.4746 1 None _AirPassengers ... 0.0550 0.5315 @@ -134,31 +171,33 @@ Forecasts { - "Dataset": { - "Signal": "AirPassengers", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1949.0", - "1959.91666666667" - ], - "TimeVariable": "time" + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "XGB", + "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" }, - "Training_Signal_Length": 132 - }, - "Model": { - "AR_Model": "XGB", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_XGB(33)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" - }, - "Model_Performance": { - "COMPLEXITY": "56", - "MAE": "17.249076053500175", - "MAPE": "0.045", - "MASE": "0.4656", - "RMSE": "23.29300793111707" + "Model_Performance": { + "COMPLEXITY": "56", + "MAE": "17.249076053500175", + "MAPE": "0.045", + "MASE": "0.4656", + "RMSE": "23.29300793111707" + } } } diff --git a/tests/references/xgb_test_ozone_xgb.log b/tests/references/xgb_test_ozone_xgb.log index eaf94d11e..a9389105a 100644 --- a/tests/references/xgb_test_ozone_xgb.log +++ b/tests/references/xgb_test_ozone_xgb.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.843384265899658 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 6.594133615493774 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -17,8 +17,8 @@ INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51 INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6135978624272591 L1_Forecast=0.5265316758013037 L1_Test=0.42992050508902385 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095700031756019 L2_Forecast=0.7315688649507152 L2_Test=0.551943269559555 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 INFO:pyaf.std:MODEL_COMPLEXITY 54 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -30,21 +30,31 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_START INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43496344136033754 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533328 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225498 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.1612820579538937 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046368 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481863 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102252 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045825 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947768 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.312664747238159 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.3324928283691406 +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.444857597351074 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.734642744064331 Split Transformation ... ForecastMAPE TestMAPE 0 None _Ozone ... 0.1595 0.1740 1 None _Ozone ... 0.1595 0.1740 @@ -94,31 +104,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "AR", - "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" - }, - "Model_Performance": { - "COMPLEXITY": "54", - "MAE": "0.5265316758013037", - "MAPE": "0.1595", - "MASE": "0.6782", - "RMSE": "0.7315688649507152" + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } } } diff --git a/tests/references/xgb_test_ozone_xgb_exogenous.log b/tests/references/xgb_test_ozone_xgb_exogenous.log index 5843d0532..08d8ad059 100644 --- a/tests/references/xgb_test_ozone_xgb_exogenous.log +++ b/tests/references/xgb_test_ozone_xgb_exogenous.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 1955 3 AS P_S 3.6 1955-03-01 3 1955-04 1955 4 AT P_U 5.0 1955-04-01 4 1955-05 1955 5 AU P_V 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.538028717041016 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 7.500288248062134 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -32,9 +32,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.306534051895142 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.31926894187927246 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.855579614639282 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.0136792659759521 Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', '_Ozone_ConstantTrend', '_Ozone_ConstantTrend_residue', '_Ozone_ConstantTrend_residue_zeroCycle', @@ -75,31 +85,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "XGB", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "38", - "MAE": "0.5146619482897067", - "MAPE": "0.1804", - "MASE": "0.663", - "RMSE": "0.6997810763047422" + "Model": { + "AR_Model": "XGB", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "38", + "MAE": "0.5146619482897067", + "MAPE": "0.1804", + "MASE": "0.663", + "RMSE": "0.6997810763047422" + } } } diff --git a/tests/references/xgb_test_ozone_xgb_only.log b/tests/references/xgb_test_ozone_xgb_only.log index 1ea333406..80d055d94 100644 --- a/tests/references/xgb_test_ozone_xgb_only.log +++ b/tests/references/xgb_test_ozone_xgb_only.log @@ -5,6 +5,54 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 42 0.433 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 46 0.2798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 38 0.3048 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 0.183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 78 0.2119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 70 0.2188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 58 0.2117 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 62 0.1939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 54 0.1939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 58 0.39 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 62 0.3659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 54 0.3659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 3.7028 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 78 2.781 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 70 0.9679 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.5787 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.5081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 102 0.5081 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 0.8198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.268 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 86 0.268 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 4.3395 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 4.841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 86 3.148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 47144203.9182 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 78 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 70 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 346920.1778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 110 65373973.0766 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 102 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 86 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 86 67040794.3863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 74 1.7346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_XGB(51) 78 1.7318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51) 70 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_XGB(51) 106 0.188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_XGB(51) 110 0.188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_XGB(51) 102 0.1844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 1.375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 0.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_XGB(51) 86 0.6548 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_XGB(51) 90 1.19 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_XGB(51) 94 1.2889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_XGB(51) 86 1.2889 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_XGB 42 0.2212 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_XGB 46 0.1794 INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_XGB 38 0.1804 @@ -53,7 +101,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lin INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_XGB 90 0.9191 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_XGB 94 0.983 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_XGB 86 0.983 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 4.777156591415405 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 8.14281439781189 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -80,16 +128,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.278090476989746 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.32746171951293945 - Split Transformation ... TestMAPE TestMASE -3 None _Ozone ... 0.3048 1.3193 -2 None _Ozone ... 0.2798 1.1873 -0 None _Ozone ... 0.3659 1.6405 -1 None _Ozone ... 0.3659 1.6405 - -[4 rows x 20 columns] +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 28.558805227279663 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.9671015739440918 Split Transformation ... TestMAPE TestMASE 0 None _Ozone ... 0.3659 1.6405 1 None _Ozone ... 0.3659 1.6405 @@ -139,31 +190,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "XGB", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "XGB", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_zeroCycle_residue_XGB(51)", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "38", - "MAE": "0.5146619482897067", - "MAPE": "0.1804", - "MASE": "0.663", - "RMSE": "0.6997810763047422" + "Model_Performance": { + "COMPLEXITY": "38", + "MAE": "0.5146619482897067", + "MAPE": "0.1804", + "MASE": "0.663", + "RMSE": "0.6997810763047422" + } } } diff --git a/tests/references/xgb_test_ozone_xgbx_exogenous.log b/tests/references/xgb_test_ozone_xgbx_exogenous.log index 6d30a80e0..cc3fd9642 100644 --- a/tests/references/xgb_test_ozone_xgbx_exogenous.log +++ b/tests/references/xgb_test_ozone_xgbx_exogenous.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 1955 3 AS P_S 3.6 1955-03-01 3 1955-04 1955 4 AT P_U 5.0 1955-04-01 4 1955-05 1955 5 AU P_V 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS 'Ozone' 15.926408767700195 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 19.922430276870728 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -33,9 +33,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.330235004425049 -INFO:pyaf.std:START_FORECASTING 'Ozone' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS 'Ozone' 0.4010927677154541 +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:generated new fontManager +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.14505624771118 +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.6064083576202393 Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', '_Ozone_ConstantTrend', '_Ozone_ConstantTrend_residue', '_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear', @@ -78,31 +88,33 @@ Forecasts { - "Dataset": { - "Signal": "Ozone", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "1955-01-01 00:00:00", - "1971-12-01 00:00:00" - ], - "TimeVariable": "Time" + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 }, - "Training_Signal_Length": 204 - }, - "Model": { - "AR_Model": "XGBX", - "Best_Decomposition": "_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGBX(51)", - "Cycle": "Seasonal_MonthOfYear", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "42", - "MAE": "0.5022033128982936", - "MAPE": "0.1732", - "MASE": "0.6469", - "RMSE": "0.6254168323818" + "Model": { + "AR_Model": "XGBX", + "Best_Decomposition": "_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_XGBX(51)", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "42", + "MAE": "0.5022033128982936", + "MAPE": "0.1732", + "MASE": "0.6469", + "RMSE": "0.6254168323818" + } } } From ea79f958ca2548fa8b4bf44225bc871b031c7d19 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 19:46:53 +0200 Subject: [PATCH 09/15] Add Missing Data Imputation Methods #146 Added these new logs --- .../missing_data_gen_air_passengers_tests.log | 0 .../missing_data_gen_ozone_tests.log | 0 ...ata_air_passengers_DiscardRow_Constant.log | 130 +++++++++++++++++ ...a_air_passengers_DiscardRow_DiscardRow.log | 130 +++++++++++++++++ ..._air_passengers_DiscardRow_Interpolate.log | 130 +++++++++++++++++ ...ng_data_air_passengers_DiscardRow_Mean.log | 130 +++++++++++++++++ ..._data_air_passengers_DiscardRow_Median.log | 130 +++++++++++++++++ ...ng_data_air_passengers_DiscardRow_None.log | 130 +++++++++++++++++ ...ir_passengers_DiscardRow_PreviousValue.log | 130 +++++++++++++++++ ...ta_air_passengers_Interpolate_Constant.log | 120 ++++++++++++++++ ..._air_passengers_Interpolate_DiscardRow.log | 130 +++++++++++++++++ ...air_passengers_Interpolate_Interpolate.log | 130 +++++++++++++++++ ...g_data_air_passengers_Interpolate_Mean.log | 130 +++++++++++++++++ ...data_air_passengers_Interpolate_Median.log | 130 +++++++++++++++++ ...g_data_air_passengers_Interpolate_None.log | 130 +++++++++++++++++ ...r_passengers_Interpolate_PreviousValue.log | 130 +++++++++++++++++ ...sing_data_air_passengers_None_Constant.log | 120 ++++++++++++++++ ...ng_data_air_passengers_None_DiscardRow.log | 130 +++++++++++++++++ ...g_data_air_passengers_None_Interpolate.log | 130 +++++++++++++++++ ..._missing_data_air_passengers_None_Mean.log | 130 +++++++++++++++++ ...issing_data_air_passengers_None_Median.log | 130 +++++++++++++++++ ..._missing_data_air_passengers_None_None.log | 130 +++++++++++++++++ ...data_air_passengers_None_PreviousValue.log | 130 +++++++++++++++++ ...st_missing_data_air_passengers_generic.log | 0 ...missing_data_ozone_DiscardRow_Constant.log | 124 ++++++++++++++++ ...ssing_data_ozone_DiscardRow_DiscardRow.log | 124 ++++++++++++++++ ...sing_data_ozone_DiscardRow_Interpolate.log | 124 ++++++++++++++++ ...est_missing_data_ozone_DiscardRow_Mean.log | 124 ++++++++++++++++ ...t_missing_data_ozone_DiscardRow_Median.log | 124 ++++++++++++++++ ...est_missing_data_ozone_DiscardRow_None.log | 124 ++++++++++++++++ ...ng_data_ozone_DiscardRow_PreviousValue.log | 124 ++++++++++++++++ ...issing_data_ozone_Interpolate_Constant.log | 123 ++++++++++++++++ ...sing_data_ozone_Interpolate_DiscardRow.log | 124 ++++++++++++++++ ...ing_data_ozone_Interpolate_Interpolate.log | 123 ++++++++++++++++ ...st_missing_data_ozone_Interpolate_Mean.log | 123 ++++++++++++++++ ..._missing_data_ozone_Interpolate_Median.log | 123 ++++++++++++++++ ...st_missing_data_ozone_Interpolate_None.log | 133 ++++++++++++++++++ ...g_data_ozone_Interpolate_PreviousValue.log | 123 ++++++++++++++++ ..._test_missing_data_ozone_None_Constant.log | 123 ++++++++++++++++ ...est_missing_data_ozone_None_DiscardRow.log | 124 ++++++++++++++++ ...st_missing_data_ozone_None_Interpolate.log | 123 ++++++++++++++++ ...data_test_missing_data_ozone_None_Mean.log | 123 ++++++++++++++++ ...ta_test_missing_data_ozone_None_Median.log | 123 ++++++++++++++++ ...data_test_missing_data_ozone_None_None.log | 133 ++++++++++++++++++ ..._missing_data_ozone_None_PreviousValue.log | 123 ++++++++++++++++ ...g_data_test_missing_data_ozone_generic.log | 0 46 files changed, 5322 insertions(+) create mode 100644 tests/references/missing_data_gen_air_passengers_tests.log create mode 100644 tests/references/missing_data_gen_ozone_tests.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Constant.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Mean.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Median.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_None.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_PreviousValue.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Constant.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Median.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_Interpolate_None.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_Interpolate_PreviousValue.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_None_Constant.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_None_DiscardRow.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_None_Interpolate.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_None_Mean.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_None_Median.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_None_None.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_None_PreviousValue.log create mode 100644 tests/references/missing_data_test_missing_data_air_passengers_generic.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_DiscardRow_Constant.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_DiscardRow_Mean.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_DiscardRow_Median.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_DiscardRow_None.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_DiscardRow_PreviousValue.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_Interpolate_Constant.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_Interpolate_Mean.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_Interpolate_Median.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_Interpolate_None.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_Interpolate_PreviousValue.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_None_Constant.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_None_DiscardRow.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_None_Interpolate.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_None_Mean.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_None_Median.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_None_None.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_None_PreviousValue.log create mode 100644 tests/references/missing_data_test_missing_data_ozone_generic.log diff --git a/tests/references/missing_data_gen_air_passengers_tests.log b/tests/references/missing_data_gen_air_passengers_tests.log new file mode 100644 index 000000000..e69de29bb diff --git a/tests/references/missing_data_gen_ozone_tests.log b/tests/references/missing_data_gen_ozone_tests.log new file mode 100644 index 000000000..e69de29bb diff --git a/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Constant.log b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Constant.log new file mode 100644 index 000000000..5a6e88c7c --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Constant.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.6610052585601807 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.8333333333399 TimeDelta=0.10585585585594394 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=106 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0725 MAPE_Forecast=0.0985 MAPE_Test=0.0644 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0714 SMAPE_Forecast=0.0939 SMAPE_Test=0.0612 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6766 MASE_Forecast=0.889 MASE_Test=0.5956 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.893862659174927 L1_Forecast=36.400686663473685 L1_Test=26.801344443932322 +INFO:pyaf.std:MODEL_L2 L2_Fit=20.281189554681816 L2_Forecast=42.57467195381298 L2_Test=36.480900936629716 +INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 218.78666666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7348386019946807 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag17 0.4111158779983695 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag9 0.24273145738869362 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.22983622781493696 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20011565039535642 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19469398401798493 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.1896760323123999 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.16021791921201803 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 0.14987076539906835 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.12053912174931067 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.4656517505645752 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0934 0.1118 +1 None _AirPassengers ... 0.0941 0.0854 +2 None _AirPassengers ... 0.0941 0.0854 +3 None _AirPassengers ... 0.0981 0.1059 +4 None _AirPassengers ... 0.0981 0.1059 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 118 entries, 0 to 117 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 118 non-null float64 + 1 AirPassengers 106 non-null float64 + 2 AirPassengers_Forecast 118 non-null float64 +dtypes: float64(3) +memory usage: 2.9 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +106 1960.022523 NaN 490.664642 +107 1960.128378 NaN 549.379377 +108 1960.234234 NaN 536.200441 +109 1960.340090 NaN 538.835327 +110 1960.445946 NaN 550.462003 +111 1960.551802 NaN 602.622773 +112 1960.657658 NaN 582.759496 +113 1960.763514 NaN 537.032508 +114 1960.869369 NaN 473.003495 +115 1960.975225 NaN 481.941071 +116 1961.081081 NaN 540.369458 +117 1961.186937 NaN 620.931119 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 106 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "18", + "MAE": "36.400686663473685", + "MAPE": "0.0985", + "MASE": "0.889", + "RMSE": "42.57467195381298" + } + } +} + + + + + + +{"time":{"94":1959.0,"95":1959.0833333333,"96":1959.1666666667,"97":1959.25,"98":1959.3333333333,"99":1959.4166666667,"100":1959.5,"101":1959.5833333333,"102":1959.6666666667,"103":1959.75,"104":1959.8333333333,"105":1959.9166666667,"106":1960.0225225225,"107":1960.1283783784,"108":1960.2342342342,"109":1960.3400900901,"110":1960.4459459459,"111":1960.5518018018,"112":1960.6576576577,"113":1960.7635135135,"114":1960.8693693694,"115":1960.9752252252,"116":1961.0810810811,"117":1961.1869369369},"AirPassengers":{"94":360.0,"95":342.0,"96":406.0,"97":396.0,"98":420.0,"99":472.0,"100":548.0,"101":559.0,"102":463.0,"103":407.0,"104":362.0,"105":405.0,"106":null,"107":null,"108":null,"109":null,"110":null,"111":null,"112":null,"113":null,"114":null,"115":null,"116":null,"117":null},"AirPassengers_Forecast":{"94":362.6968077846,"95":392.6557489774,"96":409.3600997898,"97":484.9245689056,"98":447.5324241882,"99":473.9529793566,"100":508.7732196588,"101":534.9466890762,"102":505.0515831028,"103":405.6850563804,"104":386.1081910395,"105":420.738695298,"106":490.6646418646,"107":549.3793774842,"108":536.2004409066,"109":538.8353265618,"110":550.4620031346,"111":602.6227733166,"112":582.7594956409,"113":537.0325077925,"114":473.0034946541,"115":481.9410713016,"116":540.3694580531,"117":620.9311191539}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log new file mode 100644 index 000000000..05f249950 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_DiscardRow.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.388167381286621 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.8333333333399 TimeDelta=0.10585585585594394 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=106 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0725 MAPE_Forecast=0.0985 MAPE_Test=0.0644 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0714 SMAPE_Forecast=0.0939 SMAPE_Test=0.0612 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6766 MASE_Forecast=0.889 MASE_Test=0.5956 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.893862659174927 L1_Forecast=36.400686663473685 L1_Test=26.801344443932322 +INFO:pyaf.std:MODEL_L2 L2_Fit=20.281189554681816 L2_Forecast=42.57467195381298 L2_Test=36.480900936629716 +INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 218.78666666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7348386019946807 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag17 0.4111158779983695 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag9 0.24273145738869362 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.22983622781493696 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20011565039535642 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19469398401798493 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.1896760323123999 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.16021791921201803 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 0.14987076539906835 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.12053912174931067 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.44115304946899414 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0934 0.1118 +1 None _AirPassengers ... 0.0941 0.0854 +2 None _AirPassengers ... 0.0941 0.0854 +3 None _AirPassengers ... 0.0981 0.1059 +4 None _AirPassengers ... 0.0981 0.1059 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 118 entries, 0 to 117 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 118 non-null float64 + 1 AirPassengers 106 non-null float64 + 2 AirPassengers_Forecast 118 non-null float64 +dtypes: float64(3) +memory usage: 2.9 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +106 1960.022523 NaN 490.664642 +107 1960.128378 NaN 549.379377 +108 1960.234234 NaN 536.200441 +109 1960.340090 NaN 538.835327 +110 1960.445946 NaN 550.462003 +111 1960.551802 NaN 602.622773 +112 1960.657658 NaN 582.759496 +113 1960.763514 NaN 537.032508 +114 1960.869369 NaN 473.003495 +115 1960.975225 NaN 481.941071 +116 1961.081081 NaN 540.369458 +117 1961.186937 NaN 620.931119 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 106 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "18", + "MAE": "36.400686663473685", + "MAPE": "0.0985", + "MASE": "0.889", + "RMSE": "42.57467195381298" + } + } +} + + + + + + +{"time":{"94":1959.0,"95":1959.0833333333,"96":1959.1666666667,"97":1959.25,"98":1959.3333333333,"99":1959.4166666667,"100":1959.5,"101":1959.5833333333,"102":1959.6666666667,"103":1959.75,"104":1959.8333333333,"105":1959.9166666667,"106":1960.0225225225,"107":1960.1283783784,"108":1960.2342342342,"109":1960.3400900901,"110":1960.4459459459,"111":1960.5518018018,"112":1960.6576576577,"113":1960.7635135135,"114":1960.8693693694,"115":1960.9752252252,"116":1961.0810810811,"117":1961.1869369369},"AirPassengers":{"94":360.0,"95":342.0,"96":406.0,"97":396.0,"98":420.0,"99":472.0,"100":548.0,"101":559.0,"102":463.0,"103":407.0,"104":362.0,"105":405.0,"106":null,"107":null,"108":null,"109":null,"110":null,"111":null,"112":null,"113":null,"114":null,"115":null,"116":null,"117":null},"AirPassengers_Forecast":{"94":362.6968077846,"95":392.6557489774,"96":409.3600997898,"97":484.9245689056,"98":447.5324241882,"99":473.9529793566,"100":508.7732196588,"101":534.9466890762,"102":505.0515831028,"103":405.6850563804,"104":386.1081910395,"105":420.738695298,"106":490.6646418646,"107":549.3793774842,"108":536.2004409066,"109":538.8353265618,"110":550.4620031346,"111":602.6227733166,"112":582.7594956409,"113":537.0325077925,"114":473.0034946541,"115":481.9410713016,"116":540.3694580531,"117":620.9311191539}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log new file mode 100644 index 000000000..6c31b3a58 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Interpolate.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.093492269515991 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.8333333333399 TimeDelta=0.10585585585594394 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=106 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0725 MAPE_Forecast=0.0985 MAPE_Test=0.0644 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0714 SMAPE_Forecast=0.0939 SMAPE_Test=0.0612 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6766 MASE_Forecast=0.889 MASE_Test=0.5956 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.893862659174927 L1_Forecast=36.400686663473685 L1_Test=26.801344443932322 +INFO:pyaf.std:MODEL_L2 L2_Fit=20.281189554681816 L2_Forecast=42.57467195381298 L2_Test=36.480900936629716 +INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 218.78666666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7348386019946807 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag17 0.4111158779983695 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag9 0.24273145738869362 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.22983622781493696 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20011565039535642 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19469398401798493 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.1896760323123999 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.16021791921201803 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 0.14987076539906835 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.12053912174931067 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.6478183269500732 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0934 0.1118 +1 None _AirPassengers ... 0.0941 0.0854 +2 None _AirPassengers ... 0.0941 0.0854 +3 None _AirPassengers ... 0.0981 0.1059 +4 None _AirPassengers ... 0.0981 0.1059 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 118 entries, 0 to 117 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 118 non-null float64 + 1 AirPassengers 106 non-null float64 + 2 AirPassengers_Forecast 118 non-null float64 +dtypes: float64(3) +memory usage: 2.9 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +106 1960.022523 NaN 490.664642 +107 1960.128378 NaN 549.379377 +108 1960.234234 NaN 536.200441 +109 1960.340090 NaN 538.835327 +110 1960.445946 NaN 550.462003 +111 1960.551802 NaN 602.622773 +112 1960.657658 NaN 582.759496 +113 1960.763514 NaN 537.032508 +114 1960.869369 NaN 473.003495 +115 1960.975225 NaN 481.941071 +116 1961.081081 NaN 540.369458 +117 1961.186937 NaN 620.931119 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 106 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "18", + "MAE": "36.400686663473685", + "MAPE": "0.0985", + "MASE": "0.889", + "RMSE": "42.57467195381298" + } + } +} + + + + + + +{"time":{"94":1959.0,"95":1959.0833333333,"96":1959.1666666667,"97":1959.25,"98":1959.3333333333,"99":1959.4166666667,"100":1959.5,"101":1959.5833333333,"102":1959.6666666667,"103":1959.75,"104":1959.8333333333,"105":1959.9166666667,"106":1960.0225225225,"107":1960.1283783784,"108":1960.2342342342,"109":1960.3400900901,"110":1960.4459459459,"111":1960.5518018018,"112":1960.6576576577,"113":1960.7635135135,"114":1960.8693693694,"115":1960.9752252252,"116":1961.0810810811,"117":1961.1869369369},"AirPassengers":{"94":360.0,"95":342.0,"96":406.0,"97":396.0,"98":420.0,"99":472.0,"100":548.0,"101":559.0,"102":463.0,"103":407.0,"104":362.0,"105":405.0,"106":null,"107":null,"108":null,"109":null,"110":null,"111":null,"112":null,"113":null,"114":null,"115":null,"116":null,"117":null},"AirPassengers_Forecast":{"94":362.6968077846,"95":392.6557489774,"96":409.3600997898,"97":484.9245689056,"98":447.5324241882,"99":473.9529793566,"100":508.7732196588,"101":534.9466890762,"102":505.0515831028,"103":405.6850563804,"104":386.1081910395,"105":420.738695298,"106":490.6646418646,"107":549.3793774842,"108":536.2004409066,"109":538.8353265618,"110":550.4620031346,"111":602.6227733166,"112":582.7594956409,"113":537.0325077925,"114":473.0034946541,"115":481.9410713016,"116":540.3694580531,"117":620.9311191539}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Mean.log b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Mean.log new file mode 100644 index 000000000..d85b9b576 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Mean.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.472761392593384 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.8333333333399 TimeDelta=0.10585585585594394 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=106 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0725 MAPE_Forecast=0.0985 MAPE_Test=0.0644 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0714 SMAPE_Forecast=0.0939 SMAPE_Test=0.0612 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6766 MASE_Forecast=0.889 MASE_Test=0.5956 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.893862659174927 L1_Forecast=36.400686663473685 L1_Test=26.801344443932322 +INFO:pyaf.std:MODEL_L2 L2_Fit=20.281189554681816 L2_Forecast=42.57467195381298 L2_Test=36.480900936629716 +INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 218.78666666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7348386019946807 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag17 0.4111158779983695 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag9 0.24273145738869362 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.22983622781493696 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20011565039535642 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19469398401798493 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.1896760323123999 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.16021791921201803 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 0.14987076539906835 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.12053912174931067 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.5912940502166748 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0934 0.1118 +1 None _AirPassengers ... 0.0941 0.0854 +2 None _AirPassengers ... 0.0941 0.0854 +3 None _AirPassengers ... 0.0981 0.1059 +4 None _AirPassengers ... 0.0981 0.1059 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 118 entries, 0 to 117 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 118 non-null float64 + 1 AirPassengers 106 non-null float64 + 2 AirPassengers_Forecast 118 non-null float64 +dtypes: float64(3) +memory usage: 2.9 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +106 1960.022523 NaN 490.664642 +107 1960.128378 NaN 549.379377 +108 1960.234234 NaN 536.200441 +109 1960.340090 NaN 538.835327 +110 1960.445946 NaN 550.462003 +111 1960.551802 NaN 602.622773 +112 1960.657658 NaN 582.759496 +113 1960.763514 NaN 537.032508 +114 1960.869369 NaN 473.003495 +115 1960.975225 NaN 481.941071 +116 1961.081081 NaN 540.369458 +117 1961.186937 NaN 620.931119 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 106 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "18", + "MAE": "36.400686663473685", + "MAPE": "0.0985", + "MASE": "0.889", + "RMSE": "42.57467195381298" + } + } +} + + + + + + +{"time":{"94":1959.0,"95":1959.0833333333,"96":1959.1666666667,"97":1959.25,"98":1959.3333333333,"99":1959.4166666667,"100":1959.5,"101":1959.5833333333,"102":1959.6666666667,"103":1959.75,"104":1959.8333333333,"105":1959.9166666667,"106":1960.0225225225,"107":1960.1283783784,"108":1960.2342342342,"109":1960.3400900901,"110":1960.4459459459,"111":1960.5518018018,"112":1960.6576576577,"113":1960.7635135135,"114":1960.8693693694,"115":1960.9752252252,"116":1961.0810810811,"117":1961.1869369369},"AirPassengers":{"94":360.0,"95":342.0,"96":406.0,"97":396.0,"98":420.0,"99":472.0,"100":548.0,"101":559.0,"102":463.0,"103":407.0,"104":362.0,"105":405.0,"106":null,"107":null,"108":null,"109":null,"110":null,"111":null,"112":null,"113":null,"114":null,"115":null,"116":null,"117":null},"AirPassengers_Forecast":{"94":362.6968077846,"95":392.6557489774,"96":409.3600997898,"97":484.9245689056,"98":447.5324241882,"99":473.9529793566,"100":508.7732196588,"101":534.9466890762,"102":505.0515831028,"103":405.6850563804,"104":386.1081910395,"105":420.738695298,"106":490.6646418646,"107":549.3793774842,"108":536.2004409066,"109":538.8353265618,"110":550.4620031346,"111":602.6227733166,"112":582.7594956409,"113":537.0325077925,"114":473.0034946541,"115":481.9410713016,"116":540.3694580531,"117":620.9311191539}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Median.log b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Median.log new file mode 100644 index 000000000..d5f70412b --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_Median.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.040433883666992 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.8333333333399 TimeDelta=0.10585585585594394 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=106 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0725 MAPE_Forecast=0.0985 MAPE_Test=0.0644 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0714 SMAPE_Forecast=0.0939 SMAPE_Test=0.0612 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6766 MASE_Forecast=0.889 MASE_Test=0.5956 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.893862659174927 L1_Forecast=36.400686663473685 L1_Test=26.801344443932322 +INFO:pyaf.std:MODEL_L2 L2_Fit=20.281189554681816 L2_Forecast=42.57467195381298 L2_Test=36.480900936629716 +INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 218.78666666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7348386019946807 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag17 0.4111158779983695 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag9 0.24273145738869362 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.22983622781493696 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20011565039535642 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19469398401798493 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.1896760323123999 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.16021791921201803 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 0.14987076539906835 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.12053912174931067 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.8704869747161865 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0934 0.1118 +1 None _AirPassengers ... 0.0941 0.0854 +2 None _AirPassengers ... 0.0941 0.0854 +3 None _AirPassengers ... 0.0981 0.1059 +4 None _AirPassengers ... 0.0981 0.1059 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 118 entries, 0 to 117 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 118 non-null float64 + 1 AirPassengers 106 non-null float64 + 2 AirPassengers_Forecast 118 non-null float64 +dtypes: float64(3) +memory usage: 2.9 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +106 1960.022523 NaN 490.664642 +107 1960.128378 NaN 549.379377 +108 1960.234234 NaN 536.200441 +109 1960.340090 NaN 538.835327 +110 1960.445946 NaN 550.462003 +111 1960.551802 NaN 602.622773 +112 1960.657658 NaN 582.759496 +113 1960.763514 NaN 537.032508 +114 1960.869369 NaN 473.003495 +115 1960.975225 NaN 481.941071 +116 1961.081081 NaN 540.369458 +117 1961.186937 NaN 620.931119 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 106 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "18", + "MAE": "36.400686663473685", + "MAPE": "0.0985", + "MASE": "0.889", + "RMSE": "42.57467195381298" + } + } +} + + + + + + +{"time":{"94":1959.0,"95":1959.0833333333,"96":1959.1666666667,"97":1959.25,"98":1959.3333333333,"99":1959.4166666667,"100":1959.5,"101":1959.5833333333,"102":1959.6666666667,"103":1959.75,"104":1959.8333333333,"105":1959.9166666667,"106":1960.0225225225,"107":1960.1283783784,"108":1960.2342342342,"109":1960.3400900901,"110":1960.4459459459,"111":1960.5518018018,"112":1960.6576576577,"113":1960.7635135135,"114":1960.8693693694,"115":1960.9752252252,"116":1961.0810810811,"117":1961.1869369369},"AirPassengers":{"94":360.0,"95":342.0,"96":406.0,"97":396.0,"98":420.0,"99":472.0,"100":548.0,"101":559.0,"102":463.0,"103":407.0,"104":362.0,"105":405.0,"106":null,"107":null,"108":null,"109":null,"110":null,"111":null,"112":null,"113":null,"114":null,"115":null,"116":null,"117":null},"AirPassengers_Forecast":{"94":362.6968077846,"95":392.6557489774,"96":409.3600997898,"97":484.9245689056,"98":447.5324241882,"99":473.9529793566,"100":508.7732196588,"101":534.9466890762,"102":505.0515831028,"103":405.6850563804,"104":386.1081910395,"105":420.738695298,"106":490.6646418646,"107":549.3793774842,"108":536.2004409066,"109":538.8353265618,"110":550.4620031346,"111":602.6227733166,"112":582.7594956409,"113":537.0325077925,"114":473.0034946541,"115":481.9410713016,"116":540.3694580531,"117":620.9311191539}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_None.log b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_None.log new file mode 100644 index 000000000..8e8a4bbbf --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_None.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.6823372840881348 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.8333333333399 TimeDelta=0.10585585585594394 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=106 Min=112 Max=559 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112 Max=559 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0725 MAPE_Forecast=0.0985 MAPE_Test=0.0644 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0714 SMAPE_Forecast=0.0939 SMAPE_Test=0.0612 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6766 MASE_Forecast=0.889 MASE_Test=0.5956 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.893862659174927 L1_Forecast=36.400686663473685 L1_Test=26.801344443932322 +INFO:pyaf.std:MODEL_L2 L2_Fit=20.281189554681816 L2_Forecast=42.57467195381298 L2_Test=36.480900936629716 +INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 218.78666666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7348386019946807 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag17 0.4111158779983695 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag9 0.24273145738869362 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.22983622781493696 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20011565039535642 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19469398401798493 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.1896760323123999 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.16021791921201803 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 0.14987076539906835 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.12053912174931067 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.5938940048217773 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0934 0.1118 +1 None _AirPassengers ... 0.0941 0.0854 +2 None _AirPassengers ... 0.0941 0.0854 +3 None _AirPassengers ... 0.0981 0.1059 +4 None _AirPassengers ... 0.0981 0.1059 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 118 entries, 0 to 117 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 118 non-null float64 + 1 AirPassengers 106 non-null float64 + 2 AirPassengers_Forecast 118 non-null float64 +dtypes: float64(3) +memory usage: 2.9 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +106 1960.022523 NaN 490.664642 +107 1960.128378 NaN 549.379377 +108 1960.234234 NaN 536.200441 +109 1960.340090 NaN 538.835327 +110 1960.445946 NaN 550.462003 +111 1960.551802 NaN 602.622773 +112 1960.657658 NaN 582.759496 +113 1960.763514 NaN 537.032508 +114 1960.869369 NaN 473.003495 +115 1960.975225 NaN 481.941071 +116 1961.081081 NaN 540.369458 +117 1961.186937 NaN 620.931119 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 106 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "18", + "MAE": "36.400686663473685", + "MAPE": "0.0985", + "MASE": "0.889", + "RMSE": "42.57467195381298" + } + } +} + + + + + + +{"time":{"94":1959.0,"95":1959.0833333333,"96":1959.1666666667,"97":1959.25,"98":1959.3333333333,"99":1959.4166666667,"100":1959.5,"101":1959.5833333333,"102":1959.6666666667,"103":1959.75,"104":1959.8333333333,"105":1959.9166666667,"106":1960.0225225225,"107":1960.1283783784,"108":1960.2342342342,"109":1960.3400900901,"110":1960.4459459459,"111":1960.5518018018,"112":1960.6576576577,"113":1960.7635135135,"114":1960.8693693694,"115":1960.9752252252,"116":1961.0810810811,"117":1961.1869369369},"AirPassengers":{"94":360.0,"95":342.0,"96":406.0,"97":396.0,"98":420.0,"99":472.0,"100":548.0,"101":559.0,"102":463.0,"103":407.0,"104":362.0,"105":405.0,"106":null,"107":null,"108":null,"109":null,"110":null,"111":null,"112":null,"113":null,"114":null,"115":null,"116":null,"117":null},"AirPassengers_Forecast":{"94":362.6968077846,"95":392.6557489774,"96":409.3600997898,"97":484.9245689056,"98":447.5324241882,"99":473.9529793566,"100":508.7732196588,"101":534.9466890762,"102":505.0515831028,"103":405.6850563804,"104":386.1081910395,"105":420.738695298,"106":490.6646418646,"107":549.3793774842,"108":536.2004409066,"109":538.8353265618,"110":550.4620031346,"111":602.6227733166,"112":582.7594956409,"113":537.0325077925,"114":473.0034946541,"115":481.9410713016,"116":540.3694580531,"117":620.9311191539}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_PreviousValue.log b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_PreviousValue.log new file mode 100644 index 000000000..d35761d6c --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_DiscardRow_PreviousValue.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.456226348876953 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.8333333333399 TimeDelta=0.10585585585594394 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=106 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0725 MAPE_Forecast=0.0985 MAPE_Test=0.0644 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0714 SMAPE_Forecast=0.0939 SMAPE_Test=0.0612 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6766 MASE_Forecast=0.889 MASE_Test=0.5956 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.893862659174927 L1_Forecast=36.400686663473685 L1_Test=26.801344443932322 +INFO:pyaf.std:MODEL_L2 L2_Fit=20.281189554681816 L2_Forecast=42.57467195381298 L2_Test=36.480900936629716 +INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 218.78666666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7348386019946807 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag17 0.4111158779983695 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag9 0.24273145738869362 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.22983622781493696 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20011565039535642 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19469398401798493 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.1896760323123999 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.16021791921201803 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 0.14987076539906835 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.12053912174931067 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.5666334629058838 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0934 0.1118 +1 None _AirPassengers ... 0.0941 0.0854 +2 None _AirPassengers ... 0.0941 0.0854 +3 None _AirPassengers ... 0.0981 0.1059 +4 None _AirPassengers ... 0.0981 0.1059 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 118 entries, 0 to 117 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 118 non-null float64 + 1 AirPassengers 106 non-null float64 + 2 AirPassengers_Forecast 118 non-null float64 +dtypes: float64(3) +memory usage: 2.9 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +106 1960.022523 NaN 490.664642 +107 1960.128378 NaN 549.379377 +108 1960.234234 NaN 536.200441 +109 1960.340090 NaN 538.835327 +110 1960.445946 NaN 550.462003 +111 1960.551802 NaN 602.622773 +112 1960.657658 NaN 582.759496 +113 1960.763514 NaN 537.032508 +114 1960.869369 NaN 473.003495 +115 1960.975225 NaN 481.941071 +116 1961.081081 NaN 540.369458 +117 1961.186937 NaN 620.931119 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 106 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "18", + "MAE": "36.400686663473685", + "MAPE": "0.0985", + "MASE": "0.889", + "RMSE": "42.57467195381298" + } + } +} + + + + + + +{"time":{"94":1959.0,"95":1959.0833333333,"96":1959.1666666667,"97":1959.25,"98":1959.3333333333,"99":1959.4166666667,"100":1959.5,"101":1959.5833333333,"102":1959.6666666667,"103":1959.75,"104":1959.8333333333,"105":1959.9166666667,"106":1960.0225225225,"107":1960.1283783784,"108":1960.2342342342,"109":1960.3400900901,"110":1960.4459459459,"111":1960.5518018018,"112":1960.6576576577,"113":1960.7635135135,"114":1960.8693693694,"115":1960.9752252252,"116":1961.0810810811,"117":1961.1869369369},"AirPassengers":{"94":360.0,"95":342.0,"96":406.0,"97":396.0,"98":420.0,"99":472.0,"100":548.0,"101":559.0,"102":463.0,"103":407.0,"104":362.0,"105":405.0,"106":null,"107":null,"108":null,"109":null,"110":null,"111":null,"112":null,"113":null,"114":null,"115":null,"116":null,"117":null},"AirPassengers_Forecast":{"94":362.6968077846,"95":392.6557489774,"96":409.3600997898,"97":484.9245689056,"98":447.5324241882,"99":473.9529793566,"100":508.7732196588,"101":534.9466890762,"102":505.0515831028,"103":405.6850563804,"104":386.1081910395,"105":420.738695298,"106":490.6646418646,"107":549.3793774842,"108":536.2004409066,"109":538.8353265618,"110":550.4620031346,"111":602.6227733166,"112":582.7594956409,"113":537.0325077925,"114":473.0034946541,"115":481.9410713016,"116":540.3694580531,"117":620.9311191539}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Constant.log b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Constant.log new file mode 100644 index 000000000..d22e0a13f --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Constant.log @@ -0,0 +1,120 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.6320266723632812 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=0.0 Max=559.0 Mean=217.7651515151515 StdDev=145.19849839822123 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_AirPassengers' Min=112.0 Max=28745.0 Mean=10835.780303030304 StdDev=8180.063378944654 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' +INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] +INFO:pyaf.std:TREND_DETAIL 'CumSum_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.3744 MAPE_Forecast=0.7917 MAPE_Test=1.0 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.5618 SMAPE_Forecast=1.5833 SMAPE_Test=2.0 +INFO:pyaf.std:MODEL_MASE MASE_Fit=3.496 MASE_Forecast=1.7806 MASE_Test=9.5185 +INFO:pyaf.std:MODEL_L1 L1_Fit=237.36208767361111 L1_Forecast=299.8333333333333 L1_Test=428.3333333333333 +INFO:pyaf.std:MODEL_L2 L2_Fit=692.9687888724792 L2_Forecast=341.0758713248417 L2_Test=433.5197035122318 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6601.760416666667 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.5044364929199219 + Split Transformation ... ForecastMAPE TestMAPE +0 None CumSum_AirPassengers ... 7.917000e-01 1.0000 +1 None CumSum_AirPassengers ... 7.917000e-01 1.0000 +2 None Diff_AirPassengers ... 2.916667e+10 1.0845 +3 None Diff_AirPassengers ... 2.916667e+10 1.0845 +4 None Diff_AirPassengers ... 1.698962e+11 1.0369 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + 'CumSum_AirPassengers', 'CumSum_AirPassengers_ConstantTrend', + 'CumSum_AirPassengers_ConstantTrend_residue', + 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle', + 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR', + 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', + 'CumSum_AirPassengers_Trend', 'CumSum_AirPassengers_Trend_residue', + 'CumSum_AirPassengers_Cycle', 'CumSum_AirPassengers_Cycle_residue', + 'CumSum_AirPassengers_AR', 'CumSum_AirPassengers_AR_residue', + 'CumSum_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + 'CumSum_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 0.0 +133 1960.083333 NaN 0.0 +134 1960.166667 NaN 0.0 +135 1960.250000 NaN 0.0 +136 1960.333333 NaN 0.0 +137 1960.416667 NaN 0.0 +138 1960.500000 NaN 0.0 +139 1960.583333 NaN 0.0 +140 1960.666667 NaN 0.0 +141 1960.750000 NaN 0.0 +142 1960.833333 NaN 0.0 +143 1960.916667 NaN 0.0 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "Integration", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "299.8333333333333", + "MAPE": "0.7917", + "MASE": "1.7806", + "RMSE": "341.0758713248417" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":0.0,"121":0.0,"122":0.0,"123":0.0,"124":0.0,"125":0.0,"126":0.0,"127":0.0,"128":0.0,"129":0.0,"130":0.0,"131":0.0,"132":0.0,"133":0.0,"134":0.0,"135":0.0,"136":0.0,"137":0.0,"138":0.0,"139":0.0,"140":0.0,"141":0.0,"142":0.0,"143":0.0}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log new file mode 100644 index 000000000..0f41b4c1b --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_DiscardRow.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.472024440765381 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.8333333333399 TimeDelta=0.10585585585594394 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=106 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0725 MAPE_Forecast=0.0985 MAPE_Test=0.0644 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0714 SMAPE_Forecast=0.0939 SMAPE_Test=0.0612 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6766 MASE_Forecast=0.889 MASE_Test=0.5956 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.893862659174927 L1_Forecast=36.400686663473685 L1_Test=26.801344443932322 +INFO:pyaf.std:MODEL_L2 L2_Fit=20.281189554681816 L2_Forecast=42.57467195381298 L2_Test=36.480900936629716 +INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 218.78666666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7348386019946807 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag17 0.4111158779983695 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag9 0.24273145738869362 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.22983622781493696 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20011565039535642 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19469398401798493 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.1896760323123999 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.16021791921201803 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 0.14987076539906835 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.12053912174931067 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.7366759777069092 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0934 0.1118 +1 None _AirPassengers ... 0.0941 0.0854 +2 None _AirPassengers ... 0.0941 0.0854 +3 None _AirPassengers ... 0.0981 0.1059 +4 None _AirPassengers ... 0.0981 0.1059 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 118 entries, 0 to 117 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 118 non-null float64 + 1 AirPassengers 106 non-null float64 + 2 AirPassengers_Forecast 118 non-null float64 +dtypes: float64(3) +memory usage: 2.9 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +106 1960.022523 NaN 490.664642 +107 1960.128378 NaN 549.379377 +108 1960.234234 NaN 536.200441 +109 1960.340090 NaN 538.835327 +110 1960.445946 NaN 550.462003 +111 1960.551802 NaN 602.622773 +112 1960.657658 NaN 582.759496 +113 1960.763514 NaN 537.032508 +114 1960.869369 NaN 473.003495 +115 1960.975225 NaN 481.941071 +116 1961.081081 NaN 540.369458 +117 1961.186937 NaN 620.931119 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 106 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "18", + "MAE": "36.400686663473685", + "MAPE": "0.0985", + "MASE": "0.889", + "RMSE": "42.57467195381298" + } + } +} + + + + + + +{"time":{"94":1959.0,"95":1959.0833333333,"96":1959.1666666667,"97":1959.25,"98":1959.3333333333,"99":1959.4166666667,"100":1959.5,"101":1959.5833333333,"102":1959.6666666667,"103":1959.75,"104":1959.8333333333,"105":1959.9166666667,"106":1960.0225225225,"107":1960.1283783784,"108":1960.2342342342,"109":1960.3400900901,"110":1960.4459459459,"111":1960.5518018018,"112":1960.6576576577,"113":1960.7635135135,"114":1960.8693693694,"115":1960.9752252252,"116":1961.0810810811,"117":1961.1869369369},"AirPassengers":{"94":360.0,"95":342.0,"96":406.0,"97":396.0,"98":420.0,"99":472.0,"100":548.0,"101":559.0,"102":463.0,"103":407.0,"104":362.0,"105":405.0,"106":null,"107":null,"108":null,"109":null,"110":null,"111":null,"112":null,"113":null,"114":null,"115":null,"116":null,"117":null},"AirPassengers_Forecast":{"94":362.6968077846,"95":392.6557489774,"96":409.3600997898,"97":484.9245689056,"98":447.5324241882,"99":473.9529793566,"100":508.7732196588,"101":534.9466890762,"102":505.0515831028,"103":405.6850563804,"104":386.1081910395,"105":420.738695298,"106":490.6646418646,"107":549.3793774842,"108":536.2004409066,"109":538.8353265618,"110":550.4620031346,"111":602.6227733166,"112":582.7594956409,"113":537.0325077925,"114":473.0034946541,"115":481.9410713016,"116":540.3694580531,"117":620.9311191539}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log new file mode 100644 index 000000000..c85e2379c --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Interpolate.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.287615537643433 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=263.29924242424244 StdDev=104.85841221084239 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=263.29924242424244 StdDev=104.85841221084239 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0429 MAPE_Forecast=0.046 MAPE_Test=0.0397 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0428 SMAPE_Forecast=0.0453 SMAPE_Test=0.0392 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4728 MASE_Forecast=0.518 MASE_Test=0.3721 +INFO:pyaf.std:MODEL_L1 L1_Fit=8.217597173732674 L1_Forecast=17.161315479641637 L1_Test=16.744946957777486 +INFO:pyaf.std:MODEL_L2 L2_Fit=10.322965678348632 L2_Forecast=21.059550122364946 L2_Test=21.05332492372224 +INFO:pyaf.std:MODEL_COMPLEXITY 24 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 214.85416666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6633643091368311 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.4465982966114721 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.42585699216623124 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.339210423620908 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.19092512184979765 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag23 0.17654809944161448 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.15639513398790306 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag20 -0.15546055696618183 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag15 0.1265734875676441 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.1081066441463383 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.8531968593597412 + Split Transformation ... ForecastMAPE TestMAPE +0 None _AirPassengers ... 0.0416 0.0348 +1 None _AirPassengers ... 0.0416 0.0348 +2 None _AirPassengers ... 0.0422 0.0612 +3 None _AirPassengers ... 0.0422 0.0612 +4 None Diff_AirPassengers ... 0.0451 0.1142 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 408.904537 +133 1960.083333 NaN 395.427754 +134 1960.166667 NaN 440.322975 +135 1960.250000 NaN 449.422941 +136 1960.333333 NaN 478.774398 +137 1960.416667 NaN 565.316388 +138 1960.500000 NaN 647.407475 +139 1960.583333 NaN 646.180590 +140 1960.666667 NaN 536.007422 +141 1960.750000 NaN 457.822350 +142 1960.833333 NaN 403.105121 +143 1960.916667 NaN 437.766598 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "24", + "MAE": "17.161315479641637", + "MAPE": "0.046", + "MASE": "0.518", + "RMSE": "21.059550122364946" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":349.7617800423,"121":353.9145499919,"122":394.5266842858,"123":415.8579539921,"124":412.1649085134,"125":520.440182951,"126":549.1334550178,"127":549.6935438438,"128":493.4010625647,"129":401.1791445314,"130":377.1394483386,"131":375.6212281461,"132":408.9045371233,"133":395.4277544932,"134":440.3229754196,"135":449.4229409739,"136":478.7743984394,"137":565.3163879942,"138":647.4074752043,"139":646.1805903936,"140":536.0074217459,"141":457.8223503961,"142":403.1051209803,"143":437.7665984952}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log new file mode 100644 index 000000000..a49f160da --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.316856622695923 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=267.5984848484849 StdDev=97.48409814756306 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_AirPassengers' Min=-212.0 Max=151.0 Mean=2.2196969696969697 StdDev=56.028985246807395 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL 'Diff_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2241 MAPE_Forecast=0.1303 MAPE_Test=0.2116 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2724 SMAPE_Forecast=0.1324 SMAPE_Test=0.2475 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.5089 MASE_Forecast=0.6613 MASE_Test=2.0931 +INFO:pyaf.std:MODEL_L1 L1_Fit=48.268959262635995 L1_Forecast=45.890897343739745 L1_Test=94.19030363488753 +INFO:pyaf.std:MODEL_L2 L2_Fit=62.23048368433899 L2_Forecast=57.54047980050583 L2_Test=112.33526236154151 +INFO:pyaf.std:MODEL_COMPLEXITY 56 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 112.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.46875 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 -0.45828198289550787 +INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.36773663417749636 +INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.35890691259028823 +INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 0.3050145559710619 +INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.2458215455721469 +INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.20490553150780677 +INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.17377400337660448 +INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1654882362659333 +INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15806914068920735 +INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.14193238313007694 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.9909157752990723 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.1263 0.2141 +1 None Diff_AirPassengers ... 0.1303 0.2116 +2 None _AirPassengers ... 0.1489 0.0795 +3 None _AirPassengers ... 0.1541 0.1560 +4 None _AirPassengers ... 0.1541 0.1560 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + 'Diff_AirPassengers', 'Diff_AirPassengers_ConstantTrend', + 'Diff_AirPassengers_ConstantTrend_residue', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', + 'Diff_AirPassengers_Trend', 'Diff_AirPassengers_Trend_residue', + 'Diff_AirPassengers_Cycle', 'Diff_AirPassengers_Cycle_residue', + 'Diff_AirPassengers_AR', 'Diff_AirPassengers_AR_residue', + 'Diff_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + 'Diff_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 263.395460 +133 1960.083333 NaN 358.811402 +134 1960.166667 NaN 356.880321 +135 1960.250000 NaN 418.256565 +136 1960.333333 NaN 405.907959 +137 1960.416667 NaN 455.559807 +138 1960.500000 NaN 517.136444 +139 1960.583333 NaN 482.980264 +140 1960.666667 NaN 471.688999 +141 1960.750000 NaN 407.830463 +142 1960.833333 NaN 388.357460 +143 1960.916667 NaN 342.624174 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Difference", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "56", + "MAE": "45.890897343739745", + "MAPE": "0.1303", + "MASE": "0.6613", + "RMSE": "57.54047980050583" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":365.5442810913,"121":366.83868392,"122":402.3261625211,"123":319.0394086405,"124":335.1599353305,"125":407.3971689594,"126":427.9053981254,"127":367.3764279551,"128":273.4256691304,"129":282.3166110423,"130":263.7148991496,"131":259.4376405383,"132":263.3954595823,"133":358.8114019757,"134":356.8803207615,"135":418.2565652055,"136":405.9079591852,"137":455.5598074419,"138":517.1364435327,"139":482.9802644552,"140":471.6889988825,"141":407.8304630844,"142":388.3574595565,"143":342.6241744893}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Median.log b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Median.log new file mode 100644 index 000000000..88322c882 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Median.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.493807554244995 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=267.5984848484849 StdDev=97.48409814756306 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_AirPassengers' Min=-212.0 Max=151.0 Mean=2.2196969696969697 StdDev=56.028985246807395 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL 'Diff_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2241 MAPE_Forecast=0.1303 MAPE_Test=0.2116 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2724 SMAPE_Forecast=0.1324 SMAPE_Test=0.2475 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.5089 MASE_Forecast=0.6613 MASE_Test=2.0931 +INFO:pyaf.std:MODEL_L1 L1_Fit=48.268959262635995 L1_Forecast=45.890897343739745 L1_Test=94.19030363488753 +INFO:pyaf.std:MODEL_L2 L2_Fit=62.23048368433899 L2_Forecast=57.54047980050583 L2_Test=112.33526236154151 +INFO:pyaf.std:MODEL_COMPLEXITY 56 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 112.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.46875 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 -0.45828198289550787 +INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.36773663417749636 +INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.35890691259028823 +INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 0.3050145559710619 +INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.2458215455721469 +INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.20490553150780677 +INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.17377400337660448 +INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1654882362659333 +INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15806914068920735 +INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.14193238313007694 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.726832389831543 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.1263 0.2141 +1 None Diff_AirPassengers ... 0.1303 0.2116 +2 None _AirPassengers ... 0.1489 0.0795 +3 None _AirPassengers ... 0.1541 0.1560 +4 None _AirPassengers ... 0.1541 0.1560 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + 'Diff_AirPassengers', 'Diff_AirPassengers_ConstantTrend', + 'Diff_AirPassengers_ConstantTrend_residue', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', + 'Diff_AirPassengers_Trend', 'Diff_AirPassengers_Trend_residue', + 'Diff_AirPassengers_Cycle', 'Diff_AirPassengers_Cycle_residue', + 'Diff_AirPassengers_AR', 'Diff_AirPassengers_AR_residue', + 'Diff_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + 'Diff_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 263.395460 +133 1960.083333 NaN 358.811402 +134 1960.166667 NaN 356.880321 +135 1960.250000 NaN 418.256565 +136 1960.333333 NaN 405.907959 +137 1960.416667 NaN 455.559807 +138 1960.500000 NaN 517.136444 +139 1960.583333 NaN 482.980264 +140 1960.666667 NaN 471.688999 +141 1960.750000 NaN 407.830463 +142 1960.833333 NaN 388.357460 +143 1960.916667 NaN 342.624174 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Difference", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "56", + "MAE": "45.890897343739745", + "MAPE": "0.1303", + "MASE": "0.6613", + "RMSE": "57.54047980050583" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":365.5442810913,"121":366.83868392,"122":402.3261625211,"123":319.0394086405,"124":335.1599353305,"125":407.3971689594,"126":427.9053981254,"127":367.3764279551,"128":273.4256691304,"129":282.3166110423,"130":263.7148991496,"131":259.4376405383,"132":263.3954595823,"133":358.8114019757,"134":356.8803207615,"135":418.2565652055,"136":405.9079591852,"137":455.5598074419,"138":517.1364435327,"139":482.9802644552,"140":471.6889988825,"141":407.8304630844,"142":388.3574595565,"143":342.6241744893}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_None.log b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_None.log new file mode 100644 index 000000000..85b8d321c --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_None.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.712966442108154 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0349 MAPE_Forecast=0.0217 MAPE_Test=0.0541 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0346 SMAPE_Forecast=0.022 SMAPE_Test=0.0558 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3563 MASE_Forecast=0.2287 MASE_Test=0.495 +INFO:pyaf.std:MODEL_L1 L1_Fit=6.661608657431955 L1_Forecast=8.471185219416746 L1_Test=22.27708217073943 +INFO:pyaf.std:MODEL_L2 L2_Fit=8.55391701208927 L2_Forecast=11.971762904643164 L2_Test=23.591231597994106 +INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (114.90523344815693, array([197.60619977])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag1 0.7786311442770555 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag12 0.7393757053696919 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag13 -0.5331389013974266 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag25 -0.28768918871674176 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag24 0.2430278567562677 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag33 -0.18406731003878712 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag5 0.17161844385288771 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag29 -0.16896686902619484 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16816626466383688 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag16 -0.16495284212921793 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.8581726551055908 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0205 0.0402 +1 None _AirPassengers ... 0.0217 0.0541 +2 None _AirPassengers ... 0.0217 0.0541 +3 None Diff_AirPassengers ... 0.0232 0.0296 +4 None _AirPassengers ... 0.0267 0.0281 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_LinearTrend', + '_AirPassengers_LinearTrend_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 404.192766 +133 1960.083333 NaN 362.272358 +134 1960.166667 NaN 407.365999 +135 1960.250000 NaN 392.377370 +136 1960.333333 NaN 426.008204 +137 1960.416667 NaN 494.632580 +138 1960.500000 NaN 561.124175 +139 1960.583333 NaN 558.395091 +140 1960.666667 NaN 437.986522 +141 1960.750000 NaN 374.130960 +142 1960.833333 NaN 319.906884 +143 1960.916667 NaN 355.980727 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "40", + "MAE": "8.471185219416746", + "MAPE": "0.0217", + "MASE": "0.2287", + "RMSE": "11.971762904643164" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":336.76433931,"121":322.8158768977,"122":370.1970011391,"123":378.032857026,"124":398.9335945669,"125":490.9916896704,"126":527.6050111739,"127":547.2692898968,"128":447.2569355179,"129":389.1498397716,"130":336.5874463252,"131":365.0545119964,"132":404.1927658781,"133":362.2723575754,"134":407.3659989043,"135":392.3773701386,"136":426.0082038069,"137":494.6325800922,"138":561.1241745776,"139":558.3950905275,"140":437.9865222535,"141":374.130959537,"142":319.9068836754,"143":355.9807270004}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_PreviousValue.log b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_PreviousValue.log new file mode 100644 index 000000000..13aa35c16 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_PreviousValue.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.74706244468689 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=262.54545454545456 StdDev=105.66229536195503 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=262.54545454545456 StdDev=105.66229536195503 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0578 MAPE_Forecast=0.0534 MAPE_Test=0.0458 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.057 SMAPE_Forecast=0.0529 SMAPE_Test=0.0468 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6624 MASE_Forecast=0.6008 MASE_Test=0.4199 +INFO:pyaf.std:MODEL_L1 L1_Fit=11.358328075276114 L1_Forecast=19.251997549361224 L1_Test=18.89462456527714 +INFO:pyaf.std:MODEL_L2 L2_Fit=14.077985325212376 L2_Forecast=24.067502391810844 L2_Test=23.649749044902716 +INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (117.5921115576074, array([192.02411022])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag1 0.4252197985200266 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag12 0.36697595235564745 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag24 0.3401705365803703 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag11 0.16420226860472517 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag32 -0.14805393889401813 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag23 0.14584806884402687 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag18 -0.13450057279540323 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag8 0.13142752937409 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag17 0.1297704970236005 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag13 -0.12777415957294197 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.955784797668457 + Split Transformation ... ForecastMAPE TestMAPE +0 None _AirPassengers ... 0.0534 0.0458 +1 None _AirPassengers ... 0.0534 0.0458 +2 None _AirPassengers ... 0.0577 0.0664 +3 None _AirPassengers ... 0.0577 0.0664 +4 None _AirPassengers ... 0.0674 0.0444 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_LinearTrend', + '_AirPassengers_LinearTrend_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 409.395560 +133 1960.083333 NaN 387.429379 +134 1960.166667 NaN 418.121199 +135 1960.250000 NaN 416.181510 +136 1960.333333 NaN 471.325027 +137 1960.416667 NaN 571.404690 +138 1960.500000 NaN 626.275575 +139 1960.583333 NaN 619.764037 +140 1960.666667 NaN 511.369319 +141 1960.750000 NaN 425.760934 +142 1960.833333 NaN 360.742997 +143 1960.916667 NaN 386.381643 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "40", + "MAE": "19.251997549361224", + "MAPE": "0.0534", + "MASE": "0.6008", + "RMSE": "24.067502391810844" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":342.9996280813,"121":358.3492861199,"122":377.1748585028,"123":404.3650724364,"124":426.6327624185,"125":505.6902207585,"126":541.5700614312,"127":543.5331825799,"128":464.1397374569,"129":383.703275922,"130":347.6393495103,"131":349.8212283795,"132":409.395559955,"133":387.4293792479,"134":418.1211990776,"135":416.1815095588,"136":471.3250269206,"137":571.4046899041,"138":626.2755745152,"139":619.7640367639,"140":511.3693194122,"141":425.7609337655,"142":360.742996881,"143":386.3816431888}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_None_Constant.log b/tests/references/missing_data_test_missing_data_air_passengers_None_Constant.log new file mode 100644 index 000000000..d687e9ccf --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_None_Constant.log @@ -0,0 +1,120 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.661966323852539 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=0.0 Max=559.0 Mean=217.7651515151515 StdDev=145.19849839822123 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_AirPassengers' Min=112.0 Max=28745.0 Mean=10835.780303030304 StdDev=8180.063378944654 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' +INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] +INFO:pyaf.std:TREND_DETAIL 'CumSum_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.3744 MAPE_Forecast=0.7917 MAPE_Test=1.0 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.5618 SMAPE_Forecast=1.5833 SMAPE_Test=2.0 +INFO:pyaf.std:MODEL_MASE MASE_Fit=3.496 MASE_Forecast=1.7806 MASE_Test=9.5185 +INFO:pyaf.std:MODEL_L1 L1_Fit=237.36208767361111 L1_Forecast=299.8333333333333 L1_Test=428.3333333333333 +INFO:pyaf.std:MODEL_L2 L2_Fit=692.9687888724792 L2_Forecast=341.0758713248417 L2_Test=433.5197035122318 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6601.760416666667 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.614473819732666 + Split Transformation ... ForecastMAPE TestMAPE +0 None CumSum_AirPassengers ... 7.917000e-01 1.0000 +1 None CumSum_AirPassengers ... 7.917000e-01 1.0000 +2 None Diff_AirPassengers ... 2.916667e+10 1.0845 +3 None Diff_AirPassengers ... 2.916667e+10 1.0845 +4 None Diff_AirPassengers ... 1.698962e+11 1.0369 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + 'CumSum_AirPassengers', 'CumSum_AirPassengers_ConstantTrend', + 'CumSum_AirPassengers_ConstantTrend_residue', + 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle', + 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR', + 'CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', + 'CumSum_AirPassengers_Trend', 'CumSum_AirPassengers_Trend_residue', + 'CumSum_AirPassengers_Cycle', 'CumSum_AirPassengers_Cycle_residue', + 'CumSum_AirPassengers_AR', 'CumSum_AirPassengers_AR_residue', + 'CumSum_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + 'CumSum_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 0.0 +133 1960.083333 NaN 0.0 +134 1960.166667 NaN 0.0 +135 1960.250000 NaN 0.0 +136 1960.333333 NaN 0.0 +137 1960.416667 NaN 0.0 +138 1960.500000 NaN 0.0 +139 1960.583333 NaN 0.0 +140 1960.666667 NaN 0.0 +141 1960.750000 NaN 0.0 +142 1960.833333 NaN 0.0 +143 1960.916667 NaN 0.0 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "Integration", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "299.8333333333333", + "MAPE": "0.7917", + "MASE": "1.7806", + "RMSE": "341.0758713248417" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":0.0,"121":0.0,"122":0.0,"123":0.0,"124":0.0,"125":0.0,"126":0.0,"127":0.0,"128":0.0,"129":0.0,"130":0.0,"131":0.0,"132":0.0,"133":0.0,"134":0.0,"135":0.0,"136":0.0,"137":0.0,"138":0.0,"139":0.0,"140":0.0,"141":0.0,"142":0.0,"143":0.0}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_None_DiscardRow.log b/tests/references/missing_data_test_missing_data_air_passengers_None_DiscardRow.log new file mode 100644 index 000000000..965317d37 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_None_DiscardRow.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.95564341545105 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.8333333333399 TimeDelta=0.10585585585594394 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=106 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=108.48509133028298 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0725 MAPE_Forecast=0.0985 MAPE_Test=0.0644 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0714 SMAPE_Forecast=0.0939 SMAPE_Test=0.0612 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6766 MASE_Forecast=0.889 MASE_Test=0.5956 +INFO:pyaf.std:MODEL_L1 L1_Fit=14.893862659174927 L1_Forecast=36.400686663473685 L1_Test=26.801344443932322 +INFO:pyaf.std:MODEL_L2 L2_Fit=20.281189554681816 L2_Forecast=42.57467195381298 L2_Test=36.480900936629716 +INFO:pyaf.std:MODEL_COMPLEXITY 18 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 218.78666666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.7348386019946807 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag17 0.4111158779983695 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag9 0.24273145738869362 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.22983622781493696 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag18 -0.20011565039535642 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.19469398401798493 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.1896760323123999 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.16021791921201803 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 0.14987076539906835 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.12053912174931067 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 1.026822805404663 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0934 0.1118 +1 None _AirPassengers ... 0.0941 0.0854 +2 None _AirPassengers ... 0.0941 0.0854 +3 None _AirPassengers ... 0.0981 0.1059 +4 None _AirPassengers ... 0.0981 0.1059 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 118 entries, 0 to 117 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 118 non-null float64 + 1 AirPassengers 106 non-null float64 + 2 AirPassengers_Forecast 118 non-null float64 +dtypes: float64(3) +memory usage: 2.9 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +106 1960.022523 NaN 490.664642 +107 1960.128378 NaN 549.379377 +108 1960.234234 NaN 536.200441 +109 1960.340090 NaN 538.835327 +110 1960.445946 NaN 550.462003 +111 1960.551802 NaN 602.622773 +112 1960.657658 NaN 582.759496 +113 1960.763514 NaN 537.032508 +114 1960.869369 NaN 473.003495 +115 1960.975225 NaN 481.941071 +116 1961.081081 NaN 540.369458 +117 1961.186937 NaN 620.931119 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 106 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(26)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "18", + "MAE": "36.400686663473685", + "MAPE": "0.0985", + "MASE": "0.889", + "RMSE": "42.57467195381298" + } + } +} + + + + + + +{"time":{"94":1959.0,"95":1959.0833333333,"96":1959.1666666667,"97":1959.25,"98":1959.3333333333,"99":1959.4166666667,"100":1959.5,"101":1959.5833333333,"102":1959.6666666667,"103":1959.75,"104":1959.8333333333,"105":1959.9166666667,"106":1960.0225225225,"107":1960.1283783784,"108":1960.2342342342,"109":1960.3400900901,"110":1960.4459459459,"111":1960.5518018018,"112":1960.6576576577,"113":1960.7635135135,"114":1960.8693693694,"115":1960.9752252252,"116":1961.0810810811,"117":1961.1869369369},"AirPassengers":{"94":360.0,"95":342.0,"96":406.0,"97":396.0,"98":420.0,"99":472.0,"100":548.0,"101":559.0,"102":463.0,"103":407.0,"104":362.0,"105":405.0,"106":null,"107":null,"108":null,"109":null,"110":null,"111":null,"112":null,"113":null,"114":null,"115":null,"116":null,"117":null},"AirPassengers_Forecast":{"94":362.6968077846,"95":392.6557489774,"96":409.3600997898,"97":484.9245689056,"98":447.5324241882,"99":473.9529793566,"100":508.7732196588,"101":534.9466890762,"102":505.0515831028,"103":405.6850563804,"104":386.1081910395,"105":420.738695298,"106":490.6646418646,"107":549.3793774842,"108":536.2004409066,"109":538.8353265618,"110":550.4620031346,"111":602.6227733166,"112":582.7594956409,"113":537.0325077925,"114":473.0034946541,"115":481.9410713016,"116":540.3694580531,"117":620.9311191539}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_None_Interpolate.log b/tests/references/missing_data_test_missing_data_air_passengers_None_Interpolate.log new file mode 100644 index 000000000..b7743b4f7 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_None_Interpolate.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.173720359802246 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=263.29924242424244 StdDev=104.85841221084239 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=263.29924242424244 StdDev=104.85841221084239 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0429 MAPE_Forecast=0.046 MAPE_Test=0.0397 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0428 SMAPE_Forecast=0.0453 SMAPE_Test=0.0392 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4728 MASE_Forecast=0.518 MASE_Test=0.3721 +INFO:pyaf.std:MODEL_L1 L1_Fit=8.217597173732674 L1_Forecast=17.161315479641637 L1_Test=16.744946957777486 +INFO:pyaf.std:MODEL_L2 L2_Fit=10.322965678348632 L2_Forecast=21.059550122364946 L2_Test=21.05332492372224 +INFO:pyaf.std:MODEL_COMPLEXITY 24 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 214.85416666666666 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.6633643091368311 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.4465982966114721 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.42585699216623124 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 -0.339210423620908 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag14 -0.19092512184979765 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag23 0.17654809944161448 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag21 0.15639513398790306 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag20 -0.15546055696618183 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag15 0.1265734875676441 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag16 -0.1081066441463383 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.7909109592437744 + Split Transformation ... ForecastMAPE TestMAPE +0 None _AirPassengers ... 0.0416 0.0348 +1 None _AirPassengers ... 0.0416 0.0348 +2 None _AirPassengers ... 0.0422 0.0612 +3 None _AirPassengers ... 0.0422 0.0612 +4 None Diff_AirPassengers ... 0.0451 0.1142 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 408.904537 +133 1960.083333 NaN 395.427754 +134 1960.166667 NaN 440.322975 +135 1960.250000 NaN 449.422941 +136 1960.333333 NaN 478.774398 +137 1960.416667 NaN 565.316388 +138 1960.500000 NaN 647.407475 +139 1960.583333 NaN 646.180590 +140 1960.666667 NaN 536.007422 +141 1960.750000 NaN 457.822350 +142 1960.833333 NaN 403.105121 +143 1960.916667 NaN 437.766598 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "24", + "MAE": "17.161315479641637", + "MAPE": "0.046", + "MASE": "0.518", + "RMSE": "21.059550122364946" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":349.7617800423,"121":353.9145499919,"122":394.5266842858,"123":415.8579539921,"124":412.1649085134,"125":520.440182951,"126":549.1334550178,"127":549.6935438438,"128":493.4010625647,"129":401.1791445314,"130":377.1394483386,"131":375.6212281461,"132":408.9045371233,"133":395.4277544932,"134":440.3229754196,"135":449.4229409739,"136":478.7743984394,"137":565.3163879942,"138":647.4074752043,"139":646.1805903936,"140":536.0074217459,"141":457.8223503961,"142":403.1051209803,"143":437.7665984952}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_None_Mean.log b/tests/references/missing_data_test_missing_data_air_passengers_None_Mean.log new file mode 100644 index 000000000..d7edac4a3 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_None_Mean.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.6368353366851807 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=267.5984848484849 StdDev=97.48409814756306 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_AirPassengers' Min=-212.0 Max=151.0 Mean=2.2196969696969697 StdDev=56.028985246807395 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL 'Diff_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2241 MAPE_Forecast=0.1303 MAPE_Test=0.2116 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2724 SMAPE_Forecast=0.1324 SMAPE_Test=0.2475 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.5089 MASE_Forecast=0.6613 MASE_Test=2.0931 +INFO:pyaf.std:MODEL_L1 L1_Fit=48.268959262635995 L1_Forecast=45.890897343739745 L1_Test=94.19030363488753 +INFO:pyaf.std:MODEL_L2 L2_Fit=62.23048368433899 L2_Forecast=57.54047980050583 L2_Test=112.33526236154151 +INFO:pyaf.std:MODEL_COMPLEXITY 56 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 112.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.46875 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 -0.45828198289550787 +INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.36773663417749636 +INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.35890691259028823 +INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 0.3050145559710619 +INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.2458215455721469 +INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.20490553150780677 +INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.17377400337660448 +INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1654882362659333 +INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15806914068920735 +INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.14193238313007694 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.661602258682251 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.1263 0.2141 +1 None Diff_AirPassengers ... 0.1303 0.2116 +2 None _AirPassengers ... 0.1489 0.0795 +3 None _AirPassengers ... 0.1541 0.1560 +4 None _AirPassengers ... 0.1541 0.1560 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + 'Diff_AirPassengers', 'Diff_AirPassengers_ConstantTrend', + 'Diff_AirPassengers_ConstantTrend_residue', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', + 'Diff_AirPassengers_Trend', 'Diff_AirPassengers_Trend_residue', + 'Diff_AirPassengers_Cycle', 'Diff_AirPassengers_Cycle_residue', + 'Diff_AirPassengers_AR', 'Diff_AirPassengers_AR_residue', + 'Diff_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + 'Diff_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 263.395460 +133 1960.083333 NaN 358.811402 +134 1960.166667 NaN 356.880321 +135 1960.250000 NaN 418.256565 +136 1960.333333 NaN 405.907959 +137 1960.416667 NaN 455.559807 +138 1960.500000 NaN 517.136444 +139 1960.583333 NaN 482.980264 +140 1960.666667 NaN 471.688999 +141 1960.750000 NaN 407.830463 +142 1960.833333 NaN 388.357460 +143 1960.916667 NaN 342.624174 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Difference", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "56", + "MAE": "45.890897343739745", + "MAPE": "0.1303", + "MASE": "0.6613", + "RMSE": "57.54047980050583" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":365.5442810913,"121":366.83868392,"122":402.3261625211,"123":319.0394086405,"124":335.1599353305,"125":407.3971689594,"126":427.9053981254,"127":367.3764279551,"128":273.4256691304,"129":282.3166110423,"130":263.7148991496,"131":259.4376405383,"132":263.3954595823,"133":358.8114019757,"134":356.8803207615,"135":418.2565652055,"136":405.9079591852,"137":455.5598074419,"138":517.1364435327,"139":482.9802644552,"140":471.6889988825,"141":407.8304630844,"142":388.3574595565,"143":342.6241744893}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_None_Median.log b/tests/references/missing_data_test_missing_data_air_passengers_None_Median.log new file mode 100644 index 000000000..88909d86f --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_None_Median.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.514920473098755 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=267.5984848484849 StdDev=97.48409814756306 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_AirPassengers' Min=-212.0 Max=151.0 Mean=2.2196969696969697 StdDev=56.028985246807395 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL 'Diff_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2241 MAPE_Forecast=0.1303 MAPE_Test=0.2116 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2724 SMAPE_Forecast=0.1324 SMAPE_Test=0.2475 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.5089 MASE_Forecast=0.6613 MASE_Test=2.0931 +INFO:pyaf.std:MODEL_L1 L1_Fit=48.268959262635995 L1_Forecast=45.890897343739745 L1_Test=94.19030363488753 +INFO:pyaf.std:MODEL_L2 L2_Fit=62.23048368433899 L2_Forecast=57.54047980050583 L2_Test=112.33526236154151 +INFO:pyaf.std:MODEL_COMPLEXITY 56 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 112.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.46875 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 -0.45828198289550787 +INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.36773663417749636 +INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.35890691259028823 +INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 0.3050145559710619 +INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.2458215455721469 +INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.20490553150780677 +INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.17377400337660448 +INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1654882362659333 +INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15806914068920735 +INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.14193238313007694 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.49689722061157227 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.1263 0.2141 +1 None Diff_AirPassengers ... 0.1303 0.2116 +2 None _AirPassengers ... 0.1489 0.0795 +3 None _AirPassengers ... 0.1541 0.1560 +4 None _AirPassengers ... 0.1541 0.1560 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + 'Diff_AirPassengers', 'Diff_AirPassengers_ConstantTrend', + 'Diff_AirPassengers_ConstantTrend_residue', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', + 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', + 'Diff_AirPassengers_Trend', 'Diff_AirPassengers_Trend_residue', + 'Diff_AirPassengers_Cycle', 'Diff_AirPassengers_Cycle_residue', + 'Diff_AirPassengers_AR', 'Diff_AirPassengers_AR_residue', + 'Diff_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + 'Diff_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 263.395460 +133 1960.083333 NaN 358.811402 +134 1960.166667 NaN 356.880321 +135 1960.250000 NaN 418.256565 +136 1960.333333 NaN 405.907959 +137 1960.416667 NaN 455.559807 +138 1960.500000 NaN 517.136444 +139 1960.583333 NaN 482.980264 +140 1960.666667 NaN 471.688999 +141 1960.750000 NaN 407.830463 +142 1960.833333 NaN 388.357460 +143 1960.916667 NaN 342.624174 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "Difference", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "56", + "MAE": "45.890897343739745", + "MAPE": "0.1303", + "MASE": "0.6613", + "RMSE": "57.54047980050583" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":365.5442810913,"121":366.83868392,"122":402.3261625211,"123":319.0394086405,"124":335.1599353305,"125":407.3971689594,"126":427.9053981254,"127":367.3764279551,"128":273.4256691304,"129":282.3166110423,"130":263.7148991496,"131":259.4376405383,"132":263.3954595823,"133":358.8114019757,"134":356.8803207615,"135":418.2565652055,"136":405.9079591852,"137":455.5598074419,"138":517.1364435327,"139":482.9802644552,"140":471.6889988825,"141":407.8304630844,"142":388.3574595565,"143":342.6241744893}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_None_None.log b/tests/references/missing_data_test_missing_data_air_passengers_None_None.log new file mode 100644 index 000000000..6bf006d17 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_None_None.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.8549957275390625 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0349 MAPE_Forecast=0.0217 MAPE_Test=0.0541 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0346 SMAPE_Forecast=0.022 SMAPE_Test=0.0558 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.3563 MASE_Forecast=0.2287 MASE_Test=0.495 +INFO:pyaf.std:MODEL_L1 L1_Fit=6.6616086574266005 L1_Forecast=8.471185219451263 L1_Test=22.27708217077866 +INFO:pyaf.std:MODEL_L2 L2_Fit=8.553917012084607 L2_Forecast=11.971762904662638 L2_Test=23.59123159802204 +INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (114.90523344816275, array([197.60619977])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag1 0.7786311442771912 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag12 0.7393757053683319 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag13 -0.5331389013991886 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag25 -0.2876891887152461 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag24 0.24302785675794508 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag33 -0.18406731004063412 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag5 0.17161844385149028 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag29 -0.16896686902773 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16816626466278906 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag16 -0.16495284212978803 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.6219191551208496 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_AirPassengers ... 0.0205 0.0402 +1 None _AirPassengers ... 0.0217 0.0541 +2 None _AirPassengers ... 0.0217 0.0541 +3 None Diff_AirPassengers ... 0.0232 0.0296 +4 None _AirPassengers ... 0.0267 0.0281 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_LinearTrend', + '_AirPassengers_LinearTrend_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 404.192766 +133 1960.083333 NaN 362.272358 +134 1960.166667 NaN 407.365999 +135 1960.250000 NaN 392.377370 +136 1960.333333 NaN 426.008204 +137 1960.416667 NaN 494.632580 +138 1960.500000 NaN 561.124175 +139 1960.583333 NaN 558.395091 +140 1960.666667 NaN 437.986522 +141 1960.750000 NaN 374.130960 +142 1960.833333 NaN 319.906884 +143 1960.916667 NaN 355.980727 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "40", + "MAE": "8.471185219451263", + "MAPE": "0.0217", + "MASE": "0.2287", + "RMSE": "11.971762904662638" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":336.76433931,"121":322.8158768976,"122":370.1970011391,"123":378.032857026,"124":398.9335945668,"125":490.9916896704,"126":527.6050111738,"127":547.2692898967,"128":447.2569355178,"129":389.1498397716,"130":336.5874463251,"131":365.0545119965,"132":404.1927658782,"133":362.2723575753,"134":407.3659989041,"135":392.3773701384,"136":426.0082038065,"137":494.6325800918,"138":561.1241745773,"139":558.3950905274,"140":437.9865222534,"141":374.1309595368,"142":319.9068836751,"143":355.980727}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_None_PreviousValue.log b/tests/references/missing_data_test_missing_data_air_passengers_None_PreviousValue.log new file mode 100644 index 000000000..d0b9bf8ac --- /dev/null +++ b/tests/references/missing_data_test_missing_data_air_passengers_None_PreviousValue.log @@ -0,0 +1,130 @@ +INFO:pyaf.std:START_TRAINING 'AirPassengers' +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.708620071411133 +INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=262.54545454545456 StdDev=105.66229536195503 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=262.54545454545456 StdDev=105.66229536195503 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0578 MAPE_Forecast=0.0534 MAPE_Test=0.0458 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.057 SMAPE_Forecast=0.0529 SMAPE_Test=0.0468 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6624 MASE_Forecast=0.6008 MASE_Test=0.4199 +INFO:pyaf.std:MODEL_L1 L1_Fit=11.358328075279735 L1_Forecast=19.25199754938692 L1_Test=18.89462456533037 +INFO:pyaf.std:MODEL_L2 L2_Fit=14.077985325218098 L2_Forecast=24.067502391822522 L2_Test=23.64974904495388 +INFO:pyaf.std:MODEL_COMPLEXITY 40 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (117.59211155761854, array([192.02411022])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag1 0.4252197985196897 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag12 0.366975952354409 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag24 0.34017053658055135 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag11 0.16420226860523576 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag32 -0.14805393889460772 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag23 0.14584806884481516 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag18 -0.13450057279560845 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag8 0.1314275293746011 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag17 0.1297704970242422 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_LinearTrend_residue_zeroCycle_residue_Lag13 -0.1277741595725081 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['AirPassengers']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.8362336158752441 + Split Transformation ... ForecastMAPE TestMAPE +0 None _AirPassengers ... 0.0534 0.0458 +1 None _AirPassengers ... 0.0534 0.0458 +2 None _AirPassengers ... 0.0577 0.0664 +3 None _AirPassengers ... 0.0577 0.0664 +4 None _AirPassengers ... 0.0674 0.0444 + +[5 rows x 8 columns] +Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', + '_AirPassengers', '_AirPassengers_LinearTrend', + '_AirPassengers_LinearTrend_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)', + '_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + 'AirPassengers_Forecast_Lower_Bound', + 'AirPassengers_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 144 entries, 0 to 143 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 time 144 non-null float64 + 1 AirPassengers 132 non-null float64 + 2 AirPassengers_Forecast 144 non-null float64 +dtypes: float64(3) +memory usage: 3.5 KB +None +Forecasts + time AirPassengers AirPassengers_Forecast +132 1960.000000 NaN 409.395560 +133 1960.083333 NaN 387.429379 +134 1960.166667 NaN 418.121199 +135 1960.250000 NaN 416.181510 +136 1960.333333 NaN 471.325027 +137 1960.416667 NaN 571.404690 +138 1960.500000 NaN 626.275575 +139 1960.583333 NaN 619.764037 +140 1960.666667 NaN 511.369319 +141 1960.750000 NaN 425.760934 +142 1960.833333 NaN 360.742997 +143 1960.916667 NaN 386.381643 + + + +{ + "AirPassengers": { + "Dataset": { + "Signal": "AirPassengers", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1949.0", + "1959.91666666667" + ], + "TimeVariable": "time" + }, + "Training_Signal_Length": 132 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_AR(33)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "40", + "MAE": "19.25199754938692", + "MAPE": "0.0534", + "MASE": "0.6008", + "RMSE": "24.067502391822522" + } + } +} + + + + + + +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":342.9996280812,"121":358.3492861198,"122":377.1748585027,"123":404.3650724364,"124":426.6327624186,"125":505.6902207586,"126":541.5700614312,"127":543.5331825798,"128":464.1397374569,"129":383.7032759219,"130":347.6393495102,"131":349.8212283795,"132":409.3955599549,"133":387.4293792477,"134":418.1211990774,"135":416.1815095587,"136":471.3250269205,"137":571.404689904,"138":626.2755745152,"139":619.7640367638,"140":511.369319412,"141":425.7609337652,"142":360.7429968807,"143":386.3816431886}} + + + diff --git a/tests/references/missing_data_test_missing_data_air_passengers_generic.log b/tests/references/missing_data_test_missing_data_air_passengers_generic.log new file mode 100644 index 000000000..e69de29bb diff --git a/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Constant.log b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Constant.log new file mode 100644 index 000000000..ea03b007d --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Constant.log @@ -0,0 +1,124 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.425104141235352 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-03-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=163 Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [PolyTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1779 MAPE_Test=0.6192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1576 SMAPE_Forecast=0.1634 SMAPE_Test=0.4293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.66 MASE_Forecast=0.5851 MASE_Test=1.742 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6538610100197174 L1_Forecast=0.5382912013378723 L1_Test=1.3143865373788406 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8960840013762631 L2_Forecast=0.6513448403061931 L2_Test=1.5381543451739965 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.195634187452363, array([-2.03243044, -0.50849057, 0.92798832])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear 0.001283377861313273 {1: -1.8072953772333187, 2: -1.5624007363452148, 3: -1.0834052126451248, 4: -0.29839806767416643, 5: -0.35288018119311837, 6: 0.6463434902138667, 7: 1.1559359715842499, 8: 1.1214585687792324, 9: 1.0749755140316934, 10: 1.074015035678863, 11: -0.13360876187702075, 12: -1.2692285670645405} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.39724159240722656 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1779 0.6192 +1 None _Ozone ... 0.1828 0.3141 +2 None _Ozone ... 0.1835 0.4118 +3 None _Ozone ... 0.1866 0.2956 +4 None _Ozone ... 0.2289 0.4397 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 175 entries, 0 to 174 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 175 non-null datetime64[ns] + 1 Ozone 163 non-null float64 + 2 Ozone_Forecast 175 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 4.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +163 1972-01-01 NaN 2.088298 +164 1972-02-01 NaN 2.347214 +165 1972-03-01 NaN 2.839626 +166 1972-04-01 NaN 3.639297 +167 1972-05-01 NaN 3.599325 +168 1972-06-01 NaN 4.613874 +169 1972-07-01 NaN 5.138620 +170 1972-08-01 NaN 5.120137 +171 1972-09-01 NaN 5.089990 +172 1972-10-01 NaN 5.105167 +173 1972-11-01 NaN 3.914559 +174 1972-12-01 NaN 2.795738 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 163 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5382912013378723", + "MAPE": "0.1779", + "MASE": "0.5851", + "RMSE": "0.6513448403061931" + } + } +} + + + + + + +{"Time":{"151":"1970-08-01T00:00:00.000Z","152":"1970-10-01T00:00:00.000Z","153":"1970-11-01T00:00:00.000Z","154":"1971-01-01T00:00:00.000Z","155":"1971-03-01T00:00:00.000Z","156":"1971-04-01T00:00:00.000Z","157":"1971-05-01T00:00:00.000Z","158":"1971-07-01T00:00:00.000Z","159":"1971-09-01T00:00:00.000Z","160":"1971-10-01T00:00:00.000Z","161":"1971-11-01T00:00:00.000Z","162":"1971-12-01T00:00:00.000Z","163":"1972-01-01T00:00:00.000Z","164":"1972-02-01T00:00:00.000Z","165":"1972-03-01T00:00:00.000Z","166":"1972-04-01T00:00:00.000Z","167":"1972-05-01T00:00:00.000Z","168":"1972-06-01T00:00:00.000Z","169":"1972-07-01T00:00:00.000Z","170":"1972-08-01T00:00:00.000Z","171":"1972-09-01T00:00:00.000Z","172":"1972-10-01T00:00:00.000Z","173":"1972-11-01T00:00:00.000Z","174":"1972-12-01T00:00:00.000Z"},"Ozone":{"151":4.7,"152":2.9,"153":1.7,"154":1.8,"155":2.2,"156":3.0,"157":2.4,"158":3.5,"159":2.7,"160":2.5,"161":1.6,"162":1.2,"163":null,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null},"Ozone_Forecast":{"151":4.8300551954,"152":4.8001408026,"153":3.6018719234,"154":1.9474813888,"155":2.6911666002,"156":3.4870249296,"157":3.4433417093,"158":4.9750259476,"159":4.9185714414,"160":4.9299302496,"161":3.7353550851,"162":2.6126731755,"163":2.0882978918,"164":2.3472135837,"165":2.8396255719,"166":3.6392969337,"167":3.5993250191,"168":4.6138740604,"169":5.138620112,"170":5.12013656,"171":5.0899898841,"172":5.1051667195,"173":3.9145588336,"174":2.7957375004}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log new file mode 100644 index 000000000..d9faecef3 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_DiscardRow.log @@ -0,0 +1,124 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.671087980270386 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-03-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=163 Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [PolyTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1779 MAPE_Test=0.6192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1576 SMAPE_Forecast=0.1634 SMAPE_Test=0.4293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.66 MASE_Forecast=0.5851 MASE_Test=1.742 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6538610100197174 L1_Forecast=0.5382912013378723 L1_Test=1.3143865373788406 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8960840013762631 L2_Forecast=0.6513448403061931 L2_Test=1.5381543451739965 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.195634187452363, array([-2.03243044, -0.50849057, 0.92798832])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear 0.001283377861313273 {1: -1.8072953772333187, 2: -1.5624007363452148, 3: -1.0834052126451248, 4: -0.29839806767416643, 5: -0.35288018119311837, 6: 0.6463434902138667, 7: 1.1559359715842499, 8: 1.1214585687792324, 9: 1.0749755140316934, 10: 1.074015035678863, 11: -0.13360876187702075, 12: -1.2692285670645405} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.46007323265075684 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1779 0.6192 +1 None _Ozone ... 0.1828 0.3141 +2 None _Ozone ... 0.1835 0.4118 +3 None _Ozone ... 0.1866 0.2956 +4 None _Ozone ... 0.2289 0.4397 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 175 entries, 0 to 174 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 175 non-null datetime64[ns] + 1 Ozone 163 non-null float64 + 2 Ozone_Forecast 175 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 4.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +163 1972-01-01 NaN 2.088298 +164 1972-02-01 NaN 2.347214 +165 1972-03-01 NaN 2.839626 +166 1972-04-01 NaN 3.639297 +167 1972-05-01 NaN 3.599325 +168 1972-06-01 NaN 4.613874 +169 1972-07-01 NaN 5.138620 +170 1972-08-01 NaN 5.120137 +171 1972-09-01 NaN 5.089990 +172 1972-10-01 NaN 5.105167 +173 1972-11-01 NaN 3.914559 +174 1972-12-01 NaN 2.795738 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 163 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5382912013378723", + "MAPE": "0.1779", + "MASE": "0.5851", + "RMSE": "0.6513448403061931" + } + } +} + + + + + + +{"Time":{"151":"1970-08-01T00:00:00.000Z","152":"1970-10-01T00:00:00.000Z","153":"1970-11-01T00:00:00.000Z","154":"1971-01-01T00:00:00.000Z","155":"1971-03-01T00:00:00.000Z","156":"1971-04-01T00:00:00.000Z","157":"1971-05-01T00:00:00.000Z","158":"1971-07-01T00:00:00.000Z","159":"1971-09-01T00:00:00.000Z","160":"1971-10-01T00:00:00.000Z","161":"1971-11-01T00:00:00.000Z","162":"1971-12-01T00:00:00.000Z","163":"1972-01-01T00:00:00.000Z","164":"1972-02-01T00:00:00.000Z","165":"1972-03-01T00:00:00.000Z","166":"1972-04-01T00:00:00.000Z","167":"1972-05-01T00:00:00.000Z","168":"1972-06-01T00:00:00.000Z","169":"1972-07-01T00:00:00.000Z","170":"1972-08-01T00:00:00.000Z","171":"1972-09-01T00:00:00.000Z","172":"1972-10-01T00:00:00.000Z","173":"1972-11-01T00:00:00.000Z","174":"1972-12-01T00:00:00.000Z"},"Ozone":{"151":4.7,"152":2.9,"153":1.7,"154":1.8,"155":2.2,"156":3.0,"157":2.4,"158":3.5,"159":2.7,"160":2.5,"161":1.6,"162":1.2,"163":null,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null},"Ozone_Forecast":{"151":4.8300551954,"152":4.8001408026,"153":3.6018719234,"154":1.9474813888,"155":2.6911666002,"156":3.4870249296,"157":3.4433417093,"158":4.9750259476,"159":4.9185714414,"160":4.9299302496,"161":3.7353550851,"162":2.6126731755,"163":2.0882978918,"164":2.3472135837,"165":2.8396255719,"166":3.6392969337,"167":3.5993250191,"168":4.6138740604,"169":5.138620112,"170":5.12013656,"171":5.0899898841,"172":5.1051667195,"173":3.9145588336,"174":2.7957375004}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log new file mode 100644 index 000000000..9d258f881 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Interpolate.log @@ -0,0 +1,124 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.950061559677124 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-03-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=163 Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [PolyTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1779 MAPE_Test=0.6192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1576 SMAPE_Forecast=0.1634 SMAPE_Test=0.4293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.66 MASE_Forecast=0.5851 MASE_Test=1.742 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6538610100197174 L1_Forecast=0.5382912013378723 L1_Test=1.3143865373788406 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8960840013762631 L2_Forecast=0.6513448403061931 L2_Test=1.5381543451739965 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.195634187452363, array([-2.03243044, -0.50849057, 0.92798832])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear 0.001283377861313273 {1: -1.8072953772333187, 2: -1.5624007363452148, 3: -1.0834052126451248, 4: -0.29839806767416643, 5: -0.35288018119311837, 6: 0.6463434902138667, 7: 1.1559359715842499, 8: 1.1214585687792324, 9: 1.0749755140316934, 10: 1.074015035678863, 11: -0.13360876187702075, 12: -1.2692285670645405} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.7054290771484375 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1779 0.6192 +1 None _Ozone ... 0.1828 0.3141 +2 None _Ozone ... 0.1835 0.4118 +3 None _Ozone ... 0.1866 0.2956 +4 None _Ozone ... 0.2289 0.4397 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 175 entries, 0 to 174 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 175 non-null datetime64[ns] + 1 Ozone 163 non-null float64 + 2 Ozone_Forecast 175 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 4.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +163 1972-01-01 NaN 2.088298 +164 1972-02-01 NaN 2.347214 +165 1972-03-01 NaN 2.839626 +166 1972-04-01 NaN 3.639297 +167 1972-05-01 NaN 3.599325 +168 1972-06-01 NaN 4.613874 +169 1972-07-01 NaN 5.138620 +170 1972-08-01 NaN 5.120137 +171 1972-09-01 NaN 5.089990 +172 1972-10-01 NaN 5.105167 +173 1972-11-01 NaN 3.914559 +174 1972-12-01 NaN 2.795738 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 163 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5382912013378723", + "MAPE": "0.1779", + "MASE": "0.5851", + "RMSE": "0.6513448403061931" + } + } +} + + + + + + +{"Time":{"151":"1970-08-01T00:00:00.000Z","152":"1970-10-01T00:00:00.000Z","153":"1970-11-01T00:00:00.000Z","154":"1971-01-01T00:00:00.000Z","155":"1971-03-01T00:00:00.000Z","156":"1971-04-01T00:00:00.000Z","157":"1971-05-01T00:00:00.000Z","158":"1971-07-01T00:00:00.000Z","159":"1971-09-01T00:00:00.000Z","160":"1971-10-01T00:00:00.000Z","161":"1971-11-01T00:00:00.000Z","162":"1971-12-01T00:00:00.000Z","163":"1972-01-01T00:00:00.000Z","164":"1972-02-01T00:00:00.000Z","165":"1972-03-01T00:00:00.000Z","166":"1972-04-01T00:00:00.000Z","167":"1972-05-01T00:00:00.000Z","168":"1972-06-01T00:00:00.000Z","169":"1972-07-01T00:00:00.000Z","170":"1972-08-01T00:00:00.000Z","171":"1972-09-01T00:00:00.000Z","172":"1972-10-01T00:00:00.000Z","173":"1972-11-01T00:00:00.000Z","174":"1972-12-01T00:00:00.000Z"},"Ozone":{"151":4.7,"152":2.9,"153":1.7,"154":1.8,"155":2.2,"156":3.0,"157":2.4,"158":3.5,"159":2.7,"160":2.5,"161":1.6,"162":1.2,"163":null,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null},"Ozone_Forecast":{"151":4.8300551954,"152":4.8001408026,"153":3.6018719234,"154":1.9474813888,"155":2.6911666002,"156":3.4870249296,"157":3.4433417093,"158":4.9750259476,"159":4.9185714414,"160":4.9299302496,"161":3.7353550851,"162":2.6126731755,"163":2.0882978918,"164":2.3472135837,"165":2.8396255719,"166":3.6392969337,"167":3.5993250191,"168":4.6138740604,"169":5.138620112,"170":5.12013656,"171":5.0899898841,"172":5.1051667195,"173":3.9145588336,"174":2.7957375004}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Mean.log b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Mean.log new file mode 100644 index 000000000..2bfaf9bee --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Mean.log @@ -0,0 +1,124 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.562351226806641 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-03-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=163 Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [PolyTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1779 MAPE_Test=0.6192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1576 SMAPE_Forecast=0.1634 SMAPE_Test=0.4293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.66 MASE_Forecast=0.5851 MASE_Test=1.742 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6538610100197174 L1_Forecast=0.5382912013378723 L1_Test=1.3143865373788406 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8960840013762631 L2_Forecast=0.6513448403061931 L2_Test=1.5381543451739965 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.195634187452363, array([-2.03243044, -0.50849057, 0.92798832])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear 0.001283377861313273 {1: -1.8072953772333187, 2: -1.5624007363452148, 3: -1.0834052126451248, 4: -0.29839806767416643, 5: -0.35288018119311837, 6: 0.6463434902138667, 7: 1.1559359715842499, 8: 1.1214585687792324, 9: 1.0749755140316934, 10: 1.074015035678863, 11: -0.13360876187702075, 12: -1.2692285670645405} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4741179943084717 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1779 0.6192 +1 None _Ozone ... 0.1828 0.3141 +2 None _Ozone ... 0.1835 0.4118 +3 None _Ozone ... 0.1866 0.2956 +4 None _Ozone ... 0.2289 0.4397 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 175 entries, 0 to 174 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 175 non-null datetime64[ns] + 1 Ozone 163 non-null float64 + 2 Ozone_Forecast 175 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 4.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +163 1972-01-01 NaN 2.088298 +164 1972-02-01 NaN 2.347214 +165 1972-03-01 NaN 2.839626 +166 1972-04-01 NaN 3.639297 +167 1972-05-01 NaN 3.599325 +168 1972-06-01 NaN 4.613874 +169 1972-07-01 NaN 5.138620 +170 1972-08-01 NaN 5.120137 +171 1972-09-01 NaN 5.089990 +172 1972-10-01 NaN 5.105167 +173 1972-11-01 NaN 3.914559 +174 1972-12-01 NaN 2.795738 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 163 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5382912013378723", + "MAPE": "0.1779", + "MASE": "0.5851", + "RMSE": "0.6513448403061931" + } + } +} + + + + + + +{"Time":{"151":"1970-08-01T00:00:00.000Z","152":"1970-10-01T00:00:00.000Z","153":"1970-11-01T00:00:00.000Z","154":"1971-01-01T00:00:00.000Z","155":"1971-03-01T00:00:00.000Z","156":"1971-04-01T00:00:00.000Z","157":"1971-05-01T00:00:00.000Z","158":"1971-07-01T00:00:00.000Z","159":"1971-09-01T00:00:00.000Z","160":"1971-10-01T00:00:00.000Z","161":"1971-11-01T00:00:00.000Z","162":"1971-12-01T00:00:00.000Z","163":"1972-01-01T00:00:00.000Z","164":"1972-02-01T00:00:00.000Z","165":"1972-03-01T00:00:00.000Z","166":"1972-04-01T00:00:00.000Z","167":"1972-05-01T00:00:00.000Z","168":"1972-06-01T00:00:00.000Z","169":"1972-07-01T00:00:00.000Z","170":"1972-08-01T00:00:00.000Z","171":"1972-09-01T00:00:00.000Z","172":"1972-10-01T00:00:00.000Z","173":"1972-11-01T00:00:00.000Z","174":"1972-12-01T00:00:00.000Z"},"Ozone":{"151":4.7,"152":2.9,"153":1.7,"154":1.8,"155":2.2,"156":3.0,"157":2.4,"158":3.5,"159":2.7,"160":2.5,"161":1.6,"162":1.2,"163":null,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null},"Ozone_Forecast":{"151":4.8300551954,"152":4.8001408026,"153":3.6018719234,"154":1.9474813888,"155":2.6911666002,"156":3.4870249296,"157":3.4433417093,"158":4.9750259476,"159":4.9185714414,"160":4.9299302496,"161":3.7353550851,"162":2.6126731755,"163":2.0882978918,"164":2.3472135837,"165":2.8396255719,"166":3.6392969337,"167":3.5993250191,"168":4.6138740604,"169":5.138620112,"170":5.12013656,"171":5.0899898841,"172":5.1051667195,"173":3.9145588336,"174":2.7957375004}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Median.log b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Median.log new file mode 100644 index 000000000..6f5ae8cff --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_Median.log @@ -0,0 +1,124 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.126365423202515 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-03-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=163 Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [PolyTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1779 MAPE_Test=0.6192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1576 SMAPE_Forecast=0.1634 SMAPE_Test=0.4293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.66 MASE_Forecast=0.5851 MASE_Test=1.742 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6538610100197174 L1_Forecast=0.5382912013378723 L1_Test=1.3143865373788406 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8960840013762631 L2_Forecast=0.6513448403061931 L2_Test=1.5381543451739965 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.195634187452363, array([-2.03243044, -0.50849057, 0.92798832])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear 0.001283377861313273 {1: -1.8072953772333187, 2: -1.5624007363452148, 3: -1.0834052126451248, 4: -0.29839806767416643, 5: -0.35288018119311837, 6: 0.6463434902138667, 7: 1.1559359715842499, 8: 1.1214585687792324, 9: 1.0749755140316934, 10: 1.074015035678863, 11: -0.13360876187702075, 12: -1.2692285670645405} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.442946195602417 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1779 0.6192 +1 None _Ozone ... 0.1828 0.3141 +2 None _Ozone ... 0.1835 0.4118 +3 None _Ozone ... 0.1866 0.2956 +4 None _Ozone ... 0.2289 0.4397 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 175 entries, 0 to 174 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 175 non-null datetime64[ns] + 1 Ozone 163 non-null float64 + 2 Ozone_Forecast 175 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 4.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +163 1972-01-01 NaN 2.088298 +164 1972-02-01 NaN 2.347214 +165 1972-03-01 NaN 2.839626 +166 1972-04-01 NaN 3.639297 +167 1972-05-01 NaN 3.599325 +168 1972-06-01 NaN 4.613874 +169 1972-07-01 NaN 5.138620 +170 1972-08-01 NaN 5.120137 +171 1972-09-01 NaN 5.089990 +172 1972-10-01 NaN 5.105167 +173 1972-11-01 NaN 3.914559 +174 1972-12-01 NaN 2.795738 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 163 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5382912013378723", + "MAPE": "0.1779", + "MASE": "0.5851", + "RMSE": "0.6513448403061931" + } + } +} + + + + + + +{"Time":{"151":"1970-08-01T00:00:00.000Z","152":"1970-10-01T00:00:00.000Z","153":"1970-11-01T00:00:00.000Z","154":"1971-01-01T00:00:00.000Z","155":"1971-03-01T00:00:00.000Z","156":"1971-04-01T00:00:00.000Z","157":"1971-05-01T00:00:00.000Z","158":"1971-07-01T00:00:00.000Z","159":"1971-09-01T00:00:00.000Z","160":"1971-10-01T00:00:00.000Z","161":"1971-11-01T00:00:00.000Z","162":"1971-12-01T00:00:00.000Z","163":"1972-01-01T00:00:00.000Z","164":"1972-02-01T00:00:00.000Z","165":"1972-03-01T00:00:00.000Z","166":"1972-04-01T00:00:00.000Z","167":"1972-05-01T00:00:00.000Z","168":"1972-06-01T00:00:00.000Z","169":"1972-07-01T00:00:00.000Z","170":"1972-08-01T00:00:00.000Z","171":"1972-09-01T00:00:00.000Z","172":"1972-10-01T00:00:00.000Z","173":"1972-11-01T00:00:00.000Z","174":"1972-12-01T00:00:00.000Z"},"Ozone":{"151":4.7,"152":2.9,"153":1.7,"154":1.8,"155":2.2,"156":3.0,"157":2.4,"158":3.5,"159":2.7,"160":2.5,"161":1.6,"162":1.2,"163":null,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null},"Ozone_Forecast":{"151":4.8300551954,"152":4.8001408026,"153":3.6018719234,"154":1.9474813888,"155":2.6911666002,"156":3.4870249296,"157":3.4433417093,"158":4.9750259476,"159":4.9185714414,"160":4.9299302496,"161":3.7353550851,"162":2.6126731755,"163":2.0882978918,"164":2.3472135837,"165":2.8396255719,"166":3.6392969337,"167":3.5993250191,"168":4.6138740604,"169":5.138620112,"170":5.12013656,"171":5.0899898841,"172":5.1051667195,"173":3.9145588336,"174":2.7957375004}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_DiscardRow_None.log b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_None.log new file mode 100644 index 000000000..a67b644e0 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_None.log @@ -0,0 +1,124 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.2635498046875 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-03-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=163 Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [PolyTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1779 MAPE_Test=0.6192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1576 SMAPE_Forecast=0.1634 SMAPE_Test=0.4293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.66 MASE_Forecast=0.5851 MASE_Test=1.742 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6538610100197174 L1_Forecast=0.5382912013378723 L1_Test=1.3143865373788406 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8960840013762631 L2_Forecast=0.6513448403061931 L2_Test=1.5381543451739965 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.195634187452363, array([-2.03243044, -0.50849057, 0.92798832])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear 0.001283377861313273 {1: -1.8072953772333187, 2: -1.5624007363452148, 3: -1.0834052126451248, 4: -0.29839806767416643, 5: -0.35288018119311837, 6: 0.6463434902138667, 7: 1.1559359715842499, 8: 1.1214585687792324, 9: 1.0749755140316934, 10: 1.074015035678863, 11: -0.13360876187702075, 12: -1.2692285670645405} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4645233154296875 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1779 0.6192 +1 None _Ozone ... 0.1828 0.3141 +2 None _Ozone ... 0.1835 0.4118 +3 None _Ozone ... 0.1866 0.2956 +4 None _Ozone ... 0.2289 0.4397 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 175 entries, 0 to 174 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 175 non-null datetime64[ns] + 1 Ozone 163 non-null float64 + 2 Ozone_Forecast 175 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 4.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +163 1972-01-01 NaN 2.088298 +164 1972-02-01 NaN 2.347214 +165 1972-03-01 NaN 2.839626 +166 1972-04-01 NaN 3.639297 +167 1972-05-01 NaN 3.599325 +168 1972-06-01 NaN 4.613874 +169 1972-07-01 NaN 5.138620 +170 1972-08-01 NaN 5.120137 +171 1972-09-01 NaN 5.089990 +172 1972-10-01 NaN 5.105167 +173 1972-11-01 NaN 3.914559 +174 1972-12-01 NaN 2.795738 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 163 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5382912013378723", + "MAPE": "0.1779", + "MASE": "0.5851", + "RMSE": "0.6513448403061931" + } + } +} + + + + + + +{"Time":{"151":"1970-08-01T00:00:00.000Z","152":"1970-10-01T00:00:00.000Z","153":"1970-11-01T00:00:00.000Z","154":"1971-01-01T00:00:00.000Z","155":"1971-03-01T00:00:00.000Z","156":"1971-04-01T00:00:00.000Z","157":"1971-05-01T00:00:00.000Z","158":"1971-07-01T00:00:00.000Z","159":"1971-09-01T00:00:00.000Z","160":"1971-10-01T00:00:00.000Z","161":"1971-11-01T00:00:00.000Z","162":"1971-12-01T00:00:00.000Z","163":"1972-01-01T00:00:00.000Z","164":"1972-02-01T00:00:00.000Z","165":"1972-03-01T00:00:00.000Z","166":"1972-04-01T00:00:00.000Z","167":"1972-05-01T00:00:00.000Z","168":"1972-06-01T00:00:00.000Z","169":"1972-07-01T00:00:00.000Z","170":"1972-08-01T00:00:00.000Z","171":"1972-09-01T00:00:00.000Z","172":"1972-10-01T00:00:00.000Z","173":"1972-11-01T00:00:00.000Z","174":"1972-12-01T00:00:00.000Z"},"Ozone":{"151":4.7,"152":2.9,"153":1.7,"154":1.8,"155":2.2,"156":3.0,"157":2.4,"158":3.5,"159":2.7,"160":2.5,"161":1.6,"162":1.2,"163":null,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null},"Ozone_Forecast":{"151":4.8300551954,"152":4.8001408026,"153":3.6018719234,"154":1.9474813888,"155":2.6911666002,"156":3.4870249296,"157":3.4433417093,"158":4.9750259476,"159":4.9185714414,"160":4.9299302496,"161":3.7353550851,"162":2.6126731755,"163":2.0882978918,"164":2.3472135837,"165":2.8396255719,"166":3.6392969337,"167":3.5993250191,"168":4.6138740604,"169":5.138620112,"170":5.12013656,"171":5.0899898841,"172":5.1051667195,"173":3.9145588336,"174":2.7957375004}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_DiscardRow_PreviousValue.log b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_PreviousValue.log new file mode 100644 index 000000000..6cabcac30 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_DiscardRow_PreviousValue.log @@ -0,0 +1,124 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 6.518484115600586 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-03-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=163 Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [PolyTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1779 MAPE_Test=0.6192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1576 SMAPE_Forecast=0.1634 SMAPE_Test=0.4293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.66 MASE_Forecast=0.5851 MASE_Test=1.742 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6538610100197174 L1_Forecast=0.5382912013378723 L1_Test=1.3143865373788406 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8960840013762631 L2_Forecast=0.6513448403061931 L2_Test=1.5381543451739965 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.195634187452363, array([-2.03243044, -0.50849057, 0.92798832])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear 0.001283377861313273 {1: -1.8072953772333187, 2: -1.5624007363452148, 3: -1.0834052126451248, 4: -0.29839806767416643, 5: -0.35288018119311837, 6: 0.6463434902138667, 7: 1.1559359715842499, 8: 1.1214585687792324, 9: 1.0749755140316934, 10: 1.074015035678863, 11: -0.13360876187702075, 12: -1.2692285670645405} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.7850363254547119 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1779 0.6192 +1 None _Ozone ... 0.1828 0.3141 +2 None _Ozone ... 0.1835 0.4118 +3 None _Ozone ... 0.1866 0.2956 +4 None _Ozone ... 0.2289 0.4397 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 175 entries, 0 to 174 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 175 non-null datetime64[ns] + 1 Ozone 163 non-null float64 + 2 Ozone_Forecast 175 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 4.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +163 1972-01-01 NaN 2.088298 +164 1972-02-01 NaN 2.347214 +165 1972-03-01 NaN 2.839626 +166 1972-04-01 NaN 3.639297 +167 1972-05-01 NaN 3.599325 +168 1972-06-01 NaN 4.613874 +169 1972-07-01 NaN 5.138620 +170 1972-08-01 NaN 5.120137 +171 1972-09-01 NaN 5.089990 +172 1972-10-01 NaN 5.105167 +173 1972-11-01 NaN 3.914559 +174 1972-12-01 NaN 2.795738 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 163 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5382912013378723", + "MAPE": "0.1779", + "MASE": "0.5851", + "RMSE": "0.6513448403061931" + } + } +} + + + + + + +{"Time":{"151":"1970-08-01T00:00:00.000Z","152":"1970-10-01T00:00:00.000Z","153":"1970-11-01T00:00:00.000Z","154":"1971-01-01T00:00:00.000Z","155":"1971-03-01T00:00:00.000Z","156":"1971-04-01T00:00:00.000Z","157":"1971-05-01T00:00:00.000Z","158":"1971-07-01T00:00:00.000Z","159":"1971-09-01T00:00:00.000Z","160":"1971-10-01T00:00:00.000Z","161":"1971-11-01T00:00:00.000Z","162":"1971-12-01T00:00:00.000Z","163":"1972-01-01T00:00:00.000Z","164":"1972-02-01T00:00:00.000Z","165":"1972-03-01T00:00:00.000Z","166":"1972-04-01T00:00:00.000Z","167":"1972-05-01T00:00:00.000Z","168":"1972-06-01T00:00:00.000Z","169":"1972-07-01T00:00:00.000Z","170":"1972-08-01T00:00:00.000Z","171":"1972-09-01T00:00:00.000Z","172":"1972-10-01T00:00:00.000Z","173":"1972-11-01T00:00:00.000Z","174":"1972-12-01T00:00:00.000Z"},"Ozone":{"151":4.7,"152":2.9,"153":1.7,"154":1.8,"155":2.2,"156":3.0,"157":2.4,"158":3.5,"159":2.7,"160":2.5,"161":1.6,"162":1.2,"163":null,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null},"Ozone_Forecast":{"151":4.8300551954,"152":4.8001408026,"153":3.6018719234,"154":1.9474813888,"155":2.6911666002,"156":3.4870249296,"157":3.4433417093,"158":4.9750259476,"159":4.9185714414,"160":4.9299302496,"161":3.7353550851,"162":2.6126731755,"163":2.0882978918,"164":2.3472135837,"165":2.8396255719,"166":3.6392969337,"167":3.5993250191,"168":4.6138740604,"169":5.138620112,"170":5.12013656,"171":5.0899898841,"172":5.1051667195,"173":3.9145588336,"174":2.7957375004}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_Interpolate_Constant.log b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Constant.log new file mode 100644 index 000000000..673608d18 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Constant.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 20.098680019378662 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-08-31T12:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=0.0 Max=8.7 Mean=3.1926470588235296 StdDev=2.088975462583432 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=651.3 Mean=374.50441176470576 StdDev=183.0649497142193 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' +INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] +INFO:pyaf.std:TREND_DETAIL 'CumSum_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'CumSum_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.528 MAPE_Forecast=0.7436 MAPE_Test=0.75 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.6338 SMAPE_Forecast=1.4872 SMAPE_Test=1.5 +INFO:pyaf.std:MODEL_MASE MASE_Fit=3.2557 MASE_Forecast=1.6574 MASE_Test=1.0083 +INFO:pyaf.std:MODEL_L1 L1_Fit=5.404028365158699 L1_Forecast=2.4948717948717953 L1_Test=1.741666666666667 +INFO:pyaf.std:MODEL_L2 L2_Fit=24.309118564219638 L2_Forecast=3.0362341722465924 L2_Test=2.094238127179747 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 299.11633986928086 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5181660652160645 + Split Transformation ... ForecastMAPE TestMAPE +0 None CumSum_Ozone ... 7.436000e-01 7.500000e-01 +1 None RelDiff_Ozone ... 7.436000e-01 7.500000e-01 +2 None RelDiff_Ozone ... 1.738350e+07 2.603929e+07 +3 None _Ozone ... 1.051282e+09 3.416667e+09 +4 None Diff_Ozone ... 1.117647e+09 1.955882e+09 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', 'CumSum_Ozone', + 'CumSum_Ozone_ConstantTrend', 'CumSum_Ozone_ConstantTrend_residue', + 'CumSum_Ozone_ConstantTrend_residue_zeroCycle', + 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue', + 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR', + 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', + 'CumSum_Ozone_Trend', 'CumSum_Ozone_Trend_residue', + 'CumSum_Ozone_Cycle', 'CumSum_Ozone_Cycle_residue', 'CumSum_Ozone_AR', + 'CumSum_Ozone_AR_residue', 'CumSum_Ozone_TransformedForecast', + 'Ozone_Forecast', 'CumSum_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1971-12-31 10:00:00 NaN 0.0 +205 1972-01-30 20:00:00 NaN 0.0 +206 1972-03-01 06:00:00 NaN 0.0 +207 1972-03-31 16:00:00 NaN 0.0 +208 1972-05-01 02:00:00 NaN 0.0 +209 1972-05-31 12:00:00 NaN 0.0 +210 1972-06-30 22:00:00 NaN 0.0 +211 1972-07-31 08:00:00 NaN 0.0 +212 1972-08-30 18:00:00 NaN 0.0 +213 1972-09-30 04:00:00 NaN 0.0 +214 1972-10-30 14:00:00 NaN 0.0 +215 1972-11-30 00:00:00 NaN 0.0 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "Integration", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "2.4948717948717953", + "MAPE": "0.7436", + "MASE": "1.6574", + "RMSE": "3.0362341722465924" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-01-30T12:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-05-31T12:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1971-12-31T10:00:00.000Z","205":"1972-01-30T20:00:00.000Z","206":"1972-03-01T06:00:00.000Z","207":"1972-03-31T16:00:00.000Z","208":"1972-05-01T02:00:00.000Z","209":"1972-05-31T12:00:00.000Z","210":"1972-06-30T22:00:00.000Z","211":"1972-07-31T08:00:00.000Z","212":"1972-08-30T18:00:00.000Z","213":"1972-09-30T04:00:00.000Z","214":"1972-10-30T14:00:00.000Z","215":"1972-11-30T00:00:00.000Z"},"Ozone":{"192":1.8,"193":0.0,"194":2.2,"195":3.0,"196":2.4,"197":0.0,"198":3.5,"199":0.0,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.0,"193":0.0,"194":0.0,"195":0.0,"196":0.0,"197":0.0,"198":0.0,"199":0.0,"200":0.0,"201":0.0,"202":0.0,"203":0.0,"204":0.0,"205":0.0,"206":0.0,"207":0.0,"208":0.0,"209":0.0,"210":0.0,"211":0.0,"212":0.0,"213":0.0,"214":0.0,"215":0.0}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log b/tests/references/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log new file mode 100644 index 000000000..ef13dcd3d --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_Interpolate_DiscardRow.log @@ -0,0 +1,124 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.932959079742432 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-03-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=163 Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [PolyTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1779 MAPE_Test=0.6192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1576 SMAPE_Forecast=0.1634 SMAPE_Test=0.4293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.66 MASE_Forecast=0.5851 MASE_Test=1.742 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6538610100197174 L1_Forecast=0.5382912013378723 L1_Test=1.3143865373788406 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8960840013762631 L2_Forecast=0.6513448403061931 L2_Test=1.5381543451739965 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.195634187452363, array([-2.03243044, -0.50849057, 0.92798832])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear 0.001283377861313273 {1: -1.8072953772333187, 2: -1.5624007363452148, 3: -1.0834052126451248, 4: -0.29839806767416643, 5: -0.35288018119311837, 6: 0.6463434902138667, 7: 1.1559359715842499, 8: 1.1214585687792324, 9: 1.0749755140316934, 10: 1.074015035678863, 11: -0.13360876187702075, 12: -1.2692285670645405} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5147905349731445 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1779 0.6192 +1 None _Ozone ... 0.1828 0.3141 +2 None _Ozone ... 0.1835 0.4118 +3 None _Ozone ... 0.1866 0.2956 +4 None _Ozone ... 0.2289 0.4397 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 175 entries, 0 to 174 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 175 non-null datetime64[ns] + 1 Ozone 163 non-null float64 + 2 Ozone_Forecast 175 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 4.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +163 1972-01-01 NaN 2.088298 +164 1972-02-01 NaN 2.347214 +165 1972-03-01 NaN 2.839626 +166 1972-04-01 NaN 3.639297 +167 1972-05-01 NaN 3.599325 +168 1972-06-01 NaN 4.613874 +169 1972-07-01 NaN 5.138620 +170 1972-08-01 NaN 5.120137 +171 1972-09-01 NaN 5.089990 +172 1972-10-01 NaN 5.105167 +173 1972-11-01 NaN 3.914559 +174 1972-12-01 NaN 2.795738 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 163 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5382912013378723", + "MAPE": "0.1779", + "MASE": "0.5851", + "RMSE": "0.6513448403061931" + } + } +} + + + + + + +{"Time":{"151":"1970-08-01T00:00:00.000Z","152":"1970-10-01T00:00:00.000Z","153":"1970-11-01T00:00:00.000Z","154":"1971-01-01T00:00:00.000Z","155":"1971-03-01T00:00:00.000Z","156":"1971-04-01T00:00:00.000Z","157":"1971-05-01T00:00:00.000Z","158":"1971-07-01T00:00:00.000Z","159":"1971-09-01T00:00:00.000Z","160":"1971-10-01T00:00:00.000Z","161":"1971-11-01T00:00:00.000Z","162":"1971-12-01T00:00:00.000Z","163":"1972-01-01T00:00:00.000Z","164":"1972-02-01T00:00:00.000Z","165":"1972-03-01T00:00:00.000Z","166":"1972-04-01T00:00:00.000Z","167":"1972-05-01T00:00:00.000Z","168":"1972-06-01T00:00:00.000Z","169":"1972-07-01T00:00:00.000Z","170":"1972-08-01T00:00:00.000Z","171":"1972-09-01T00:00:00.000Z","172":"1972-10-01T00:00:00.000Z","173":"1972-11-01T00:00:00.000Z","174":"1972-12-01T00:00:00.000Z"},"Ozone":{"151":4.7,"152":2.9,"153":1.7,"154":1.8,"155":2.2,"156":3.0,"157":2.4,"158":3.5,"159":2.7,"160":2.5,"161":1.6,"162":1.2,"163":null,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null},"Ozone_Forecast":{"151":4.8300551954,"152":4.8001408026,"153":3.6018719234,"154":1.9474813888,"155":2.6911666002,"156":3.4870249296,"157":3.4433417093,"158":4.9750259476,"159":4.9185714414,"160":4.9299302496,"161":3.7353550851,"162":2.6126731755,"163":2.0882978918,"164":2.3472135837,"165":2.8396255719,"166":3.6392969337,"167":3.5993250191,"168":4.6138740604,"169":5.138620112,"170":5.12013656,"171":5.0899898841,"172":5.1051667195,"173":3.9145588336,"174":2.7957375004}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log new file mode 100644 index 000000000..a7d1a0085 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Interpolate.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 22.187026500701904 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-08-31T12:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8769607843137255 StdDev=1.4457579544074046 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8769607843137255 StdDev=1.4457579544074046 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1711 MAPE_Forecast=0.1703 MAPE_Test=0.284 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1631 SMAPE_Forecast=0.1807 SMAPE_Test=0.2663 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8185 MASE_Forecast=0.7162 MASE_Test=1.3226 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6577676676115165 L1_Forecast=0.5268079808982122 L1_Test=0.6252096445434877 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8837464381752311 L2_Forecast=0.6382664268741287 L2_Test=0.6879251233223642 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.03643280857071, array([-1.75200629])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear -0.05234999480994951 {1: -1.5527815355911603, 2: -1.509557641681043, 3: -0.8880986539200117, 4: -0.44876925805667467, 5: -0.14896523986247523, 6: 0.6085947934506089, 7: 1.1388942924573606, 8: 1.2892664674841559, 9: 1.0623781082046184, 10: 0.5438534980461709, 11: -0.037635084185251966, 12: -1.0256642135311473} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.7095727920532227 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1626 0.1821 +1 None Diff_Ozone ... 0.1627 0.4207 +2 None _Ozone ... 0.1636 0.1692 +3 None _Ozone ... 0.1642 0.1569 +4 None _Ozone ... 0.1642 0.1569 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1971-12-31 10:00:00 NaN 1.659199 +205 1972-01-30 20:00:00 NaN 1.120561 +206 1972-03-01 06:00:00 NaN 1.773723 +207 1972-03-31 16:00:00 NaN 1.762202 +208 1972-05-01 02:00:00 NaN 2.489814 +209 1972-05-31 12:00:00 NaN 2.478293 +210 1972-06-30 22:00:00 NaN 3.224332 +211 1972-07-31 08:00:00 NaN 3.743111 +212 1972-08-30 18:00:00 NaN 3.881962 +213 1972-09-30 04:00:00 NaN 3.643553 +214 1972-10-30 14:00:00 NaN 3.113507 +215 1972-11-30 00:00:00 NaN 2.520498 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5268079808982122", + "MAPE": "0.1703", + "MASE": "0.7162", + "RMSE": "0.6382664268741287" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-01-30T12:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-05-31T12:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1971-12-31T10:00:00.000Z","205":"1972-01-30T20:00:00.000Z","206":"1972-03-01T06:00:00.000Z","207":"1972-03-31T16:00:00.000Z","208":"1972-05-01T02:00:00.000Z","209":"1972-05-31T12:00:00.000Z","210":"1972-06-30T22:00:00.000Z","211":"1972-07-31T08:00:00.000Z","212":"1972-08-30T18:00:00.000Z","213":"1972-09-30T04:00:00.000Z","214":"1972-10-30T14:00:00.000Z","215":"1972-11-30T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":2.95,"198":3.5,"199":3.1,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.2701122532,"193":1.2589385021,"194":1.9124476328,"195":2.3400351208,"196":2.6284760023,"197":2.61692348,"198":3.8932304901,"199":4.0318607573,"200":3.7932304901,"201":3.2633427433,"202":2.6701122532,"203":1.6707199872,"204":1.6591990292,"205":1.1205607491,"206":1.7737226728,"207":1.7622017148,"208":2.4898141709,"209":2.4782932129,"210":3.2243322882,"211":3.7431108292,"212":3.8819620463,"213":3.643552729,"214":3.1135071608,"215":2.5204976206}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_Interpolate_Mean.log b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Mean.log new file mode 100644 index 000000000..4e68abac2 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Mean.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 22.09389042854309 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-08-31T12:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.186 MAPE_Forecast=0.2019 MAPE_Test=0.3391 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1792 SMAPE_Forecast=0.2193 SMAPE_Test=0.3236 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7716 MASE_Forecast=0.7481 MASE_Test=0.8962 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7097052035997504 L1_Forecast=0.683170439922372 L1_Test=0.8310116976326895 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9319453089418573 L2_Forecast=0.8631899645276432 L2_Test=1.0573621390350647 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.957004747946751, array([-1.51797933])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.021757535043924747 {1: -1.658176842441088, 2: -1.5681158920836822, 3: -0.6127361256970136, 4: -0.3178345860081, 5: -0.3782307474080735, 6: 0.5314856051298322, 7: 1.0613262103845034, 8: 1.1326811347201735, 9: 0.9816731389750735, 10: 0.36126368848731394, 11: -0.09755797332098304, 12: -0.7674438878433212} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5276765823364258 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_Ozone ... 0.2012 0.8251 +1 None _Ozone ... 0.2019 0.3391 +2 None _Ozone ... 0.2228 0.2746 +3 None _Ozone ... 0.2288 0.3513 +4 None _Ozone ... 0.2321 0.2598 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1971-12-31 10:00:00 NaN 2.152106 +205 1972-01-30 20:00:00 NaN 1.251391 +206 1972-03-01 06:00:00 NaN 2.286850 +207 1972-03-31 16:00:00 NaN 2.276868 +208 1972-05-01 02:00:00 NaN 2.501391 +209 1972-05-31 12:00:00 NaN 2.491409 +210 1972-06-30 22:00:00 NaN 3.391143 +211 1972-07-31 08:00:00 NaN 3.911002 +212 1972-08-30 18:00:00 NaN 3.972375 +213 1972-09-30 04:00:00 NaN 3.811385 +214 1972-10-30 14:00:00 NaN 3.180993 +215 1972-11-30 00:00:00 NaN 2.712189 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.683170439922372", + "MAPE": "0.2019", + "MASE": "0.7481", + "RMSE": "0.8631899645276432" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-01-30T12:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-05-31T12:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1971-12-31T10:00:00.000Z","205":"1972-01-30T20:00:00.000Z","206":"1972-03-01T06:00:00.000Z","207":"1972-03-31T16:00:00.000Z","208":"1972-05-01T02:00:00.000Z","209":"1972-05-31T12:00:00.000Z","210":"1972-06-30T22:00:00.000Z","211":"1972-07-31T08:00:00.000Z","212":"1972-08-30T18:00:00.000Z","213":"1972-09-30T04:00:00.000Z","214":"1972-10-30T14:00:00.000Z","215":"1972-11-30T00:00:00.000Z"},"Ozone":{"192":1.8,"193":3.9,"194":2.2,"195":3.0,"196":2.4,"197":3.9,"198":3.5,"199":3.9,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.3809657977,"193":1.3712845978,"194":2.4070441147,"195":2.6917721901,"196":2.6215307406,"197":2.6115213645,"198":4.0410689461,"199":4.1022504061,"200":3.9410689461,"201":3.3108142076,"202":2.8418190815,"203":2.1620878789,"204":2.1521058508,"205":1.2513908681,"206":2.2868495567,"207":2.2768675285,"208":2.5013908787,"209":2.4914088505,"210":3.3911431749,"211":3.911001752,"212":3.9723746482,"213":3.8113846244,"214":3.1809931457,"215":2.7121894558}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_Interpolate_Median.log b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Median.log new file mode 100644 index 000000000..8fabd7480 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Median.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 20.59668517112732 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-08-31T12:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.186 MAPE_Forecast=0.2019 MAPE_Test=0.3391 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1792 SMAPE_Forecast=0.2193 SMAPE_Test=0.3236 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7716 MASE_Forecast=0.7481 MASE_Test=0.8962 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7097052035997504 L1_Forecast=0.683170439922372 L1_Test=0.8310116976326895 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9319453089418573 L2_Forecast=0.8631899645276432 L2_Test=1.0573621390350647 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.957004747946751, array([-1.51797933])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.021757535043924747 {1: -1.658176842441088, 2: -1.5681158920836822, 3: -0.6127361256970136, 4: -0.3178345860081, 5: -0.3782307474080735, 6: 0.5314856051298322, 7: 1.0613262103845034, 8: 1.1326811347201735, 9: 0.9816731389750735, 10: 0.36126368848731394, 11: -0.09755797332098304, 12: -0.7674438878433212} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4043614864349365 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_Ozone ... 0.2012 0.8251 +1 None _Ozone ... 0.2019 0.3391 +2 None _Ozone ... 0.2228 0.2746 +3 None _Ozone ... 0.2288 0.3513 +4 None _Ozone ... 0.2321 0.2598 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1971-12-31 10:00:00 NaN 2.152106 +205 1972-01-30 20:00:00 NaN 1.251391 +206 1972-03-01 06:00:00 NaN 2.286850 +207 1972-03-31 16:00:00 NaN 2.276868 +208 1972-05-01 02:00:00 NaN 2.501391 +209 1972-05-31 12:00:00 NaN 2.491409 +210 1972-06-30 22:00:00 NaN 3.391143 +211 1972-07-31 08:00:00 NaN 3.911002 +212 1972-08-30 18:00:00 NaN 3.972375 +213 1972-09-30 04:00:00 NaN 3.811385 +214 1972-10-30 14:00:00 NaN 3.180993 +215 1972-11-30 00:00:00 NaN 2.712189 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.683170439922372", + "MAPE": "0.2019", + "MASE": "0.7481", + "RMSE": "0.8631899645276432" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-01-30T12:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-05-31T12:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1971-12-31T10:00:00.000Z","205":"1972-01-30T20:00:00.000Z","206":"1972-03-01T06:00:00.000Z","207":"1972-03-31T16:00:00.000Z","208":"1972-05-01T02:00:00.000Z","209":"1972-05-31T12:00:00.000Z","210":"1972-06-30T22:00:00.000Z","211":"1972-07-31T08:00:00.000Z","212":"1972-08-30T18:00:00.000Z","213":"1972-09-30T04:00:00.000Z","214":"1972-10-30T14:00:00.000Z","215":"1972-11-30T00:00:00.000Z"},"Ozone":{"192":1.8,"193":3.9,"194":2.2,"195":3.0,"196":2.4,"197":3.9,"198":3.5,"199":3.9,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.3809657977,"193":1.3712845978,"194":2.4070441147,"195":2.6917721901,"196":2.6215307406,"197":2.6115213645,"198":4.0410689461,"199":4.1022504061,"200":3.9410689461,"201":3.3108142076,"202":2.8418190815,"203":2.1620878789,"204":2.1521058508,"205":1.2513908681,"206":2.2868495567,"207":2.2768675285,"208":2.5013908787,"209":2.4914088505,"210":3.3911431749,"211":3.911001752,"212":3.9723746482,"213":3.8113846244,"214":3.1809931457,"215":2.7121894558}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_Interpolate_None.log b/tests/references/missing_data_test_missing_data_ozone_Interpolate_None.log new file mode 100644 index 000000000..a31a3fea5 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_Interpolate_None.log @@ -0,0 +1,133 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 20.602391242980957 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-08-31T12:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.1739 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.1819 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6783 MASE_Test=0.9092 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6136101713443373 L1_Forecast=0.526542405155067 L1_Test=0.42979840671780245 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8095756882626074 L2_Forecast=0.7315662279542086 L2_Test=0.5518675091904144 +INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022642094023689, array([-1.82704162])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.43497254356109066 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.1903748534522913 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.1690848048112774 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16127846376437446 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14090593553176237 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13103518725516794 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12662122114617835 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12088774974620058 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11843782821154691 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11611700640734349 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.9557478427886963 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1563 0.2394 +1 None _Ozone ... 0.1595 0.1739 +2 None _Ozone ... 0.1595 0.1739 +3 None _Ozone ... 0.1658 0.3431 +4 None _Ozone ... 0.1658 0.3431 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_zeroCycle', + '_Ozone_LinearTrend_residue_zeroCycle_residue', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1971-12-31 10:00:00 NaN 0.611572 +205 1972-01-30 20:00:00 NaN 1.626992 +206 1972-03-01 06:00:00 NaN 1.942021 +207 1972-03-31 16:00:00 NaN 2.369638 +208 1972-05-01 02:00:00 NaN 2.662922 +209 1972-05-31 12:00:00 NaN 3.248855 +210 1972-06-30 22:00:00 NaN 3.220168 +211 1972-07-31 08:00:00 NaN 3.329437 +212 1972-08-30 18:00:00 NaN 2.997249 +213 1972-09-30 04:00:00 NaN 2.119110 +214 1972-10-30 14:00:00 NaN 1.333125 +215 1972-11-30 00:00:00 NaN 0.841840 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.526542405155067", + "MAPE": "0.1595", + "MASE": "0.6783", + "RMSE": "0.7315662279542086" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-01-30T12:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-05-31T12:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1971-12-31T10:00:00.000Z","205":"1972-01-30T20:00:00.000Z","206":"1972-03-01T06:00:00.000Z","207":"1972-03-31T16:00:00.000Z","208":"1972-05-01T02:00:00.000Z","209":"1972-05-31T12:00:00.000Z","210":"1972-06-30T22:00:00.000Z","211":"1972-07-31T08:00:00.000Z","212":"1972-08-30T18:00:00.000Z","213":"1972-09-30T04:00:00.000Z","214":"1972-10-30T14:00:00.000Z","215":"1972-11-30T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0203503303,"193":1.9482460846,"194":2.806842419,"195":1.9809915819,"196":3.2711760115,"197":3.018117693,"198":4.2390165104,"199":3.3630015337,"200":2.9405248253,"201":2.3739071848,"202":1.5678932825,"203":1.0534742622,"204":0.6115724593,"205":1.6269915297,"206":1.9420209185,"207":2.3696381176,"208":2.6629221982,"209":3.2488551947,"210":3.2201681195,"211":3.3294366778,"212":2.9972491991,"213":2.1191097585,"214":1.3331250269,"215":0.8418402819}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_Interpolate_PreviousValue.log b/tests/references/missing_data_test_missing_data_ozone_Interpolate_PreviousValue.log new file mode 100644 index 000000000..e789fccb1 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_Interpolate_PreviousValue.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 20.17494559288025 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-08-31T12:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.894607843137255 StdDev=1.494932729530275 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.894607843137255 StdDev=1.494932729530275 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1812 MAPE_Forecast=0.197 MAPE_Test=0.295 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1741 SMAPE_Forecast=0.2152 SMAPE_Test=0.2807 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8574 MASE_Forecast=0.8461 MASE_Test=1.347 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6881484790621084 L1_Forecast=0.6212481738840939 L1_Test=0.6367790775875833 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9400752652770673 L2_Forecast=0.7488520595407366 L2_Test=0.7456314037058528 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.0403572876692415, array([-1.76050911])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear -0.022677020935572223 {1: -1.6671388696032872, 2: -1.5114104135353617, 3: -1.1999561593430323, 4: -0.6218064742056091, 5: -0.36306273352439744, 6: 0.6099846938046238, 7: 1.140673901716232, 8: 1.3033844506889327, 9: 1.0642716889356656, 10: 1.1149930395630085, 11: -0.03428560980964779, 12: -1.026624515228353} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5090084075927734 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1951 0.2871 +1 None _Ozone ... 0.1970 0.2950 +2 None _Ozone ... 0.2084 0.2986 +3 None _Ozone ... 0.2129 0.4431 +4 None Diff_Ozone ... 0.2140 0.4008 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1971-12-31 10:00:00 NaN 1.650751 +205 1972-01-30 20:00:00 NaN 0.998659 +206 1972-03-01 06:00:00 NaN 1.454265 +207 1972-03-31 16:00:00 NaN 1.442688 +208 1972-05-01 02:00:00 NaN 2.268005 +209 1972-05-31 12:00:00 NaN 2.256428 +210 1972-06-30 22:00:00 NaN 3.217899 +211 1972-07-31 08:00:00 NaN 3.737011 +212 1972-08-30 18:00:00 NaN 3.888145 +213 1972-09-30 04:00:00 NaN 3.637455 +214 1972-10-30 14:00:00 NaN 3.676599 +215 1972-11-30 00:00:00 NaN 2.515744 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.6212481738840939", + "MAPE": "0.197", + "MASE": "0.8461", + "RMSE": "0.7488520595407366" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-01-30T12:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-05-31T12:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1971-12-31T10:00:00.000Z","205":"1972-01-30T20:00:00.000Z","206":"1972-03-01T06:00:00.000Z","207":"1972-03-31T16:00:00.000Z","208":"1972-05-01T02:00:00.000Z","209":"1972-05-31T12:00:00.000Z","210":"1972-06-30T22:00:00.000Z","211":"1972-07-31T08:00:00.000Z","212":"1972-08-30T18:00:00.000Z","213":"1972-09-30T04:00:00.000Z","214":"1972-10-30T14:00:00.000Z","215":"1972-11-30T00:00:00.000Z"},"Ozone":{"192":1.8,"193":1.8,"194":2.2,"195":3.0,"196":2.4,"197":2.4,"198":3.5,"199":3.5,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.1489366679,"193":1.1377086885,"194":1.5936634194,"195":2.1600142109,"196":2.4073396674,"197":2.3957310785,"198":3.8878591249,"199":4.0387707803,"200":3.7878591249,"201":3.8271621914,"202":2.6660846484,"203":1.6623274589,"204":1.6507505874,"205":0.9986593617,"206":1.4542652005,"207":1.4426883291,"208":2.2680048835,"209":2.2564280121,"210":3.217898568,"211":3.7370109045,"212":3.888144582,"213":3.6374549488,"214":3.6765994281,"215":2.5157439073}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_None_Constant.log b/tests/references/missing_data_test_missing_data_ozone_None_Constant.log new file mode 100644 index 000000000..959f2ff19 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_None_Constant.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.008234262466431 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=0.0 Max=8.7 Mean=3.1926470588235296 StdDev=2.088975462583432 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=651.3 Mean=374.50441176470576 StdDev=183.0649497142193 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' +INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] +INFO:pyaf.std:TREND_DETAIL 'CumSum_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'CumSum_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=1.528 MAPE_Forecast=0.7436 MAPE_Test=0.75 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=1.6338 SMAPE_Forecast=1.4872 SMAPE_Test=1.5 +INFO:pyaf.std:MODEL_MASE MASE_Fit=3.2557 MASE_Forecast=1.6574 MASE_Test=1.0083 +INFO:pyaf.std:MODEL_L1 L1_Fit=5.404028365158699 L1_Forecast=2.4948717948717953 L1_Test=1.741666666666667 +INFO:pyaf.std:MODEL_L2 L2_Fit=24.309118564219638 L2_Forecast=3.0362341722465924 L2_Test=2.094238127179747 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 299.11633986928086 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES CumSum_Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5649886131286621 + Split Transformation ... ForecastMAPE TestMAPE +0 None CumSum_Ozone ... 7.436000e-01 7.500000e-01 +1 None Diff_Ozone ... 1.117647e+09 1.955882e+09 +2 None Diff_Ozone ... 1.117647e+09 1.955882e+09 +3 None _Ozone ... 3.521752e+09 3.904247e+09 +4 None _Ozone ... 3.521752e+09 3.904247e+09 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', 'CumSum_Ozone', + 'CumSum_Ozone_ConstantTrend', 'CumSum_Ozone_ConstantTrend_residue', + 'CumSum_Ozone_ConstantTrend_residue_zeroCycle', + 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue', + 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR', + 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', + 'CumSum_Ozone_Trend', 'CumSum_Ozone_Trend_residue', + 'CumSum_Ozone_Cycle', 'CumSum_Ozone_Cycle_residue', 'CumSum_Ozone_AR', + 'CumSum_Ozone_AR_residue', 'CumSum_Ozone_TransformedForecast', + 'Ozone_Forecast', 'CumSum_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1972-01-01 NaN 0.0 +205 1972-02-01 NaN 0.0 +206 1972-03-01 NaN 0.0 +207 1972-04-01 NaN 0.0 +208 1972-05-01 NaN 0.0 +209 1972-06-01 NaN 0.0 +210 1972-07-01 NaN 0.0 +211 1972-08-01 NaN 0.0 +212 1972-09-01 NaN 0.0 +213 1972-10-01 NaN 0.0 +214 1972-11-01 NaN 0.0 +215 1972-12-01 NaN 0.0 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "Integration", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "2.4948717948717953", + "MAPE": "0.7436", + "MASE": "1.6574", + "RMSE": "3.0362341722465924" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":0.0,"194":2.2,"195":3.0,"196":2.4,"197":0.0,"198":3.5,"199":0.0,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":0.0,"193":0.0,"194":0.0,"195":0.0,"196":0.0,"197":0.0,"198":0.0,"199":0.0,"200":0.0,"201":0.0,"202":0.0,"203":0.0,"204":0.0,"205":0.0,"206":0.0,"207":0.0,"208":0.0,"209":0.0,"210":0.0,"211":0.0,"212":0.0,"213":0.0,"214":0.0,"215":0.0}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_None_DiscardRow.log b/tests/references/missing_data_test_missing_data_ozone_None_DiscardRow.log new file mode 100644 index 000000000..d5b771067 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_None_DiscardRow.log @@ -0,0 +1,124 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.886777639389038 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-03-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=163 Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9957055214723924 StdDev=1.500893381941983 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [PolyTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1648 MAPE_Forecast=0.1779 MAPE_Test=0.6192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1576 SMAPE_Forecast=0.1634 SMAPE_Test=0.4293 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.66 MASE_Forecast=0.5851 MASE_Test=1.742 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6538610100197174 L1_Forecast=0.5382912013378723 L1_Test=1.3143865373788406 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.8960840013762631 L2_Forecast=0.6513448403061931 L2_Test=1.5381543451739965 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.195634187452363, array([-2.03243044, -0.50849057, 0.92798832])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear 0.001283377861313273 {1: -1.8072953772333187, 2: -1.5624007363452148, 3: -1.0834052126451248, 4: -0.29839806767416643, 5: -0.35288018119311837, 6: 0.6463434902138667, 7: 1.1559359715842499, 8: 1.1214585687792324, 9: 1.0749755140316934, 10: 1.074015035678863, 11: -0.13360876187702075, 12: -1.2692285670645405} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4218943119049072 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1779 0.6192 +1 None _Ozone ... 0.1828 0.3141 +2 None _Ozone ... 0.1835 0.4118 +3 None _Ozone ... 0.1866 0.2956 +4 None _Ozone ... 0.2289 0.4397 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', + '_Ozone_PolyTrend_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 175 entries, 0 to 174 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 175 non-null datetime64[ns] + 1 Ozone 163 non-null float64 + 2 Ozone_Forecast 175 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 4.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +163 1972-01-01 NaN 2.088298 +164 1972-02-01 NaN 2.347214 +165 1972-03-01 NaN 2.839626 +166 1972-04-01 NaN 3.639297 +167 1972-05-01 NaN 3.599325 +168 1972-06-01 NaN 4.613874 +169 1972-07-01 NaN 5.138620 +170 1972-08-01 NaN 5.120137 +171 1972-09-01 NaN 5.089990 +172 1972-10-01 NaN 5.105167 +173 1972-11-01 NaN 3.914559 +174 1972-12-01 NaN 2.795738 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 163 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.5382912013378723", + "MAPE": "0.1779", + "MASE": "0.5851", + "RMSE": "0.6513448403061931" + } + } +} + + + + + + +{"Time":{"151":"1970-08-01T00:00:00.000Z","152":"1970-10-01T00:00:00.000Z","153":"1970-11-01T00:00:00.000Z","154":"1971-01-01T00:00:00.000Z","155":"1971-03-01T00:00:00.000Z","156":"1971-04-01T00:00:00.000Z","157":"1971-05-01T00:00:00.000Z","158":"1971-07-01T00:00:00.000Z","159":"1971-09-01T00:00:00.000Z","160":"1971-10-01T00:00:00.000Z","161":"1971-11-01T00:00:00.000Z","162":"1971-12-01T00:00:00.000Z","163":"1972-01-01T00:00:00.000Z","164":"1972-02-01T00:00:00.000Z","165":"1972-03-01T00:00:00.000Z","166":"1972-04-01T00:00:00.000Z","167":"1972-05-01T00:00:00.000Z","168":"1972-06-01T00:00:00.000Z","169":"1972-07-01T00:00:00.000Z","170":"1972-08-01T00:00:00.000Z","171":"1972-09-01T00:00:00.000Z","172":"1972-10-01T00:00:00.000Z","173":"1972-11-01T00:00:00.000Z","174":"1972-12-01T00:00:00.000Z"},"Ozone":{"151":4.7,"152":2.9,"153":1.7,"154":1.8,"155":2.2,"156":3.0,"157":2.4,"158":3.5,"159":2.7,"160":2.5,"161":1.6,"162":1.2,"163":null,"164":null,"165":null,"166":null,"167":null,"168":null,"169":null,"170":null,"171":null,"172":null,"173":null,"174":null},"Ozone_Forecast":{"151":4.8300551954,"152":4.8001408026,"153":3.6018719234,"154":1.9474813888,"155":2.6911666002,"156":3.4870249296,"157":3.4433417093,"158":4.9750259476,"159":4.9185714414,"160":4.9299302496,"161":3.7353550851,"162":2.6126731755,"163":2.0882978918,"164":2.3472135837,"165":2.8396255719,"166":3.6392969337,"167":3.5993250191,"168":4.6138740604,"169":5.138620112,"170":5.12013656,"171":5.0899898841,"172":5.1051667195,"173":3.9145588336,"174":2.7957375004}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_None_Interpolate.log b/tests/references/missing_data_test_missing_data_ozone_None_Interpolate.log new file mode 100644 index 000000000..28b1ff5c5 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_None_Interpolate.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.280732870101929 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8769607843137255 StdDev=1.4457579544074046 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8769607843137255 StdDev=1.4457579544074046 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' [Lag1Trend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1676 MAPE_Forecast=0.1706 MAPE_Test=0.1249 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1581 SMAPE_Forecast=0.1661 SMAPE_Test=0.1218 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8057 MASE_Forecast=0.7205 MASE_Test=0.5773 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6474945533769063 L1_Forecast=0.52991452991453 L1_Test=0.27291666666666653 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9041359818292513 L2_Forecast=0.7189810572496058 L2_Test=0.38682737321273764 +INFO:pyaf.std:MODEL_COMPLEXITY 36 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 2.7 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear 0.10000000000000053 {1: -0.09999999999999964, 2: 0.2999999999999998, 3: 0.4500000000000002, 4: 0.5, 5: 0.44999999999999973, 6: 0.5, 7: 0.5, 8: -0.20000000000000018, 9: 0.15000000000000036, 10: -0.30000000000000027, 11: -1.0, 12: -0.7749999999999999} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.556013822555542 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1642 0.1569 +1 None _Ozone ... 0.1642 0.1569 +2 None _Ozone ... 0.1689 0.2209 +3 None _Ozone ... 0.1706 0.1249 +4 None _Ozone ... 0.1758 0.3832 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_Lag1Trend', '_Ozone_Lag1Trend_residue', + '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear', + '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1972-01-01 NaN 1.100 +205 1972-02-01 NaN 1.400 +206 1972-03-01 NaN 1.850 +207 1972-04-01 NaN 2.350 +208 1972-05-01 NaN 2.800 +209 1972-06-01 NaN 3.300 +210 1972-07-01 NaN 3.800 +211 1972-08-01 NaN 3.600 +212 1972-09-01 NaN 3.750 +213 1972-10-01 NaN 3.450 +214 1972-11-01 NaN 2.450 +215 1972-12-01 NaN 1.675 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "Lag1Trend" + }, + "Model_Performance": { + "COMPLEXITY": "36", + "MAE": "0.52991452991453", + "MAPE": "0.1706", + "MASE": "0.7205", + "RMSE": "0.7189810572496058" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":2.95,"198":3.5,"199":3.1,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.65,"193":2.1,"194":2.45,"195":2.7,"196":3.45,"197":2.9,"198":3.45,"199":3.3,"200":3.25,"201":2.4,"202":1.5,"203":0.825,"204":1.1,"205":1.4,"206":1.85,"207":2.35,"208":2.8,"209":3.3,"210":3.8,"211":3.6,"212":3.75,"213":3.45,"214":2.45,"215":1.675}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_None_Mean.log b/tests/references/missing_data_test_missing_data_ozone_None_Mean.log new file mode 100644 index 000000000..47a97bb7c --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_None_Mean.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.239546775817871 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1822 MAPE_Forecast=0.1997 MAPE_Test=0.33 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1756 SMAPE_Forecast=0.216 SMAPE_Test=0.3036 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7609 MASE_Forecast=0.7309 MASE_Test=0.8424 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6998707162064494 L1_Forecast=0.6674497132372681 L1_Test=0.7811220833031284 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9283159076128001 L2_Forecast=0.8437799795537803 L2_Test=0.9944646980528729 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.956972420759976, array([-1.51809377])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.021764394693804867 {1: -1.7170834143981075, 2: -1.4871298623881208, 3: -0.8992611126075549, 4: -0.3080986690256613, 5: -0.33858427618918574, 6: 0.39136927582080006, 7: 1.061325235520295, 8: 1.1916069530656594, 9: 0.9022216515254193, 10: 0.7721209105610938, 11: -0.10781574763554258, 12: -0.7675304185559155} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4609558582305908 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1997 0.3300 +1 None _Ozone ... 0.2126 0.2566 +2 None _Ozone ... 0.2156 0.4677 +3 None Diff_Ozone ... 0.2278 0.3931 +4 None _Ozone ... 0.2321 0.2598 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1972-01-01 NaN 1.202309 +205 1972-02-01 NaN 1.422090 +206 1972-03-01 NaN 2.000442 +207 1972-04-01 NaN 2.581431 +208 1972-05-01 NaN 2.541100 +209 1972-06-01 NaN 3.260881 +210 1972-07-01 NaN 3.920992 +211 1972-08-01 NaN 4.041100 +212 1972-09-01 NaN 3.741542 +213 1972-10-01 NaN 3.601596 +214 1972-11-01 NaN 2.711486 +215 1972-12-01 NaN 2.041927 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.6674497132372681", + "MAPE": "0.1997", + "MASE": "0.7309", + "RMSE": "0.8437799795537803" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":3.9,"194":2.2,"195":3.0,"196":2.4,"197":3.9,"198":3.5,"199":3.9,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.3220896176,"193":1.541870038,"194":2.1205501528,"195":2.7015394648,"196":2.6612088916,"197":3.380989312,"198":4.0411003056,"199":4.1612088916,"200":3.8616504584,"201":3.7217047514,"202":2.8315949616,"203":2.1620353246,"204":1.2023091972,"205":1.4220896176,"206":2.0004415669,"207":2.5814308788,"208":2.5411003056,"209":3.260880726,"210":3.9209917197,"211":4.0411003056,"212":3.7415418725,"213":3.6015961655,"214":2.7114863757,"215":2.0419267387}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_None_Median.log b/tests/references/missing_data_test_missing_data_ozone_None_Median.log new file mode 100644 index 000000000..5bdb0730a --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_None_Median.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 3.9248032569885254 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1822 MAPE_Forecast=0.1997 MAPE_Test=0.33 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1756 SMAPE_Forecast=0.216 SMAPE_Test=0.3036 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7609 MASE_Forecast=0.7309 MASE_Test=0.8424 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6998707162064494 L1_Forecast=0.6674497132372681 L1_Test=0.7811220833031284 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9283159076128001 L2_Forecast=0.8437799795537803 L2_Test=0.9944646980528729 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.956972420759976, array([-1.51809377])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.021764394693804867 {1: -1.7170834143981075, 2: -1.4871298623881208, 3: -0.8992611126075549, 4: -0.3080986690256613, 5: -0.33858427618918574, 6: 0.39136927582080006, 7: 1.061325235520295, 8: 1.1916069530656594, 9: 0.9022216515254193, 10: 0.7721209105610938, 11: -0.10781574763554258, 12: -0.7675304185559155} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.3290259838104248 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1997 0.3300 +1 None _Ozone ... 0.2126 0.2566 +2 None _Ozone ... 0.2156 0.4677 +3 None Diff_Ozone ... 0.2278 0.3931 +4 None _Ozone ... 0.2321 0.2598 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1972-01-01 NaN 1.202309 +205 1972-02-01 NaN 1.422090 +206 1972-03-01 NaN 2.000442 +207 1972-04-01 NaN 2.581431 +208 1972-05-01 NaN 2.541100 +209 1972-06-01 NaN 3.260881 +210 1972-07-01 NaN 3.920992 +211 1972-08-01 NaN 4.041100 +212 1972-09-01 NaN 3.741542 +213 1972-10-01 NaN 3.601596 +214 1972-11-01 NaN 2.711486 +215 1972-12-01 NaN 2.041927 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.6674497132372681", + "MAPE": "0.1997", + "MASE": "0.7309", + "RMSE": "0.8437799795537803" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":3.9,"194":2.2,"195":3.0,"196":2.4,"197":3.9,"198":3.5,"199":3.9,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.3220896176,"193":1.541870038,"194":2.1205501528,"195":2.7015394648,"196":2.6612088916,"197":3.380989312,"198":4.0411003056,"199":4.1612088916,"200":3.8616504584,"201":3.7217047514,"202":2.8315949616,"203":2.1620353246,"204":1.2023091972,"205":1.4220896176,"206":2.0004415669,"207":2.5814308788,"208":2.5411003056,"209":3.260880726,"210":3.9209917197,"211":4.0411003056,"212":3.7415418725,"213":3.6015961655,"214":2.7114863757,"215":2.0419267387}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_None_None.log b/tests/references/missing_data_test_missing_data_ozone_None_None.log new file mode 100644 index 000000000..9b8b0854d --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_None_None.log @@ -0,0 +1,133 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.416539192199707 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [LinearTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1641 MAPE_Forecast=0.1595 MAPE_Test=0.174 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1547 SMAPE_Forecast=0.178 SMAPE_Test=0.182 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6981 MASE_Forecast=0.6782 MASE_Test=0.9094 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.613597862427259 L1_Forecast=0.5265316758013029 L1_Test=0.42992050508902313 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.809570003175602 L2_Forecast=0.731568864950715 L2_Test=0.5519432695595543 +INFO:pyaf.std:MODEL_COMPLEXITY 54 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag1 0.4349634413603371 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag10 0.19036685617533317 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag7 -0.16906339635225442 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag30 0.16128205795389375 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag12 0.14093045574208746 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag32 -0.13104711303046346 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag48 0.12661310040481832 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag36 0.12091470037102245 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag22 0.11845464489045833 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag39 -0.11612291477947745 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.7444770336151123 + Split Transformation ... ForecastMAPE TestMAPE +0 None _Ozone ... 0.1595 0.1740 +1 None _Ozone ... 0.1595 0.1740 +2 None _Ozone ... 0.1657 0.3430 +3 None _Ozone ... 0.1657 0.3430 +4 None _Ozone ... 0.1765 0.2209 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_zeroCycle', + '_Ozone_LinearTrend_residue_zeroCycle_residue', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)', + '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1972-01-01 NaN 0.611147 +205 1972-02-01 NaN 1.626529 +206 1972-03-01 NaN 1.942209 +207 1972-04-01 NaN 2.369673 +208 1972-05-01 NaN 2.663022 +209 1972-06-01 NaN 3.248702 +210 1972-07-01 NaN 3.220270 +211 1972-08-01 NaN 3.329387 +212 1972-09-01 NaN 2.996846 +213 1972-10-01 NaN 2.118734 +214 1972-11-01 NaN 1.332600 +215 1972-12-01 NaN 0.841575 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_AR(51)", + "Cycle": "NoCycle", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "54", + "MAE": "0.5265316758013029", + "MAPE": "0.1595", + "MASE": "0.6782", + "RMSE": "0.731568864950715" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.0201234776,"193":1.9477080322,"194":2.8071877532,"195":1.9810299731,"196":3.2711119481,"197":3.0180455521,"198":4.2391631176,"199":3.363140279,"200":2.9406052694,"201":2.3736934134,"202":1.5680019494,"203":1.0535599086,"204":0.6111465642,"205":1.6265294603,"206":1.9422085586,"207":2.3696733129,"208":2.6630222018,"209":3.2487020879,"210":3.220269503,"211":3.32938729,"212":2.996846273,"213":2.1187344328,"214":1.3326002972,"215":0.8415747446}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_None_PreviousValue.log b/tests/references/missing_data_test_missing_data_ozone_None_PreviousValue.log new file mode 100644 index 000000000..83571f6d1 --- /dev/null +++ b/tests/references/missing_data_test_missing_data_ozone_None_PreviousValue.log @@ -0,0 +1,123 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 5.0434205532073975 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.894607843137255 StdDev=1.494932729530275 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.894607843137255 StdDev=1.494932729530275 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1846 MAPE_Forecast=0.2035 MAPE_Test=0.3406 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1778 SMAPE_Forecast=0.2219 SMAPE_Test=0.3159 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8747 MASE_Forecast=0.8599 MASE_Test=1.5666 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7020573838381703 L1_Forecast=0.6313296922866238 L1_Test=0.7405767373606921 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.940956787291862 L2_Forecast=0.7629312224974826 L2_Test=0.8254794625272465 +INFO:pyaf.std:MODEL_COMPLEXITY 20 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.040289901229871, array([-1.76058204])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear -0.022901102636356363 {1: -1.5721897998415153, 2: -1.6942514514497402, 3: -1.26647492533509, 4: -0.6546768182981197, 5: -0.29929266248856834, 6: 0.5074520074357238, 7: 1.1406625547610183, 8: 1.291754441653826, 9: 1.1475191815613446, 10: 1.1369533880650733, 11: 0.15964800729518847, 12: -1.0265980432584532} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Ozone']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.3555004596710205 + Split Transformation ... ForecastMAPE TestMAPE +0 None Diff_Ozone ... 0.2006 0.5703 +1 None _Ozone ... 0.2035 0.3406 +2 None _Ozone ... 0.2085 0.3335 +3 None Diff_Ozone ... 0.2177 0.5305 +4 None Diff_Ozone ... 0.2177 0.5305 + +[5 rows x 8 columns] +Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR', + '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR_residue', + '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', + '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', + '_Ozone_TransformedForecast', 'Ozone_Forecast', + '_Ozone_TransformedResidue', 'Ozone_Residue', + 'Ozone_Forecast_Lower_Bound', 'Ozone_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 216 entries, 0 to 215 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Time 216 non-null datetime64[ns] + 1 Ozone 204 non-null float64 + 2 Ozone_Forecast 216 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 5.2 KB +None +Forecasts + Time Ozone Ozone_Forecast +204 1972-01-01 NaN 1.105053 +205 1972-02-01 NaN 0.971194 +206 1972-03-01 NaN 1.387933 +207 1972-04-01 NaN 1.987933 +208 1972-05-01 NaN 2.331900 +209 1972-06-01 NaN 3.126846 +210 1972-07-01 NaN 3.748639 +211 1972-08-01 NaN 3.887933 +212 1972-09-01 NaN 3.731900 +213 1972-10-01 NaN 3.709917 +214 1972-11-01 NaN 2.720813 +215 1972-12-01 NaN 1.523150 + + + +{ + "Ozone": { + "Dataset": { + "Signal": "Ozone", + "Time": { + "Horizon": 12, + "TimeMinMax": [ + "1955-01-01 00:00:00", + "1971-12-01 00:00:00" + ], + "TimeVariable": "Time" + }, + "Training_Signal_Length": 204 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR", + "Cycle": "Seasonal_MonthOfYear", + "Signal_Transoformation": "NoTransf", + "Trend": "LinearTrend" + }, + "Model_Performance": { + "COMPLEXITY": "20", + "MAE": "0.6313296922866238", + "MAPE": "0.2035", + "MASE": "0.8599", + "RMSE": "0.7629312224974826" + } + } +} + + + + + + +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":1.8,"194":2.2,"195":3.0,"196":2.4,"197":2.4,"198":3.5,"199":3.5,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.2439666329,"193":1.1101068742,"194":1.5272270456,"195":2.1272270456,"196":2.4711936785,"197":3.2661402414,"198":3.8879332657,"199":4.0272270456,"200":3.8711936785,"201":3.849210362,"202":2.8601068742,"203":1.6624433007,"204":1.1050534371,"205":0.9711936785,"206":1.3879332657,"207":1.9879332657,"208":2.3318998986,"209":3.1268464615,"210":3.7486394859,"211":3.8879332657,"212":3.7318998986,"213":3.7099165822,"214":2.7208130944,"215":1.5231495209}} + + + diff --git a/tests/references/missing_data_test_missing_data_ozone_generic.log b/tests/references/missing_data_test_missing_data_ozone_generic.log new file mode 100644 index 000000000..e69de29bb From d19e582965b9bc6fc3f96d22aecb31f25dd2d200 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 22:19:51 +0200 Subject: [PATCH 10/15] Split some very time consuming tests --- tests/perf/test_cycles_full_long_long.py | 13 +++--- tests/perf/test_cycles_full_long_long_2.py | 52 ---------------------- tests/perf/test_ozone_ar_speed_many.py | 11 ++++- 3 files changed, 16 insertions(+), 60 deletions(-) delete mode 100644 tests/perf/test_cycles_full_long_long_2.py diff --git a/tests/perf/test_cycles_full_long_long.py b/tests/perf/test_cycles_full_long_long.py index 575167b63..0ece05a10 100644 --- a/tests/perf/test_cycles_full_long_long.py +++ b/tests/perf/test_cycles_full_long_long.py @@ -7,12 +7,13 @@ #get_ipython().magic('matplotlib inline') +def test_nbrows_cycle(nbrows , cyc): -lValues = [ k for k in range(2,24, 4)]; -# lValues = lValues + [ k for k in range(24, 128, 8)]; -for cyc in lValues: - print("TEST_CYCLES_START", cyc) - b1 = tsds.generate_random_TS(N = 32000 , FREQ = 'H', seed = 0, trendtype = "constant", cycle_length = cyc, transform = "None", sigma = 0.1, exog_count = 0, ar_order=0); + # lValues = [ k for k in range(2,24, 4)]; + # lValues = lValues + [ k for k in range(24, 128, 8)]; + # for cyc in lValues: + print("TEST_CYCLES_START", nbrows, cyc) + b1 = tsds.generate_random_TS(N = nbrows , FREQ = 'H', seed = 0, trendtype = "constant", cycle_length = cyc, transform = "None", sigma = 0.1, exog_count = 0, ar_order=0); df = b1.mPastData # df.tail(10) @@ -21,7 +22,7 @@ # df.describe() lEngine = autof.cForecastEngine() - lEngine.mOptions.mCycleLengths = [ k for k in range(2,128) ]; + lEngine.mOptions.mCycleLengths = [ k for k in range(2, cyc * 4) ]; lEngine H = cyc * 2; diff --git a/tests/perf/test_cycles_full_long_long_2.py b/tests/perf/test_cycles_full_long_long_2.py deleted file mode 100644 index c289cfa2d..000000000 --- a/tests/perf/test_cycles_full_long_long_2.py +++ /dev/null @@ -1,52 +0,0 @@ -import pandas as pd -import numpy as np - -import pyaf.ForecastEngine as autof -import pyaf.Bench.TS_datasets as tsds - - -#get_ipython().magic('matplotlib inline') - - -lValues = [ 2 ]; -# lValues = lValues + [ k for k in range(24, 128, 8)]; -for nbrows in range(1000,32000, 1000): - cyc = lValues[0] - print("TEST_CYCLES_START", nbrows, cyc) - b1 = tsds.generate_random_TS(N = nbrows , FREQ = 'H', seed = 0, trendtype = "constant", cycle_length = cyc, transform = "None", sigma = 0.1, exog_count = 0, ar_order=0); - df = b1.mPastData - - # df.tail(10) - # df[:-10].tail() - # df[:-10:-1] - # df.describe() - - lEngine = autof.cForecastEngine() - lEngine.mOptions.mCycleLengths = [ k for k in range(2,128) ]; - lEngine - - H = cyc * 2; - lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H); - lEngine.getModelInfo(); - - lEngine.mSignalDecomposition.mBestModel.mTimeInfo.mResolution - - dfapp_in = df.copy(); - dfapp_in.tail() - - # H = 12 - dfapp_out = lEngine.forecast(dfapp_in, H); - dfapp_out.tail(2 * H) - print("Forecast Columns " , dfapp_out.columns); - Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']] - print(Forecast_DF.info()) - print("Forecasts\n" , Forecast_DF.tail(H).values); - - print("\n\n") - print(lEngine.to_json()); - print("\n\n") - print("\n\n") - print(Forecast_DF.tail(H).to_json(date_format='iso')) - print("\n\n") - - print("TEST_CYCLES_END", cyc) diff --git a/tests/perf/test_ozone_ar_speed_many.py b/tests/perf/test_ozone_ar_speed_many.py index 76d4f5874..226a92edd 100644 --- a/tests/perf/test_ozone_ar_speed_many.py +++ b/tests/perf/test_ozone_ar_speed_many.py @@ -39,7 +39,7 @@ def buildModel(df, ar_order, H): lEngine.getModelInfo(); -def run_test(): +def run_test___disabled___(): df = b1.mPastData df1 = replicate(df, 50); df1.head() @@ -47,5 +47,12 @@ def run_test(): for order in np.arange(0, 1000, 50): buildModel(df1 , int(order) , H) +def run_test(order): + df = b1.mPastData + df1 = replicate(df, 50); + df1.head() + H = b1.mHorizon; + buildModel(df1 , int(order) , H) -run_test() +# disabled +# run_test() From 5bdcec430ddcf555ca9518edaf41677913bdb342 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 22:20:39 +0200 Subject: [PATCH 11/15] Split some very time consuming tests --- tests/perf/gen_ar_speed_tests.py | 12 ++++++++++++ tests/perf/gen_long_cycles_tests.py | 13 +++++++++++++ ...test_long_cycles_nbrows_cycle_length_1000_140.py | 4 ++++ .../test_long_cycles_nbrows_cycle_length_1000_20.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_1000_200.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_1000_260.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_1000_320.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_1000_380.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_1000_440.py | 4 ++++ .../test_long_cycles_nbrows_cycle_length_1000_80.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_11000_140.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_11000_20.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_11000_200.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_11000_260.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_11000_320.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_11000_380.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_11000_440.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_11000_80.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_21000_140.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_21000_20.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_21000_200.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_21000_260.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_21000_320.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_21000_380.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_21000_440.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_21000_80.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_31000_140.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_31000_20.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_31000_200.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_31000_260.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_31000_320.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_31000_380.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_31000_440.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_31000_80.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_41000_140.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_41000_20.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_41000_200.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_41000_260.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_41000_320.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_41000_380.py | 4 ++++ ...est_long_cycles_nbrows_cycle_length_41000_440.py | 4 ++++ ...test_long_cycles_nbrows_cycle_length_41000_80.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_0.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_100.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_150.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_200.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_250.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_300.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_350.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_400.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_450.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_50.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_500.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_550.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_600.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_650.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_700.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_750.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_800.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_850.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_900.py | 4 ++++ tests/perf/test_ozone_ar_speed_order_950.py | 4 ++++ 62 files changed, 265 insertions(+) create mode 100644 tests/perf/gen_ar_speed_tests.py create mode 100644 tests/perf/gen_long_cycles_tests.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_1000_140.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_1000_20.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_1000_200.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_1000_260.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_1000_320.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_1000_380.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_1000_440.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_1000_80.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_11000_140.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_11000_20.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_11000_200.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_11000_260.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_11000_320.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_11000_380.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_11000_440.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_11000_80.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_21000_140.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_21000_20.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_21000_200.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_21000_260.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_21000_320.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_21000_380.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_21000_440.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_21000_80.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_31000_140.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_31000_20.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_31000_200.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_31000_260.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_31000_320.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_31000_380.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_31000_440.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_31000_80.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_41000_140.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_41000_20.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_41000_200.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_41000_260.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_41000_320.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_41000_380.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_41000_440.py create mode 100644 tests/perf/test_long_cycles_nbrows_cycle_length_41000_80.py create mode 100644 tests/perf/test_ozone_ar_speed_order_0.py create mode 100644 tests/perf/test_ozone_ar_speed_order_100.py create mode 100644 tests/perf/test_ozone_ar_speed_order_150.py create mode 100644 tests/perf/test_ozone_ar_speed_order_200.py create mode 100644 tests/perf/test_ozone_ar_speed_order_250.py create mode 100644 tests/perf/test_ozone_ar_speed_order_300.py create mode 100644 tests/perf/test_ozone_ar_speed_order_350.py create mode 100644 tests/perf/test_ozone_ar_speed_order_400.py create mode 100644 tests/perf/test_ozone_ar_speed_order_450.py create mode 100644 tests/perf/test_ozone_ar_speed_order_50.py create mode 100644 tests/perf/test_ozone_ar_speed_order_500.py create mode 100644 tests/perf/test_ozone_ar_speed_order_550.py create mode 100644 tests/perf/test_ozone_ar_speed_order_600.py create mode 100644 tests/perf/test_ozone_ar_speed_order_650.py create mode 100644 tests/perf/test_ozone_ar_speed_order_700.py create mode 100644 tests/perf/test_ozone_ar_speed_order_750.py create mode 100644 tests/perf/test_ozone_ar_speed_order_800.py create mode 100644 tests/perf/test_ozone_ar_speed_order_850.py create mode 100644 tests/perf/test_ozone_ar_speed_order_900.py create mode 100644 tests/perf/test_ozone_ar_speed_order_950.py diff --git a/tests/perf/gen_ar_speed_tests.py b/tests/perf/gen_ar_speed_tests.py new file mode 100644 index 000000000..63647ea0a --- /dev/null +++ b/tests/perf/gen_ar_speed_tests.py @@ -0,0 +1,12 @@ +import numpy as np + +def gen_all_ar_speed_tests(): + lDir = "tests/perf" + for order in np.arange(0, 1000, 50): + filename = lDir + "/test_ozone_ar_speed_order_" + str(order) + ".py"; + with open(filename, "w") as outfile: + print("WRTITING_FILE" , filename) + outfile.write("import tests.perf.test_ozone_ar_speed_many as gen\n\n") + outfile.write("gen.run_test(" + str(order) + ")\n\n") + +# gen_all_ar_speed_tests() diff --git a/tests/perf/gen_long_cycles_tests.py b/tests/perf/gen_long_cycles_tests.py new file mode 100644 index 000000000..f3a477fc0 --- /dev/null +++ b/tests/perf/gen_long_cycles_tests.py @@ -0,0 +1,13 @@ +import numpy as np + +def gen_all_long_cycles_tests(): + lDir = "tests/perf" + for nbrows in range(1000,42000, 10000): + for cyc in [ k for k in range(20 ,500, 60)]: + filename = lDir + "/test_long_cycles_nbrows_cycle_length_" + str(nbrows) + "_" + str(cyc) + ".py"; + with open(filename, "w") as outfile: + print("WRTITING_FILE" , filename) + outfile.write("import tests.perf.test_cycles_full_long_long as gen\n\n") + outfile.write("gen.test_nbrows_cycle(" + str(nbrows) + " , " + str(cyc) + ")\n\n") + +# gen_all_long_cycles_tests() diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_1000_140.py b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_140.py new file mode 100644 index 000000000..657275545 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_140.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(1000 , 140) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_1000_20.py b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_20.py new file mode 100644 index 000000000..984682a84 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_20.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(1000 , 20) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_1000_200.py b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_200.py new file mode 100644 index 000000000..e9d8e02a7 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_200.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(1000 , 200) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_1000_260.py b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_260.py new file mode 100644 index 000000000..2cccd8887 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_260.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(1000 , 260) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_1000_320.py b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_320.py new file mode 100644 index 000000000..938a22901 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_320.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(1000 , 320) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_1000_380.py b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_380.py new file mode 100644 index 000000000..481ad0fbe --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_380.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(1000 , 380) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_1000_440.py b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_440.py new file mode 100644 index 000000000..681789ee7 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_440.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(1000 , 440) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_1000_80.py b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_80.py new file mode 100644 index 000000000..453b26191 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_1000_80.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(1000 , 80) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_11000_140.py b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_140.py new file mode 100644 index 000000000..2faa8f672 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_140.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(11000 , 140) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_11000_20.py b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_20.py new file mode 100644 index 000000000..77c3631a5 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_20.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(11000 , 20) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_11000_200.py b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_200.py new file mode 100644 index 000000000..3973d115d --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_200.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(11000 , 200) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_11000_260.py b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_260.py new file mode 100644 index 000000000..0f606f81d --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_260.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(11000 , 260) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_11000_320.py b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_320.py new file mode 100644 index 000000000..7c6c43047 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_320.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(11000 , 320) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_11000_380.py b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_380.py new file mode 100644 index 000000000..abf6070ca --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_380.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(11000 , 380) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_11000_440.py b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_440.py new file mode 100644 index 000000000..00545bb7c --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_440.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(11000 , 440) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_11000_80.py b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_80.py new file mode 100644 index 000000000..3c1732536 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_11000_80.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(11000 , 80) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_21000_140.py b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_140.py new file mode 100644 index 000000000..66faeb09b --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_140.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(21000 , 140) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_21000_20.py b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_20.py new file mode 100644 index 000000000..ca2e50f66 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_20.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(21000 , 20) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_21000_200.py b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_200.py new file mode 100644 index 000000000..2e343d809 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_200.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(21000 , 200) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_21000_260.py b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_260.py new file mode 100644 index 000000000..a0e4eba47 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_260.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(21000 , 260) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_21000_320.py b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_320.py new file mode 100644 index 000000000..72754c388 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_320.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(21000 , 320) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_21000_380.py b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_380.py new file mode 100644 index 000000000..6d06dc05a --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_380.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(21000 , 380) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_21000_440.py b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_440.py new file mode 100644 index 000000000..6c61e0872 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_440.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(21000 , 440) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_21000_80.py b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_80.py new file mode 100644 index 000000000..ae070206d --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_21000_80.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(21000 , 80) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_31000_140.py b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_140.py new file mode 100644 index 000000000..d3997dc69 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_140.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(31000 , 140) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_31000_20.py b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_20.py new file mode 100644 index 000000000..c314b84a5 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_20.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(31000 , 20) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_31000_200.py b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_200.py new file mode 100644 index 000000000..d21c90df8 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_200.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(31000 , 200) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_31000_260.py b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_260.py new file mode 100644 index 000000000..70100c959 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_260.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(31000 , 260) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_31000_320.py b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_320.py new file mode 100644 index 000000000..b92d17793 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_320.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(31000 , 320) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_31000_380.py b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_380.py new file mode 100644 index 000000000..11919f179 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_380.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(31000 , 380) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_31000_440.py b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_440.py new file mode 100644 index 000000000..c4cc864b1 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_440.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(31000 , 440) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_31000_80.py b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_80.py new file mode 100644 index 000000000..93292ad17 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_31000_80.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(31000 , 80) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_41000_140.py b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_140.py new file mode 100644 index 000000000..0c2a69918 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_140.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(41000 , 140) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_41000_20.py b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_20.py new file mode 100644 index 000000000..43c2416c3 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_20.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(41000 , 20) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_41000_200.py b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_200.py new file mode 100644 index 000000000..af824d71a --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_200.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(41000 , 200) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_41000_260.py b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_260.py new file mode 100644 index 000000000..060da7bb4 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_260.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(41000 , 260) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_41000_320.py b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_320.py new file mode 100644 index 000000000..b0a100883 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_320.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(41000 , 320) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_41000_380.py b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_380.py new file mode 100644 index 000000000..27ffc9007 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_380.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(41000 , 380) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_41000_440.py b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_440.py new file mode 100644 index 000000000..82e9f3cf5 --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_440.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(41000 , 440) + diff --git a/tests/perf/test_long_cycles_nbrows_cycle_length_41000_80.py b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_80.py new file mode 100644 index 000000000..86591bf6d --- /dev/null +++ b/tests/perf/test_long_cycles_nbrows_cycle_length_41000_80.py @@ -0,0 +1,4 @@ +import tests.perf.test_cycles_full_long_long as gen + +gen.test_nbrows_cycle(41000 , 80) + diff --git a/tests/perf/test_ozone_ar_speed_order_0.py b/tests/perf/test_ozone_ar_speed_order_0.py new file mode 100644 index 000000000..5a63c7ffe --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_0.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(0) + diff --git a/tests/perf/test_ozone_ar_speed_order_100.py b/tests/perf/test_ozone_ar_speed_order_100.py new file mode 100644 index 000000000..38a080dcb --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_100.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(100) + diff --git a/tests/perf/test_ozone_ar_speed_order_150.py b/tests/perf/test_ozone_ar_speed_order_150.py new file mode 100644 index 000000000..1cb63beec --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_150.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(150) + diff --git a/tests/perf/test_ozone_ar_speed_order_200.py b/tests/perf/test_ozone_ar_speed_order_200.py new file mode 100644 index 000000000..b09d5a841 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_200.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(200) + diff --git a/tests/perf/test_ozone_ar_speed_order_250.py b/tests/perf/test_ozone_ar_speed_order_250.py new file mode 100644 index 000000000..82ba4417f --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_250.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(250) + diff --git a/tests/perf/test_ozone_ar_speed_order_300.py b/tests/perf/test_ozone_ar_speed_order_300.py new file mode 100644 index 000000000..1bc110ec7 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_300.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(300) + diff --git a/tests/perf/test_ozone_ar_speed_order_350.py b/tests/perf/test_ozone_ar_speed_order_350.py new file mode 100644 index 000000000..661e4fbb2 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_350.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(350) + diff --git a/tests/perf/test_ozone_ar_speed_order_400.py b/tests/perf/test_ozone_ar_speed_order_400.py new file mode 100644 index 000000000..d3e9238a2 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_400.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(400) + diff --git a/tests/perf/test_ozone_ar_speed_order_450.py b/tests/perf/test_ozone_ar_speed_order_450.py new file mode 100644 index 000000000..47f9cc67e --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_450.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(450) + diff --git a/tests/perf/test_ozone_ar_speed_order_50.py b/tests/perf/test_ozone_ar_speed_order_50.py new file mode 100644 index 000000000..c3186a8e9 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_50.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(50) + diff --git a/tests/perf/test_ozone_ar_speed_order_500.py b/tests/perf/test_ozone_ar_speed_order_500.py new file mode 100644 index 000000000..9b9b1af40 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_500.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(500) + diff --git a/tests/perf/test_ozone_ar_speed_order_550.py b/tests/perf/test_ozone_ar_speed_order_550.py new file mode 100644 index 000000000..0bcbfa8e5 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_550.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(550) + diff --git a/tests/perf/test_ozone_ar_speed_order_600.py b/tests/perf/test_ozone_ar_speed_order_600.py new file mode 100644 index 000000000..88d0ec879 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_600.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(600) + diff --git a/tests/perf/test_ozone_ar_speed_order_650.py b/tests/perf/test_ozone_ar_speed_order_650.py new file mode 100644 index 000000000..c3bb730f9 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_650.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(650) + diff --git a/tests/perf/test_ozone_ar_speed_order_700.py b/tests/perf/test_ozone_ar_speed_order_700.py new file mode 100644 index 000000000..e45f6cc6f --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_700.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(700) + diff --git a/tests/perf/test_ozone_ar_speed_order_750.py b/tests/perf/test_ozone_ar_speed_order_750.py new file mode 100644 index 000000000..a5c1e96cb --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_750.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(750) + diff --git a/tests/perf/test_ozone_ar_speed_order_800.py b/tests/perf/test_ozone_ar_speed_order_800.py new file mode 100644 index 000000000..b6e87c831 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_800.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(800) + diff --git a/tests/perf/test_ozone_ar_speed_order_850.py b/tests/perf/test_ozone_ar_speed_order_850.py new file mode 100644 index 000000000..c248776b5 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_850.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(850) + diff --git a/tests/perf/test_ozone_ar_speed_order_900.py b/tests/perf/test_ozone_ar_speed_order_900.py new file mode 100644 index 000000000..e4b033cda --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_900.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(900) + diff --git a/tests/perf/test_ozone_ar_speed_order_950.py b/tests/perf/test_ozone_ar_speed_order_950.py new file mode 100644 index 000000000..06f29e385 --- /dev/null +++ b/tests/perf/test_ozone_ar_speed_order_950.py @@ -0,0 +1,4 @@ +import tests.perf.test_ozone_ar_speed_many as gen + +gen.run_test(950) + From b46b70ded217880606aff625af650bd1ef3620f7 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Wed, 29 Jul 2020 22:21:00 +0200 Subject: [PATCH 12/15] Split some very time consuming tests --- tests/Makefile | 315 ++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 310 insertions(+), 5 deletions(-) diff --git a/tests/Makefile b/tests/Makefile index eb3f4c765..a1f07b151 100644 --- a/tests/Makefile +++ b/tests/Makefile @@ -1221,6 +1221,16 @@ neuralnet/test_ozone_tensorflow.py : neuralnet : neuralnet/test_ozone_tensorflow.py neuralnet/test_ozone_rnn_only_MLP.py neuralnet/test_ozone_rnn_only_LSTM.py neuralnet/test_ozone_rnn_only.py neuralnet/test_ozone__GPU_theano.py neuralnet/test_ozone__GPU_tensorflow.py neuralnet/test_ozone__CPU_theano.py neuralnet/test_ozone_GPU.py neuralnet/test_air_passengers_tensorflow.py neuralnet/test_air_passengers_rnn_only.py neuralnet/test_air_passengers_GPU_theano.py neuralnet/test_air_passengers_GPU_tensorflow.py neuralnet/test_air_passengers_GPU.py neuralnet/test_air_passengers_CPU_theano.py +perf/gen_ar_speed_tests.py : + $(PYTHON) tests/perf/gen_ar_speed_tests.py > logs/perf_gen_ar_speed_tests.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_gen_ar_speed_tests.log logs/perf_gen_ar_speed_tests.log > logs/perf_gen_ar_speed_tests.log.diff + tail -10 logs/perf_gen_ar_speed_tests.log.diff + +perf/gen_long_cycles_tests.py : + $(PYTHON) tests/perf/gen_long_cycles_tests.py > logs/perf_gen_long_cycles_tests.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_gen_long_cycles_tests.log logs/perf_gen_long_cycles_tests.log > logs/perf_gen_long_cycles_tests.log.diff + tail -10 logs/perf_gen_long_cycles_tests.log.diff + perf/test_cycles_full_long.py : $(PYTHON) tests/perf/test_cycles_full_long.py > logs/perf_test_cycles_full_long.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/perf_test_cycles_full_long.log logs/perf_test_cycles_full_long.log > logs/perf_test_cycles_full_long.log.diff @@ -1231,10 +1241,205 @@ perf/test_cycles_full_long_long.py : $(PYTHON) scripts/num_diff.py tests/references/perf_test_cycles_full_long_long.log logs/perf_test_cycles_full_long_long.log > logs/perf_test_cycles_full_long_long.log.diff tail -10 logs/perf_test_cycles_full_long_long.log.diff -perf/test_cycles_full_long_long_2.py : - $(PYTHON) tests/perf/test_cycles_full_long_long_2.py > logs/perf_test_cycles_full_long_long_2.log 2>&1 - $(PYTHON) scripts/num_diff.py tests/references/perf_test_cycles_full_long_long_2.log logs/perf_test_cycles_full_long_long_2.log > logs/perf_test_cycles_full_long_long_2.log.diff - tail -10 logs/perf_test_cycles_full_long_long_2.log.diff +perf/test_long_cycles_nbrows_cycle_length_1000_140.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_1000_140.py > logs/perf_test_long_cycles_nbrows_cycle_length_1000_140.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_140.log logs/perf_test_long_cycles_nbrows_cycle_length_1000_140.log > logs/perf_test_long_cycles_nbrows_cycle_length_1000_140.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_1000_140.log.diff + +perf/test_long_cycles_nbrows_cycle_length_1000_20.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_1000_20.py > logs/perf_test_long_cycles_nbrows_cycle_length_1000_20.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_20.log logs/perf_test_long_cycles_nbrows_cycle_length_1000_20.log > logs/perf_test_long_cycles_nbrows_cycle_length_1000_20.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_1000_20.log.diff + +perf/test_long_cycles_nbrows_cycle_length_1000_200.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_1000_200.py > logs/perf_test_long_cycles_nbrows_cycle_length_1000_200.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_200.log logs/perf_test_long_cycles_nbrows_cycle_length_1000_200.log > logs/perf_test_long_cycles_nbrows_cycle_length_1000_200.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_1000_200.log.diff + +perf/test_long_cycles_nbrows_cycle_length_1000_260.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_1000_260.py > logs/perf_test_long_cycles_nbrows_cycle_length_1000_260.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_260.log logs/perf_test_long_cycles_nbrows_cycle_length_1000_260.log > logs/perf_test_long_cycles_nbrows_cycle_length_1000_260.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_1000_260.log.diff + +perf/test_long_cycles_nbrows_cycle_length_1000_320.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_1000_320.py > logs/perf_test_long_cycles_nbrows_cycle_length_1000_320.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_320.log logs/perf_test_long_cycles_nbrows_cycle_length_1000_320.log > logs/perf_test_long_cycles_nbrows_cycle_length_1000_320.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_1000_320.log.diff + +perf/test_long_cycles_nbrows_cycle_length_1000_380.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_1000_380.py > logs/perf_test_long_cycles_nbrows_cycle_length_1000_380.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_380.log logs/perf_test_long_cycles_nbrows_cycle_length_1000_380.log > logs/perf_test_long_cycles_nbrows_cycle_length_1000_380.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_1000_380.log.diff + +perf/test_long_cycles_nbrows_cycle_length_1000_440.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_1000_440.py > logs/perf_test_long_cycles_nbrows_cycle_length_1000_440.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_440.log logs/perf_test_long_cycles_nbrows_cycle_length_1000_440.log > logs/perf_test_long_cycles_nbrows_cycle_length_1000_440.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_1000_440.log.diff + +perf/test_long_cycles_nbrows_cycle_length_1000_80.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_1000_80.py > logs/perf_test_long_cycles_nbrows_cycle_length_1000_80.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_80.log logs/perf_test_long_cycles_nbrows_cycle_length_1000_80.log > logs/perf_test_long_cycles_nbrows_cycle_length_1000_80.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_1000_80.log.diff + +perf/test_long_cycles_nbrows_cycle_length_11000_140.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_11000_140.py > logs/perf_test_long_cycles_nbrows_cycle_length_11000_140.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_140.log logs/perf_test_long_cycles_nbrows_cycle_length_11000_140.log > logs/perf_test_long_cycles_nbrows_cycle_length_11000_140.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_11000_140.log.diff + +perf/test_long_cycles_nbrows_cycle_length_11000_20.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_11000_20.py > logs/perf_test_long_cycles_nbrows_cycle_length_11000_20.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_20.log logs/perf_test_long_cycles_nbrows_cycle_length_11000_20.log > logs/perf_test_long_cycles_nbrows_cycle_length_11000_20.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_11000_20.log.diff + +perf/test_long_cycles_nbrows_cycle_length_11000_200.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_11000_200.py > logs/perf_test_long_cycles_nbrows_cycle_length_11000_200.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_200.log logs/perf_test_long_cycles_nbrows_cycle_length_11000_200.log > logs/perf_test_long_cycles_nbrows_cycle_length_11000_200.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_11000_200.log.diff + +perf/test_long_cycles_nbrows_cycle_length_11000_260.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_11000_260.py > logs/perf_test_long_cycles_nbrows_cycle_length_11000_260.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_260.log logs/perf_test_long_cycles_nbrows_cycle_length_11000_260.log > logs/perf_test_long_cycles_nbrows_cycle_length_11000_260.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_11000_260.log.diff + +perf/test_long_cycles_nbrows_cycle_length_11000_320.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_11000_320.py > logs/perf_test_long_cycles_nbrows_cycle_length_11000_320.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_320.log logs/perf_test_long_cycles_nbrows_cycle_length_11000_320.log > logs/perf_test_long_cycles_nbrows_cycle_length_11000_320.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_11000_320.log.diff + +perf/test_long_cycles_nbrows_cycle_length_11000_380.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_11000_380.py > logs/perf_test_long_cycles_nbrows_cycle_length_11000_380.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_380.log logs/perf_test_long_cycles_nbrows_cycle_length_11000_380.log > logs/perf_test_long_cycles_nbrows_cycle_length_11000_380.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_11000_380.log.diff + +perf/test_long_cycles_nbrows_cycle_length_11000_440.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_11000_440.py > logs/perf_test_long_cycles_nbrows_cycle_length_11000_440.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_440.log logs/perf_test_long_cycles_nbrows_cycle_length_11000_440.log > logs/perf_test_long_cycles_nbrows_cycle_length_11000_440.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_11000_440.log.diff + +perf/test_long_cycles_nbrows_cycle_length_11000_80.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_11000_80.py > logs/perf_test_long_cycles_nbrows_cycle_length_11000_80.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_80.log logs/perf_test_long_cycles_nbrows_cycle_length_11000_80.log > logs/perf_test_long_cycles_nbrows_cycle_length_11000_80.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_11000_80.log.diff + +perf/test_long_cycles_nbrows_cycle_length_21000_140.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_21000_140.py > logs/perf_test_long_cycles_nbrows_cycle_length_21000_140.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_140.log logs/perf_test_long_cycles_nbrows_cycle_length_21000_140.log > logs/perf_test_long_cycles_nbrows_cycle_length_21000_140.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_21000_140.log.diff + +perf/test_long_cycles_nbrows_cycle_length_21000_20.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_21000_20.py > logs/perf_test_long_cycles_nbrows_cycle_length_21000_20.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_20.log logs/perf_test_long_cycles_nbrows_cycle_length_21000_20.log > logs/perf_test_long_cycles_nbrows_cycle_length_21000_20.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_21000_20.log.diff + +perf/test_long_cycles_nbrows_cycle_length_21000_200.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_21000_200.py > logs/perf_test_long_cycles_nbrows_cycle_length_21000_200.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_200.log 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$(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_440.log logs/perf_test_long_cycles_nbrows_cycle_length_21000_440.log > logs/perf_test_long_cycles_nbrows_cycle_length_21000_440.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_21000_440.log.diff + +perf/test_long_cycles_nbrows_cycle_length_21000_80.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_21000_80.py > logs/perf_test_long_cycles_nbrows_cycle_length_21000_80.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_80.log logs/perf_test_long_cycles_nbrows_cycle_length_21000_80.log > logs/perf_test_long_cycles_nbrows_cycle_length_21000_80.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_21000_80.log.diff + +perf/test_long_cycles_nbrows_cycle_length_31000_140.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_31000_140.py > 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+perf/test_long_cycles_nbrows_cycle_length_31000_320.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_31000_320.py > logs/perf_test_long_cycles_nbrows_cycle_length_31000_320.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_320.log logs/perf_test_long_cycles_nbrows_cycle_length_31000_320.log > logs/perf_test_long_cycles_nbrows_cycle_length_31000_320.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_31000_320.log.diff + +perf/test_long_cycles_nbrows_cycle_length_31000_380.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_31000_380.py > logs/perf_test_long_cycles_nbrows_cycle_length_31000_380.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_380.log logs/perf_test_long_cycles_nbrows_cycle_length_31000_380.log > logs/perf_test_long_cycles_nbrows_cycle_length_31000_380.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_31000_380.log.diff + +perf/test_long_cycles_nbrows_cycle_length_31000_440.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_31000_440.py > logs/perf_test_long_cycles_nbrows_cycle_length_31000_440.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_440.log logs/perf_test_long_cycles_nbrows_cycle_length_31000_440.log > logs/perf_test_long_cycles_nbrows_cycle_length_31000_440.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_31000_440.log.diff + +perf/test_long_cycles_nbrows_cycle_length_31000_80.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_31000_80.py > logs/perf_test_long_cycles_nbrows_cycle_length_31000_80.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_80.log logs/perf_test_long_cycles_nbrows_cycle_length_31000_80.log > logs/perf_test_long_cycles_nbrows_cycle_length_31000_80.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_31000_80.log.diff + +perf/test_long_cycles_nbrows_cycle_length_41000_140.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_41000_140.py > logs/perf_test_long_cycles_nbrows_cycle_length_41000_140.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_140.log logs/perf_test_long_cycles_nbrows_cycle_length_41000_140.log > logs/perf_test_long_cycles_nbrows_cycle_length_41000_140.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_41000_140.log.diff + +perf/test_long_cycles_nbrows_cycle_length_41000_20.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_41000_20.py > logs/perf_test_long_cycles_nbrows_cycle_length_41000_20.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_20.log logs/perf_test_long_cycles_nbrows_cycle_length_41000_20.log > logs/perf_test_long_cycles_nbrows_cycle_length_41000_20.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_41000_20.log.diff + +perf/test_long_cycles_nbrows_cycle_length_41000_200.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_41000_200.py > logs/perf_test_long_cycles_nbrows_cycle_length_41000_200.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_200.log logs/perf_test_long_cycles_nbrows_cycle_length_41000_200.log > logs/perf_test_long_cycles_nbrows_cycle_length_41000_200.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_41000_200.log.diff + +perf/test_long_cycles_nbrows_cycle_length_41000_260.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_41000_260.py > logs/perf_test_long_cycles_nbrows_cycle_length_41000_260.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_260.log logs/perf_test_long_cycles_nbrows_cycle_length_41000_260.log > logs/perf_test_long_cycles_nbrows_cycle_length_41000_260.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_41000_260.log.diff + +perf/test_long_cycles_nbrows_cycle_length_41000_320.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_41000_320.py > logs/perf_test_long_cycles_nbrows_cycle_length_41000_320.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_320.log logs/perf_test_long_cycles_nbrows_cycle_length_41000_320.log > logs/perf_test_long_cycles_nbrows_cycle_length_41000_320.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_41000_320.log.diff + +perf/test_long_cycles_nbrows_cycle_length_41000_380.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_41000_380.py > logs/perf_test_long_cycles_nbrows_cycle_length_41000_380.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_380.log logs/perf_test_long_cycles_nbrows_cycle_length_41000_380.log > logs/perf_test_long_cycles_nbrows_cycle_length_41000_380.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_41000_380.log.diff + +perf/test_long_cycles_nbrows_cycle_length_41000_440.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_41000_440.py > logs/perf_test_long_cycles_nbrows_cycle_length_41000_440.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_440.log logs/perf_test_long_cycles_nbrows_cycle_length_41000_440.log > logs/perf_test_long_cycles_nbrows_cycle_length_41000_440.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_41000_440.log.diff + +perf/test_long_cycles_nbrows_cycle_length_41000_80.py : + $(PYTHON) tests/perf/test_long_cycles_nbrows_cycle_length_41000_80.py > logs/perf_test_long_cycles_nbrows_cycle_length_41000_80.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_80.log logs/perf_test_long_cycles_nbrows_cycle_length_41000_80.log > logs/perf_test_long_cycles_nbrows_cycle_length_41000_80.log.diff + tail -10 logs/perf_test_long_cycles_nbrows_cycle_length_41000_80.log.diff perf/test_ozone_ar_speed.py : $(PYTHON) tests/perf/test_ozone_ar_speed.py > logs/perf_test_ozone_ar_speed.log 2>&1 @@ -1246,6 +1451,106 @@ perf/test_ozone_ar_speed_many.py : $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_many.log logs/perf_test_ozone_ar_speed_many.log > logs/perf_test_ozone_ar_speed_many.log.diff tail -10 logs/perf_test_ozone_ar_speed_many.log.diff +perf/test_ozone_ar_speed_order_0.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_0.py > logs/perf_test_ozone_ar_speed_order_0.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_0.log logs/perf_test_ozone_ar_speed_order_0.log > logs/perf_test_ozone_ar_speed_order_0.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_0.log.diff + +perf/test_ozone_ar_speed_order_100.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_100.py > logs/perf_test_ozone_ar_speed_order_100.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_100.log logs/perf_test_ozone_ar_speed_order_100.log > logs/perf_test_ozone_ar_speed_order_100.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_100.log.diff + +perf/test_ozone_ar_speed_order_150.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_150.py > logs/perf_test_ozone_ar_speed_order_150.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_150.log logs/perf_test_ozone_ar_speed_order_150.log > logs/perf_test_ozone_ar_speed_order_150.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_150.log.diff + +perf/test_ozone_ar_speed_order_200.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_200.py > logs/perf_test_ozone_ar_speed_order_200.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_200.log logs/perf_test_ozone_ar_speed_order_200.log > logs/perf_test_ozone_ar_speed_order_200.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_200.log.diff + +perf/test_ozone_ar_speed_order_250.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_250.py > logs/perf_test_ozone_ar_speed_order_250.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_250.log logs/perf_test_ozone_ar_speed_order_250.log > logs/perf_test_ozone_ar_speed_order_250.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_250.log.diff + +perf/test_ozone_ar_speed_order_300.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_300.py > logs/perf_test_ozone_ar_speed_order_300.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_300.log logs/perf_test_ozone_ar_speed_order_300.log > logs/perf_test_ozone_ar_speed_order_300.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_300.log.diff + +perf/test_ozone_ar_speed_order_350.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_350.py > logs/perf_test_ozone_ar_speed_order_350.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_350.log logs/perf_test_ozone_ar_speed_order_350.log > logs/perf_test_ozone_ar_speed_order_350.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_350.log.diff + +perf/test_ozone_ar_speed_order_400.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_400.py > logs/perf_test_ozone_ar_speed_order_400.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_400.log logs/perf_test_ozone_ar_speed_order_400.log > logs/perf_test_ozone_ar_speed_order_400.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_400.log.diff + +perf/test_ozone_ar_speed_order_450.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_450.py > logs/perf_test_ozone_ar_speed_order_450.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_450.log logs/perf_test_ozone_ar_speed_order_450.log > logs/perf_test_ozone_ar_speed_order_450.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_450.log.diff + +perf/test_ozone_ar_speed_order_50.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_50.py > logs/perf_test_ozone_ar_speed_order_50.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_50.log logs/perf_test_ozone_ar_speed_order_50.log > logs/perf_test_ozone_ar_speed_order_50.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_50.log.diff + +perf/test_ozone_ar_speed_order_500.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_500.py > logs/perf_test_ozone_ar_speed_order_500.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_500.log logs/perf_test_ozone_ar_speed_order_500.log > logs/perf_test_ozone_ar_speed_order_500.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_500.log.diff + +perf/test_ozone_ar_speed_order_550.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_550.py > logs/perf_test_ozone_ar_speed_order_550.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_550.log logs/perf_test_ozone_ar_speed_order_550.log > logs/perf_test_ozone_ar_speed_order_550.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_550.log.diff + +perf/test_ozone_ar_speed_order_600.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_600.py > logs/perf_test_ozone_ar_speed_order_600.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_600.log logs/perf_test_ozone_ar_speed_order_600.log > logs/perf_test_ozone_ar_speed_order_600.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_600.log.diff + +perf/test_ozone_ar_speed_order_650.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_650.py > logs/perf_test_ozone_ar_speed_order_650.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_650.log logs/perf_test_ozone_ar_speed_order_650.log > logs/perf_test_ozone_ar_speed_order_650.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_650.log.diff + +perf/test_ozone_ar_speed_order_700.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_700.py > logs/perf_test_ozone_ar_speed_order_700.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_700.log logs/perf_test_ozone_ar_speed_order_700.log > logs/perf_test_ozone_ar_speed_order_700.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_700.log.diff + +perf/test_ozone_ar_speed_order_750.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_750.py > logs/perf_test_ozone_ar_speed_order_750.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_750.log logs/perf_test_ozone_ar_speed_order_750.log > logs/perf_test_ozone_ar_speed_order_750.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_750.log.diff + +perf/test_ozone_ar_speed_order_800.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_800.py > logs/perf_test_ozone_ar_speed_order_800.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_800.log logs/perf_test_ozone_ar_speed_order_800.log > logs/perf_test_ozone_ar_speed_order_800.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_800.log.diff + +perf/test_ozone_ar_speed_order_850.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_850.py > logs/perf_test_ozone_ar_speed_order_850.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_850.log logs/perf_test_ozone_ar_speed_order_850.log > logs/perf_test_ozone_ar_speed_order_850.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_850.log.diff + +perf/test_ozone_ar_speed_order_900.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_900.py > logs/perf_test_ozone_ar_speed_order_900.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_900.log logs/perf_test_ozone_ar_speed_order_900.log > logs/perf_test_ozone_ar_speed_order_900.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_900.log.diff + +perf/test_ozone_ar_speed_order_950.py : + $(PYTHON) tests/perf/test_ozone_ar_speed_order_950.py > logs/perf_test_ozone_ar_speed_order_950.log 2>&1 + $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_ar_speed_order_950.log logs/perf_test_ozone_ar_speed_order_950.log > logs/perf_test_ozone_ar_speed_order_950.log.diff + tail -10 logs/perf_test_ozone_ar_speed_order_950.log.diff + perf/test_ozone_debug_perf.py : $(PYTHON) tests/perf/test_ozone_debug_perf.py > logs/perf_test_ozone_debug_perf.log 2>&1 $(PYTHON) scripts/num_diff.py tests/references/perf_test_ozone_debug_perf.log logs/perf_test_ozone_debug_perf.log > logs/perf_test_ozone_debug_perf.log.diff @@ -1288,7 +1593,7 @@ perf/test_web-traffic-time-series-forecasting_all.py : - perf : perf/test_web-traffic-time-series-forecasting_all.py perf/test_web-traffic-time-series-forecasting.py perf/test_perf2.py perf/test_perf1.py perf/test_parallel.py perf/test_ozone_long_series_2.py perf/test_ozone_long_series.py perf/test_ozone_debug_perf.py perf/test_ozone_ar_speed_many.py perf/test_ozone_ar_speed.py perf/test_cycles_full_long_long_2.py perf/test_cycles_full_long_long.py perf/test_cycles_full_long.py + perf : perf/test_web-traffic-time-series-forecasting_all.py perf/test_web-traffic-time-series-forecasting.py perf/test_perf2.py perf/test_perf1.py perf/test_parallel.py perf/test_ozone_long_series_2.py perf/test_ozone_long_series.py perf/test_ozone_debug_perf.py perf/test_ozone_ar_speed_order_950.py perf/test_ozone_ar_speed_order_900.py perf/test_ozone_ar_speed_order_850.py perf/test_ozone_ar_speed_order_800.py perf/test_ozone_ar_speed_order_750.py perf/test_ozone_ar_speed_order_700.py perf/test_ozone_ar_speed_order_650.py perf/test_ozone_ar_speed_order_600.py perf/test_ozone_ar_speed_order_550.py perf/test_ozone_ar_speed_order_500.py perf/test_ozone_ar_speed_order_50.py perf/test_ozone_ar_speed_order_450.py perf/test_ozone_ar_speed_order_400.py perf/test_ozone_ar_speed_order_350.py perf/test_ozone_ar_speed_order_300.py perf/test_ozone_ar_speed_order_250.py perf/test_ozone_ar_speed_order_200.py perf/test_ozone_ar_speed_order_150.py perf/test_ozone_ar_speed_order_100.py perf/test_ozone_ar_speed_order_0.py perf/test_ozone_ar_speed_many.py perf/test_ozone_ar_speed.py perf/test_long_cycles_nbrows_cycle_length_41000_80.py perf/test_long_cycles_nbrows_cycle_length_41000_440.py perf/test_long_cycles_nbrows_cycle_length_41000_380.py perf/test_long_cycles_nbrows_cycle_length_41000_320.py perf/test_long_cycles_nbrows_cycle_length_41000_260.py perf/test_long_cycles_nbrows_cycle_length_41000_200.py perf/test_long_cycles_nbrows_cycle_length_41000_20.py perf/test_long_cycles_nbrows_cycle_length_41000_140.py perf/test_long_cycles_nbrows_cycle_length_31000_80.py perf/test_long_cycles_nbrows_cycle_length_31000_440.py perf/test_long_cycles_nbrows_cycle_length_31000_380.py perf/test_long_cycles_nbrows_cycle_length_31000_320.py perf/test_long_cycles_nbrows_cycle_length_31000_260.py perf/test_long_cycles_nbrows_cycle_length_31000_200.py perf/test_long_cycles_nbrows_cycle_length_31000_20.py perf/test_long_cycles_nbrows_cycle_length_31000_140.py perf/test_long_cycles_nbrows_cycle_length_21000_80.py perf/test_long_cycles_nbrows_cycle_length_21000_440.py perf/test_long_cycles_nbrows_cycle_length_21000_380.py perf/test_long_cycles_nbrows_cycle_length_21000_320.py perf/test_long_cycles_nbrows_cycle_length_21000_260.py perf/test_long_cycles_nbrows_cycle_length_21000_200.py perf/test_long_cycles_nbrows_cycle_length_21000_20.py perf/test_long_cycles_nbrows_cycle_length_21000_140.py perf/test_long_cycles_nbrows_cycle_length_11000_80.py perf/test_long_cycles_nbrows_cycle_length_11000_440.py perf/test_long_cycles_nbrows_cycle_length_11000_380.py perf/test_long_cycles_nbrows_cycle_length_11000_320.py perf/test_long_cycles_nbrows_cycle_length_11000_260.py perf/test_long_cycles_nbrows_cycle_length_11000_200.py perf/test_long_cycles_nbrows_cycle_length_11000_20.py perf/test_long_cycles_nbrows_cycle_length_11000_140.py perf/test_long_cycles_nbrows_cycle_length_1000_80.py perf/test_long_cycles_nbrows_cycle_length_1000_440.py perf/test_long_cycles_nbrows_cycle_length_1000_380.py perf/test_long_cycles_nbrows_cycle_length_1000_320.py perf/test_long_cycles_nbrows_cycle_length_1000_260.py perf/test_long_cycles_nbrows_cycle_length_1000_200.py perf/test_long_cycles_nbrows_cycle_length_1000_20.py perf/test_long_cycles_nbrows_cycle_length_1000_140.py perf/test_cycles_full_long_long.py perf/test_cycles_full_long.py perf/gen_long_cycles_tests.py perf/gen_ar_speed_tests.py perfs/test_ozone_perf_measure.py : From 65490a11fc9798293576cea3aad42d0ff7978f5d Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Thu, 30 Jul 2020 10:02:01 +0200 Subject: [PATCH 13/15] Updated these logs --- .../references/basic_checks_test_pearson.log | 2 +- tests/references/bugs_issue_29_test_mem_1.log | 74 +- .../bugs_issue_36_display_version_info.log | 2 +- .../bugs_issue_36_issue_36_version_info.log | 6 +- ...gs_issue_55_grouping_issue_55_notebook.log | 120 +- ...ue_58_issue_58_1_categorical_exogenous.log | 226 +- ...sue_69_issue_69_example_1_no_rectifier.log | 52 +- ...e_69_issue_69_example_1_with_rectifier.log | 50 +- tests/references/bugs_issue_94_issue_94.log | 74 +- ...uralnet_test_air_passengers_CPU_theano.log | 636 ++++-- ...net_test_air_passengers_GPU_tensorflow.log | 360 +-- ...uralnet_test_air_passengers_tensorflow.log | 108 +- .../neuralnet_test_ozone__CPU_theano.log | 969 ++++---- .../perf_test_cycles_full_long_long.log | 744 ------- tests/references/perf_test_ozone_ar_speed.log | 1956 ++++++++--------- .../perf_test_ozone_ar_speed_many.log | 754 ------- ...emporal_hierarchy_test_temporal_demo_1.log | 32 +- ...rarchy_test_temporal_demo_daily_D_W_2W.log | 32 +- ...rchy_test_temporal_demo_daily_D_W_2W_Q.log | 34 +- ...erarchy_test_temporal_demo_daily_D_W_M.log | 30 +- ...archy_test_temporal_demo_daily_D_W_M_Q.log | 30 +- ...erarchy_test_temporal_demo_daily_D_W_Q.log | 30 +- ...y_test_temporal_demo_hourly_H_6H_12H_D.log | 34 +- ...test_temporal_demo_hourly_H_6H_12H_D_W.log | 34 +- ...ierarchy_test_temporal_demo_hourly_H_D.log | 30 +- ...est_temporal_demo_minutely_T_10T_30T_H.log | 32 +- ..._test_temporal_demo_minutely_T_H_12H_D.log | 30 +- ...chy_test_temporal_demo_monthly_M_2M_6M.log | 30 +- ...test_temporal_demo_monthly_M_2M_6M_12M.log | 18 +- ...chy_test_temporal_demo_weekly_W_2W_M_Q.log | 32 +- ...rarchy_test_temporal_demo_weekly_W_Q_A.log | 30 +- 31 files changed, 2636 insertions(+), 3955 deletions(-) diff --git a/tests/references/basic_checks_test_pearson.log b/tests/references/basic_checks_test_pearson.log index b4b205a5b..63952ca32 100644 --- a/tests/references/basic_checks_test_pearson.log +++ b/tests/references/basic_checks_test_pearson.log @@ -1,2 +1,2 @@ -1.4.1 1.19.0 sys.version_info(major=3, minor=8, micro=4, releaselevel='final', serial=0) +1.4.1 1.19.1 sys.version_info(major=3, minor=8, micro=5, releaselevel='final', serial=0) (0.8660254037844386, 0.011724811003954649) (0.8660254037844386, 0.011724811003954649) diff --git a/tests/references/bugs_issue_29_test_mem_1.log b/tests/references/bugs_issue_29_test_mem_1.log index 5e1f52d8b..1c793398a 100644 --- a/tests/references/bugs_issue_29_test_mem_1.log +++ b/tests/references/bugs_issue_29_test_mem_1.log @@ -1,19 +1,19 @@ DISPLAY_USED_MEM_START function 11870 -dict 5386 -tuple 5335 +dict 5388 +tuple 5337 cell 2811 -wrapper_descriptor 2705 -method_descriptor 2036 -getset_descriptor 2000 -builtin_function_or_method 1960 -weakref 1918 +wrapper_descriptor 2712 +method_descriptor 2040 +getset_descriptor 2020 +builtin_function_or_method 1961 +weakref 1921 type 981 property 832 list 603 -module 506 +module 507 ModuleSpec 503 -member_descriptor 472 +member_descriptor 476 fused_cython_function 412 SourceFileLoader 390 classmethod 354 @@ -22,25 +22,25 @@ set 301 DISPLAY_USED_MEM_END DISPLAY_USED_MEM_START function 21693 -dict 10351 +dict 10353 tuple 9581 cell 6383 -weakref 3333 -getset_descriptor 3259 -wrapper_descriptor 3255 -method_descriptor 2761 -builtin_function_or_method 2665 +weakref 3336 +getset_descriptor 3279 +wrapper_descriptor 3262 +method_descriptor 2765 +builtin_function_or_method 2666 type 2002 list 1824 Parameter 1776 -module 1069 +module 1070 ModuleSpec 1066 property 1019 set 919 method 890 SourceFileLoader 857 fused_cython_function 578 -member_descriptor 518 +member_descriptor 522 DISPLAY_USED_MEM_END Month Ozone Time 0 1955-01 2.7 1955-01-01 @@ -49,26 +49,26 @@ DISPLAY_USED_MEM_END 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 DISPLAY_USED_MEM_START -function 22225 -dict 10604 -tuple 9813 +function 22226 +dict 10606 +tuple 9817 cell 6400 -weakref 3664 -getset_descriptor 3311 -wrapper_descriptor 3256 -builtin_function_or_method 2859 -method_descriptor 2774 +weakref 3667 +getset_descriptor 3331 +wrapper_descriptor 3263 +builtin_function_or_method 2860 +method_descriptor 2778 type 2105 list 1884 Parameter 1776 -module 1094 +module 1095 ModuleSpec 1091 property 1023 set 963 method 892 SourceFileLoader 882 fused_cython_function 578 -member_descriptor 519 +member_descriptor 523 DISPLAY_USED_MEM_END RangeIndex: 204 entries, 0 to 203 @@ -81,24 +81,24 @@ Data columns (total 3 columns): dtypes: datetime64[ns](1), float64(1), object(1) memory usage: 4.9+ KB DISPLAY_USED_MEM_START -function 22225 -dict 10609 -tuple 9816 +function 22226 +dict 10611 +tuple 9817 cell 6400 -weakref 3671 -getset_descriptor 3311 -wrapper_descriptor 3256 -builtin_function_or_method 2865 -method_descriptor 2774 +weakref 3674 +getset_descriptor 3331 +wrapper_descriptor 3263 +builtin_function_or_method 2866 +method_descriptor 2778 type 2105 list 1888 Parameter 1776 -module 1094 +module 1095 ModuleSpec 1091 property 1023 set 963 method 892 SourceFileLoader 882 fused_cython_function 578 -member_descriptor 519 +member_descriptor 523 DISPLAY_USED_MEM_END diff --git a/tests/references/bugs_issue_36_display_version_info.log b/tests/references/bugs_issue_36_display_version_info.log index 0fa8f085c..d48da07f3 100644 --- a/tests/references/bugs_issue_36_display_version_info.log +++ b/tests/references/bugs_issue_36_display_version_info.log @@ -1 +1 @@ -[('PyAF_version', '2.0'), ('matplotlib_version', '3.1.3'), ('numpy_version', '1.19.0'), ('pandas_version', '1.0.1'), ('pydot_version', '1.4.1'), ('python_implementation', 'CPython'), ('python_version', '3.8.4'), ('scipy_version', '1.4.1'), ('sklearn_version', '0.23.1'), ('sqlalchemy_version', '1.3.13'), ('system_platform', 'Linux-5.7.0-1-amd64-x86_64-with-glibc2.29'), ('system_processor', ''), ('system_uname', uname_result(system='Linux', node='z600', release='5.7.0-1-amd64', version='#1 SMP Debian 5.7.6-1 (2020-06-24)', machine='x86_64', processor=''))] +[('PyAF_version', '2.0'), ('matplotlib_version', '3.1.3'), ('numpy_version', '1.19.1'), ('pandas_version', '1.0.1'), ('pydot_version', '1.4.1'), ('python_implementation', 'CPython'), ('python_version', '3.8.5'), ('scipy_version', '1.4.1'), ('sklearn_version', '0.23.1'), ('sqlalchemy_version', '1.3.13'), ('system_platform', 'Linux-5.7.0-1-amd64-x86_64-with-glibc2.29'), ('system_processor', ''), ('system_uname', uname_result(system='Linux', node='z600', release='5.7.0-1-amd64', version='#1 SMP Debian 5.7.6-1 (2020-06-24)', machine='x86_64', processor=''))] diff --git a/tests/references/bugs_issue_36_issue_36_version_info.log b/tests/references/bugs_issue_36_issue_36_version_info.log index e897d06c9..c9c6d86e0 100644 --- a/tests/references/bugs_issue_36_issue_36_version_info.log +++ b/tests/references/bugs_issue_36_issue_36_version_info.log @@ -5,7 +5,7 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.088032245635986 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 2.95662260055542 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 @@ -43,11 +43,11 @@ INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_LinearTrend_residue_zeroCycle_residue_Lag INFO:pyaf.std:AR_MODEL_DETAIL_END PyAF_version 2.0 matplotlib_version 3.1.3 -numpy_version 1.19.0 +numpy_version 1.19.1 pandas_version 1.0.1 pydot_version 1.4.1 python_implementation CPython -python_version 3.8.4 +python_version 3.8.5 scipy_version 1.4.1 sklearn_version 0.23.1 sqlalchemy_version 1.3.13 diff --git a/tests/references/bugs_issue_55_grouping_issue_55_notebook.log b/tests/references/bugs_issue_55_grouping_issue_55_notebook.log index e2c85a779..35fd5e26e 100644 --- a/tests/references/bugs_issue_55_grouping_issue_55_notebook.log +++ b/tests/references/bugs_issue_55_grouping_issue_55_notebook.log @@ -1,14 +1,14 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.2743091583251953 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.28644347190856934 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.std:START_TRAINING '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' {'Levels': None, 'Data': None, 'Groups': {'Country': ['GB', 'US', 'DE', 'BE', 'CN'], 'Variant': ['BLANC', 'ROUGE'], 'Wine': ['ALSACE', 'BEAUJOLAIS', 'BORDEAUX']}, 'GroupOrder': ['Wine', 'Variant', 'Country'], 'Type': 'Grouped'} {0: {'ALSACE_BLANC_BE': [], 'ALSACE_BLANC_CN': [], 'ALSACE_BLANC_DE': [], 'ALSACE_BLANC_GB': [], 'ALSACE_BLANC_US': [], 'BEAUJOLAIS_ROUGE_BE': [], 'BEAUJOLAIS_ROUGE_CN': [], 'BEAUJOLAIS_ROUGE_DE': [], 'BEAUJOLAIS_ROUGE_GB': [], 'BEAUJOLAIS_ROUGE_US': [], 'BORDEAUX_BLANC_BE': [], 'BORDEAUX_BLANC_CN': [], 'BORDEAUX_BLANC_DE': [], 'BORDEAUX_BLANC_GB': [], 'BORDEAUX_BLANC_US': [], 'BORDEAUX_ROUGE_BE': [], 'BORDEAUX_ROUGE_CN': [], 'BORDEAUX_ROUGE_DE': [], 'BORDEAUX_ROUGE_GB': [], 'BORDEAUX_ROUGE_US': []}, 1: {'_BLANC_BE': ['ALSACE_BLANC_BE', 'BORDEAUX_BLANC_BE'], '_BLANC_CN': ['ALSACE_BLANC_CN', 'BORDEAUX_BLANC_CN'], '_BLANC_DE': ['ALSACE_BLANC_DE', 'BORDEAUX_BLANC_DE'], '_BLANC_GB': ['ALSACE_BLANC_GB', 'BORDEAUX_BLANC_GB'], '_BLANC_US': ['ALSACE_BLANC_US', 'BORDEAUX_BLANC_US'], '_ROUGE_BE': ['BEAUJOLAIS_ROUGE_BE', 'BORDEAUX_ROUGE_BE'], '_ROUGE_CN': ['BEAUJOLAIS_ROUGE_CN', 'BORDEAUX_ROUGE_CN'], '_ROUGE_DE': ['BEAUJOLAIS_ROUGE_DE', 'BORDEAUX_ROUGE_DE'], '_ROUGE_GB': ['BEAUJOLAIS_ROUGE_GB', 'BORDEAUX_ROUGE_GB'], '_ROUGE_US': ['BEAUJOLAIS_ROUGE_US', 'BORDEAUX_ROUGE_US']}, 2: {'__BE': ['_BLANC_BE', '_ROUGE_BE'], '__CN': ['_BLANC_CN', '_ROUGE_CN'], '__DE': ['_BLANC_DE', '_ROUGE_DE'], '__GB': ['_BLANC_GB', '_ROUGE_GB'], '__US': ['_BLANC_US', '_ROUGE_US']}, 3: {'__': ['__BE', '__CN', '__DE', '__GB', '__US']}} -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' 43.26012134552002 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' 31.7131826877594 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' 8.786235570907593 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' 6.040931463241577 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD @@ -100,9 +100,9 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '__US_BU_Fo INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '__US_BU_Forecast', 'Length': 9, 'MAPE': 0.2002, 'RMSE': 1924359.443875129, 'MAE': 1718364.8549384496, 'SMAPE': 0.1944, 'ErrorMean': -267417.1456614601, 'ErrorStdDev': 1905688.1537748177, 'R2': 0.11616477880655196, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': '___BU_Forecast', 'Length': 36, 'MAPE': 0.1721, 'RMSE': 11316113.431798287, 'MAE': 9151798.696643494, 'SMAPE': 0.187, 'ErrorMean': -5778302.228588483, 'ErrorStdDev': 9729627.256807683, 'R2': -0.014116016298558964, 'Pearson': 0.5167529288459652} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': '___BU_Forecast', 'Length': 9, 'MAPE': 0.1154, 'RMSE': 5391526.199680708, 'MAE': 4368470.163462825, 'SMAPE': 0.1108, 'ErrorMean': -602478.8009333652, 'ErrorStdDev': 5357758.305137456, 'R2': 0.4290736716519402, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 52.75352120399475 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 38.322016954422 INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.28139829635620117 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.22849273681640625 INFO:pyaf.std:TIME_DETAIL TimeVariable='Month' TimeMin=2012-01-01T00:00:00.000000 TimeMax=2014-12-01T00:00:00.000000 TimeDelta= Horizon=1 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='ALSACE_BLANC_BE' Length=46 Min=547748 Max=2166585 Mean=1210707.7826086956 StdDev=275212.5886479406 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_ALSACE_BLANC_BE' Min=547748 Max=2166585 Mean=1210707.7826086956 StdDev=275212.5886479406 @@ -1498,37 +1498,37 @@ INFO:pyaf.std:START_PLOTTING fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) /home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Trend' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Trend' which cannot be automatically added to the legend. ax.legend(patched_names) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Trend_residue' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Trend_residue' which cannot be automatically added to the legend. ax.legend(patched_names) /home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Trend_residue' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Trend_residue' which cannot be automatically added to the legend. ax.legend(patched_names) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Cycle' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Cycle' which cannot be automatically added to the legend. ax.legend(patched_names) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Cycle_residue' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Cycle_residue' which cannot be automatically added to the legend. ax.legend(patched_names) /home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Cycle_residue' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_Cycle_residue' which cannot be automatically added to the legend. ax.legend(patched_names) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_AR' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_AR' which cannot be automatically added to the legend. ax.legend(patched_names) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_AR_residue' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_AR_residue' which cannot be automatically added to the legend. ax.legend(patched_names) /home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_TransformedForecast' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_TransformedForecast' which cannot be automatically added to the legend. ax.legend(patched_names) -/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_TransformedResidue' which cannot be automatically added to the legend. +/home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:31: UserWarning: The handle has a label of '_TransformedResidue' which cannot be automatically added to the legend. ax.legend(patched_names) /home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:49: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig, axs = plt.subplots(ncols=2, figsize=(32, 16)) /home/antoine/dev/python/packages/timeseries/pyaf/pyaf/TS/Plots.py:130: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig, axs = plt.subplots(ncols=1, figsize=(16, 8)) -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 25.778441190719604 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 20.598072052001953 INFO:pyaf.std:START_HIERARCHICAL_FORECASTING INFO:pyaf.std:START_FORECASTING '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' @@ -1578,113 +1578,113 @@ dict_keys(['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLAN 7 BEAUJOLAIS_ROUGE_DE 155871.73582628762 0.6144 6 BEAUJOLAIS_ROUGE_CN 190554.48176906724 1.0 -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' 16.19733691215515 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['ALSACE_BLANC_BE', 'ALSACE_BLANC_CN', 'ALSACE_BLANC_DE', 'ALSACE_BLANC_GB', 'ALSACE_BLANC_US', 'BEAUJOLAIS_ROUGE_BE', 'BEAUJOLAIS_ROUGE_CN', 'BEAUJOLAIS_ROUGE_DE', 'BEAUJOLAIS_ROUGE_GB', 'BEAUJOLAIS_ROUGE_US', 'BORDEAUX_BLANC_BE', 'BORDEAUX_BLANC_CN', 'BORDEAUX_BLANC_DE', 'BORDEAUX_BLANC_GB', 'BORDEAUX_BLANC_US', 'BORDEAUX_ROUGE_BE', 'BORDEAUX_ROUGE_CN', 'BORDEAUX_ROUGE_DE', 'BORDEAUX_ROUGE_GB', 'BORDEAUX_ROUGE_US', '_BLANC_BE', '_BLANC_CN', '_BLANC_DE', '_BLANC_GB', '_BLANC_US', '_ROUGE_BE', '_ROUGE_CN', '_ROUGE_DE', '_ROUGE_GB', '_ROUGE_US', '__BE', '__CN', '__DE', '__GB', '__US', '__']' 7.281851291656494 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 16.418500900268555 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 7.43456506729126 /home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = self.plt.figure(figsize=self.figsize) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__GB_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. /home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = self.plt.figure(figsize=self.figsize) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__US_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. /home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = self.plt.figure(figsize=self.figsize) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__DE_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. /home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = self.plt.figure(figsize=self.figsize) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__BE_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. /home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = self.plt.figure(figsize=self.figsize) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__CN_OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. /home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:320: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = self.plt.figure(figsize=self.figsize) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '__' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___BU_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___BU_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___PHA_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___PHA_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___AHP_TD_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___AHP_TD_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___MO_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___MO_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) -/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___OC_Forecast' which cannot be automatically added to the legend. +/home/antoine/.local/lib/python3.8/site-packages/pandas/plotting/_matplotlib/core.py:572: UserWarning: The handle has a label of '___OC_Forecast' which cannot be automatically added to the legend. ax.legend(handles, labels, loc="best", title=title) WARNING:matplotlib.legend:No handles with labels found to put in legend. diff --git a/tests/references/bugs_issue_58_issue_58_1_categorical_exogenous.log b/tests/references/bugs_issue_58_issue_58_1_categorical_exogenous.log index 3828a6cf7..90ce6d8dc 100644 --- a/tests/references/bugs_issue_58_issue_58_1_categorical_exogenous.log +++ b/tests/references/bugs_issue_58_issue_58_1_categorical_exogenous.log @@ -8,134 +8,134 @@ INFO:pyaf.std:START_TRAINING 'Ozone2' Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64') Index(['AQ', 'AR', 'AS', 'AT', 'AU', 'AV', 'AW', 'AX', 'AY', 'AZ', 'A[', 'A\'], dtype='object') Index(['P_Q', 'P_R', 'P_S', 'P_T', 'P_U', 'P_V', 'P_W', 'P_X'], dtype='object') -INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS Diff_Ozone2 0.05217576026916504 -INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS _Ozone2 0.08349108695983887 -INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS RelDiff_Ozone2 0.06050848960876465 -INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS CumSum_Ozone2 0.07452082633972168 -INFO:pyaf.std:TREND_TIME_IN_SECONDS Diff_Ozone2 0.2486095428466797 -INFO:pyaf.std:TREND_TIME_IN_SECONDS _Ozone2 0.2637360095977783 -INFO:pyaf.std:TREND_TIME_IN_SECONDS RelDiff_Ozone2 0.2291109561920166 -INFO:pyaf.std:TREND_TIME_IN_SECONDS CumSum_Ozone2 0.23345732688903809 +INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS RelDiff_Ozone2 0.02799201011657715 +INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS _Ozone2 0.044539451599121094 +INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS Diff_Ozone2 0.04390573501586914 +INFO:pyaf.std:EXOGENOUS_ENCODING_TIME_IN_SECONDS CumSum_Ozone2 0.04538321495056152 +INFO:pyaf.std:TREND_TIME_IN_SECONDS RelDiff_Ozone2 0.17002153396606445 +INFO:pyaf.std:TREND_TIME_IN_SECONDS CumSum_Ozone2 0.13831210136413574 +INFO:pyaf.std:TREND_TIME_IN_SECONDS _Ozone2 0.14488887786865234 +INFO:pyaf.std:TREND_TIME_IN_SECONDS Diff_Ozone2 0.14619874954223633 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_ConstantTrend_residue_zeroCycle' 0.01 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_ConstantTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS RelDiff_Ozone2 0.037169456481933594 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_Lag1Trend_residue_zeroCycle' 0.01 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_Lag1Trend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_LinearTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_PolyTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS Diff_Ozone2 0.07030892372131348 INFO:pyaf.std:AR_MODEL_ADD_EXOGENOUS '204 15 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_ConstantTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_Lag1Trend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle' 0.02 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_LinearTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_LinearTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle' 0.04 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_LinearTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_LinearTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_PolyTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS CumSum_Ozone2 0.1327512264251709 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle' 0.01 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_PolyTrend_residue_zeroCycle' 0.02 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS _Ozone2 0.16069507598876953 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS CumSum_Ozone2 0.061154842376708984 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS '_Ozone2_PolyTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS _Ozone2 0.06089973449707031 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_PolyTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS Diff_Ozone2 0.05936574935913086 INFO:pyaf.std:AR_MODEL_ADD_EXOGENOUS '204 15 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle' 0.01 INFO:pyaf.std:AR_MODEL_ADD_EXOGENOUS '204 15 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle' 0.04 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS RelDiff_Ozone2 0.22513055801391602 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 INFO:pyaf.std:AR_MODEL_ADD_EXOGENOUS '204 15 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 0.908930778503418 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1596039991.0538392 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.07 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 1.2145593166351318 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1596039991.2111816 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 1.1635053157806396 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1596039991.304472 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 1.5944013595581055 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 0.49973368644714355 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1596039991.7966053 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1596091050.9857779 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 0.7890381813049316 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596039992.0358164 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 0.7922561168670654 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 0.6193456649780273 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1596091051.1458118 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 0.7194032669067383 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1596091051.2456298 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 0.7493560314178467 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue' 204 816 1596091051.2763798 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_ConstantTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 0.5768587589263916 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596039992.1320422 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.05 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596091051.7454867 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.01 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 1.4318256378173828 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596039992.5760245 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 0.7905464172363281 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596039992.85888 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 1.3544697761535645 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 0.7631583213806152 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596039993.1983852 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596091051.7811184 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 0.5280418395996094 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596091051.7941072 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 1.0662147998809814 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 0.6348130702972412 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue' 204 816 1596091051.938368 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_Lag1Trend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 0.5671281814575195 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596039993.2534826 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596091052.331731 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 0.5352892875671387 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596091052.3458457 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.01 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 1.121978998184204 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 0.5367546081542969 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596091052.3608088 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.01 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 0.49013543128967285 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596039993.735373 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596091052.4485974 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 1.107661485671997 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 0.47149205207824707 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596091052.8355021 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.01 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS RelDiff_Ozone2 2.3864569664001465 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone2 822 0.47844481468200684 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596039993.9981515 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS Diff_Ozone2 4.073060989379883 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 0.8100147247314453 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596091052.8581555 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.01 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS Diff_Ozone2 2.382671594619751 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone2 822 0.5877683162689209 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596039994.0964684 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS _Ozone2 4.057394981384277 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 1.3100762367248535 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue' 204 816 1596039994.5403607 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_LinearTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.04 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 6 -INFO:pyaf.std:PERF_TIME_IN_SECONDS Diff_Ozone2 4 0.6661605834960938 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS Diff_Ozone2 5.189763784408569 -INFO:pyaf.std:PERF_TIME_IN_SECONDS _Ozone2 4 0.7034308910369873 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS _Ozone2 5.326339960098267 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 1.194091796875 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596091052.9483092 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone2 822 0.4903225898742676 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596039994.9638138 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS CumSum_Ozone2 4.935167074203491 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone2 822 0.9147300720214844 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 822 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596039995.5036402 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS RelDiff_Ozone2 5.391361951828003 -INFO:pyaf.std:PERF_TIME_IN_SECONDS CumSum_Ozone2 4 0.7235720157623291 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS CumSum_Ozone2 6.175582647323608 -INFO:pyaf.std:PERF_TIME_IN_SECONDS RelDiff_Ozone2 4 0.794609785079956 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS RelDiff_Ozone2 6.8018529415130615 -INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone2 0.014703035354614258 -INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone2 0.017276287078857422 -INFO:pyaf.std:PREDICTION_INTERVAL_TIME_IN_SECONDS Ozone2 1.0943667888641357 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone2']' 8.48215103149414 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue' 204 816 1596091052.9597013 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.03 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS _Ozone2 2.48421311378479 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone2_PolyTrend_residue_zeroCycle_residue_ARX(51)' 204 816 0.02 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS CumSum_Ozone2 2.4884860515594482 +INFO:pyaf.std:PERF_TIME_IN_SECONDS RelDiff_Ozone2 4 0.3944408893585205 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS RelDiff_Ozone2 3.0442590713500977 +INFO:pyaf.std:PERF_TIME_IN_SECONDS Diff_Ozone2 4 0.37880587577819824 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS Diff_Ozone2 3.049747943878174 +INFO:pyaf.std:PERF_TIME_IN_SECONDS CumSum_Ozone2 4 0.4354534149169922 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS CumSum_Ozone2 3.20926570892334 +INFO:pyaf.std:PERF_TIME_IN_SECONDS _Ozone2 4 0.5145742893218994 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS _Ozone2 3.287590980529785 +INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone2 0.0074846744537353516 +INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone2 0.00618433952331543 +INFO:pyaf.std:PREDICTION_INTERVAL_TIME_IN_SECONDS Ozone2 0.8903744220733643 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone2']' 4.436120986938477 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone2' Length=204 Min=0.0 Max=26.099999999999998 Mean=5.529411764705882 StdDev=3.838506864406639 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone2' Min=0.0 Max=26.099999999999998 Mean=5.529411764705882 StdDev=3.838506864406639 @@ -173,19 +173,19 @@ INFO:pyaf.std:AR_MODEL_COEFF 9 Exog2=5_Lag28 -0.8383550959251904 INFO:pyaf.std:AR_MODEL_COEFF 10 Exog2=3_Lag30 -0.838355095925184 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 17.799492120742798 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 11.377612829208374 INFO:pyaf.std:START_FORECASTING '['Ozone2']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone2']' 0.6904833316802979 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone2']' 0.612473726272583 Forecast Columns Index(['Time', 'Ozone2', 'row_number', 'Time_Normalized', '_Ozone2', '_Ozone2_LinearTrend', '_Ozone2_LinearTrend_residue', '_Ozone2_LinearTrend_residue_zeroCycle', diff --git a/tests/references/bugs_issue_69_issue_69_example_1_no_rectifier.log b/tests/references/bugs_issue_69_issue_69_example_1_no_rectifier.log index a94ec1f42..a652cb5fc 100644 --- a/tests/references/bugs_issue_69_issue_69_example_1_no_rectifier.log +++ b/tests/references/bugs_issue_69_issue_69_example_1_no_rectifier.log @@ -1,40 +1,40 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 5.524866342544556 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 3.681159257888794 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-01-25T00:00:00.000000 TimeMax=2023-12-07T00:00:00.000000 TimeDelta= Horizon=7 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3600 Min=3.885288840385726e-10 Max=1.0918868391620093 Mean=0.3281951516502585 StdDev=0.38607000992505003 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=3.885288840385726e-10 Max=1.0918868391620093 Mean=0.3281951516502585 StdDev=0.38607000992505003 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3600 Min=8.125307872025766e-09 Max=1.0630078615259582 Mean=0.3280861925839644 StdDev=0.3856543493189576 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=8.125307872025766e-09 Max=1.0630078615259582 Mean=0.3280861925839644 StdDev=0.3856543493189576 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64)' [LinearTrend + Cycle + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [LinearTrend + Seasonal_DayOfWeek + AR] INFO:pyaf.std:TREND_DETAIL '_Signal_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_LinearTrend_residue_bestCycle_byMAPE' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_LinearTrend_residue_bestCycle_byMAPE_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=3259.8651 MAPE_Forecast=3622.1696 MAPE_Test=148.8339 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.7023 SMAPE_Forecast=0.7378 SMAPE_Test=1.1592 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8476 MASE_Forecast=0.8452 MASE_Test=1.0895 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.016231439572887685 L1_Forecast=0.01703393874803506 L1_Test=0.016097060855300226 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.022234108071893775 L2_Forecast=0.023915352956773623 L2_Test=0.01878751114829279 -INFO:pyaf.std:MODEL_COMPLEXITY 88 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_LinearTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=257.4068 MAPE_Forecast=1645.5932 MAPE_Test=45.0719 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.6567 SMAPE_Forecast=0.6728 SMAPE_Test=1.585 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7729 MASE_Forecast=0.7692 MASE_Test=3.2171 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.014991914243959008 L1_Forecast=0.014880505130955833 L1_Test=0.01305542604668827 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.020352210942737956 L2_Forecast=0.02083411670453415 L2_Test=0.015278855845724144 +INFO:pyaf.std:MODEL_COMPLEXITY 84 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.35651805467312264, array([-0.05545494])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.3561264812047537, array([-0.05453846])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_LinearTrend_residue_bestCycle_byMAPE 60 -0.2766741950165408 {0: -0.308103941392317, 1: -0.2846793071659676, 2: -0.2498585625760706, 3: -0.221821417236655, 4: -0.1798802614904713, 5: -0.14998063391468808, 6: -0.11198444028575934, 7: -0.08268678456339748, 8: -0.04438134649224815, 9: -0.006799770208358841, 10: 0.021040779397637377, 11: 0.052063532185492006, 12: 0.08119196266931236, 13: 0.11248777430214593, 14: 0.14349300334092063, 15: 0.15965865120853046, 16: 0.14917343819434928, 17: 0.1252313434176934, 18: 0.08527107978603748, 19: 0.048985742794448645, 20: 0.02075402601495624, 21: -0.01090542575277545, 22: -0.04765278930547012, 23: -0.06793269623945025, 24: -0.1039850083424585, 25: -0.1384359865901169, 26: -0.18010506610011576, 27: -0.2202008120131766, 28: -0.24568734777566253, 29: -0.2807443334839318, 30: -0.3061487408842068, 31: -0.3027071268530487, 32: -0.30629943191276143, 33: -0.3075862321211942, 34: -0.3074840621249422, 35: -0.30951027486908145, 36: -0.304872100627654, 37: -0.30761226196987207, 38: -0.30745394988487407, 39: -0.3052295523334428, 40: -0.3043657872526405, 41: -0.30867403049280157, 42: -0.30781885022364686, 43: -0.30838281015861035, 44: -0.3001947653687071, 45: -0.30748542181572114, 46: -0.30708145829976385, 47: -0.2996116451811961, 48: -0.30380112356353195, 49: -0.3046701965979487, 50: -0.2999460102538381, 51: -0.30664719124835116, 52: -0.29969339873038486, 53: -0.3092083025115245, 54: -0.30316914000655715, 55: -0.3083971743389113, 56: -0.30503129492756165, 57: -0.30216017854001537, 58: -0.3058223769326453, 59: -0.30574312010094906} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_LinearTrend_residue_Seasonal_DayOfWeek -0.27287545708882766 {0: -0.2764012210522507, 1: -0.268505126786707, 2: -0.2686658589865303, 3: -0.26602598332058236, 4: -0.2743387002606019, 5: -0.2722216737245292, 6: -0.28065399555551307} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag1 0.6382294145429854 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag2 0.3649233834911286 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag3 0.15949129745313198 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag48 0.06927957749232741 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag18 0.0631944487089572 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag11 -0.05431365236302221 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag17 0.05186541190890171 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag62 -0.051100232965025194 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag50 0.04953348963639562 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_LinearTrend_residue_bestCycle_byMAPE_residue_Lag4 0.04936296381218387 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag1 0.5009284045442497 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag2 0.35547386902817263 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag3 0.20853317977824096 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag4 0.11097497698710962 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag10 -0.059026541303259815 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag8 -0.05205831613828473 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag11 -0.04653752825632494 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag5 0.045896968547246356 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag9 -0.04482802034459461 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_LinearTrend_residue_Seasonal_DayOfWeek_residue_Lag56 0.040889526123765896 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_FORECASTING '['Signal']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.8145904541015625 -MIN__FORECAST -0.04798309920545002 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.45835232734680176 +MIN__FORECAST -0.05223362542575892 diff --git a/tests/references/bugs_issue_69_issue_69_example_1_with_rectifier.log b/tests/references/bugs_issue_69_issue_69_example_1_with_rectifier.log index b0be7402b..593d6f0c0 100644 --- a/tests/references/bugs_issue_69_issue_69_example_1_with_rectifier.log +++ b/tests/references/bugs_issue_69_issue_69_example_1_with_rectifier.log @@ -1,40 +1,40 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 4.540101766586304 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 3.6120269298553467 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-01-25T00:00:00.000000 TimeMax=2023-12-07T00:00:00.000000 TimeDelta= Horizon=7 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3600 Min=3.901343491789633e-09 Max=1.0676809650055774 Mean=0.3281879903068972 StdDev=0.38567009199434776 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=3.901343491789633e-09 Max=1.0676809650055774 Mean=0.3281879903068972 StdDev=0.38567009199434776 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=3600 Min=4.513395130189163e-09 Max=1.1367742095237892 Mean=0.3284034916566798 StdDev=0.38614192173168876 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=4.513395130189163e-09 Max=1.1367742095237892 Mean=0.3284034916566798 StdDev=0.38614192173168876 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64)' [LinearTrend + Seasonal_DayOfNthWeekOfMonth + AR] -INFO:pyaf.std:TREND_DETAIL '_Signal_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth' [Seasonal_DayOfNthWeekOfMonth] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=732.9726 MAPE_Forecast=365.9562 MAPE_Test=257020.6971 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.7615 SMAPE_Forecast=0.7909 SMAPE_Test=1.0956 -INFO:pyaf.std:MODEL_MASE MASE_Fit=1.2529 MASE_Forecast=1.2271 MASE_Test=8.1961 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02455794808972164 L1_Forecast=0.02476470201531443 L1_Test=0.04009062204526481 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.04563598850434382 L2_Forecast=0.03875078769575722 L2_Test=0.07277711979085466 +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [PolyTrend + Seasonal_DayOfWeek + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_PolyTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=2246.2776 MAPE_Forecast=1394.0023 MAPE_Test=111.8246 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.687 SMAPE_Forecast=0.732 SMAPE_Test=1.7844 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8054 MASE_Forecast=0.7921 MASE_Test=1.9288 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.01604673595257878 L1_Forecast=0.015826278239169083 L1_Test=0.012640248012548377 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.02229215543581034 L2_Forecast=0.0226341117515051 L2_Test=0.013525591511549025 INFO:pyaf.std:MODEL_COMPLEXITY 84 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (0.3564716912591534, array([-0.05540577])) +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (0.36714808210289296, array([-0.20543757, 0.41091728, -0.2891148 ])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth -0.2729941309354197 {-336: 0.4530470333445391, -335: 0.46127878683517065, -334: 0.4793770219109907, -333: 0.5125190569427105, -332: 0.5183010948742592, -331: 0.5633386470400812, -330: 0.5971083548678295, 7: -0.11361391855673966, 8: -0.12109090617480874, 9: -0.2572470192670618, 10: -0.2574601181304961, 11: -0.29098666032387055, 12: 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_Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag1 0.7827501311793292 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag2 0.2275999807382199 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag30 0.11325147328806356 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag28 -0.06348842059899971 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag34 -0.05036358548548821 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag64 -0.04594941497599092 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag4 0.038056025726822935 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag63 0.036534695253323604 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag57 -0.03330100316242354 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_Lag6 -0.03285679124644539 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag1 0.5021003547372512 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag2 0.34313354394402795 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag3 0.20707742070275176 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag4 0.11200079933232024 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag64 -0.06428807255477526 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag56 0.05996265843609713 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag28 0.05926533300508763 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag9 -0.057453348435966875 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag8 -0.056377690707296105 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_PolyTrend_residue_Seasonal_DayOfWeek_residue_Lag21 0.05303694281498875 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_FORECASTING '['Signal']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.634974479675293 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.468951940536499 MIN__FORECAST 0.0 diff --git a/tests/references/bugs_issue_94_issue_94.log b/tests/references/bugs_issue_94_issue_94.log index 823d88e8b..6ba536647 100644 --- a/tests/references/bugs_issue_94_issue_94.log +++ b/tests/references/bugs_issue_94_issue_94.log @@ -1,60 +1,60 @@ INFO:pyaf.std:START_TRAINING 'Signal' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 5.2231152057647705 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 3.7213187217712402 INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2016-01-25T00:00:00.000000 TimeMax=2016-11-01T00:00:00.000000 TimeDelta= Horizon=7 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=360 Min=0.4398558475299983 Max=28.712057410405663 Mean=13.97942360807454 StdDev=6.708957948567105 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=0.4398558475299983 Max=28.712057410405663 Mean=13.97942360807454 StdDev=6.708957948567105 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=360 Min=-0.5929919989283683 Max=27.93439288219365 Mean=13.948120267274762 StdDev=6.741674583777136 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=-0.5929919989283683 Max=27.93439288219365 Mean=13.948120267274762 StdDev=6.741674583777136 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [ConstantTrend + Seasonal_DayOfWeek + AR] +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)' [ConstantTrend + NoCycle + AR] INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek' [Seasonal_DayOfWeek] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2005 MAPE_Forecast=0.0754 MAPE_Test=0.1132 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1298 SMAPE_Forecast=0.0769 SMAPE_Test=0.1129 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4862 MASE_Forecast=0.5213 MASE_Test=0.3687 -INFO:pyaf.std:MODEL_L1 L1_Fit=1.0719050996810775 L1_Forecast=1.1909224026515663 L1_Test=1.526619453200319 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.5065542292934542 L2_Forecast=1.4914549101132255 L2_Test=1.8235887091054894 -INFO:pyaf.std:MODEL_COMPLEXITY 68 +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3401 MAPE_Forecast=0.074 MAPE_Test=0.0831 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1517 SMAPE_Forecast=0.0769 SMAPE_Test=0.0883 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5295 MASE_Forecast=0.5272 MASE_Test=0.2898 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.2387510311764518 L1_Forecast=1.1483804809965412 L1_Test=1.2927732673993801 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.8314325953976371 L2_Forecast=1.467790802185816 L2_Test=1.3729962484691105 +INFO:pyaf.std:MODEL_COMPLEXITY 64 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12.966785736447527 +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 12.920174298942296 INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_DayOfWeek -0.24650277143335586 {0: -2.2530107142724596, 1: -2.035069062564018, 2: -1.2208738358058708, 3: -0.8234559200919023, 4: 0.664113538758869, 5: 1.6992368766324413, 6: 2.829565147000066} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag1 0.5115291751319303 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag21 0.43142580708127914 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag22 -0.2644482725420876 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag13 -0.25298310779590827 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag42 0.24229211112227855 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag2 0.22646628175420228 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag38 -0.19147839507843975 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag7 -0.16756970128001047 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag34 0.16459931995166704 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_Lag23 -0.15354627076406213 +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag1 0.47465886300162835 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag21 0.47219797920743567 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag42 0.3138841688931112 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag22 -0.2293205923120455 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag20 0.2217795601191902 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag41 -0.20590707759402582 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag63 0.1922458917452438 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag2 0.19209247723623638 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag23 -0.1568042211305777 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_zeroCycle_residue_Lag64 -0.136691403407694 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 24.95932388305664 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 29.548747062683105 INFO:pyaf.std:START_FORECASTING '['Signal']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.9431297779083252 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.43193507194519043 Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek', - '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue', - '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64)', - '_Signal_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(64)_residue', + '_Signal_ConstantTrend_residue_zeroCycle', + '_Signal_ConstantTrend_residue_zeroCycle_residue', + '_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)', + '_Signal_ConstantTrend_residue_zeroCycle_residue_AR(64)_residue', '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', '_Signal_TransformedForecast', 'Signal_Forecast', @@ -65,5 +65,5 @@ Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', '2017-01-21T00:00:00.000000000' '2017-01-22T00:00:00.000000000' '2017-01-23T00:00:00.000000000' '2017-01-24T00:00:00.000000000' '2017-01-25T00:00:00.000000000'] -[ 9.42903143 10.8281931 9.83790778 11.20939992 12.67746026 13.31901525 - 13.26762694] +[11.18170075 12.67977768 12.72030317 13.85742526 13.53385543 14.71349633 + 14.69375627] diff --git a/tests/references/neuralnet_test_air_passengers_CPU_theano.log b/tests/references/neuralnet_test_air_passengers_CPU_theano.log index e22241032..94d52e32f 100644 --- a/tests/references/neuralnet_test_air_passengers_CPU_theano.log +++ b/tests/references/neuralnet_test_air_passengers_CPU_theano.log @@ -7,251 +7,455 @@ Using Theano backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. Using Theano backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20490') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20492') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20492') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20491') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20489') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20491') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20492') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20490') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20491') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20492') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20489') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20180' (I am process '20490') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20489') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20492') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20489') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20492') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20491') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20490') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20492') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20491') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20490') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20491') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20490') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20491') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20490') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20489') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20491') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20489') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20489') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20489') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8981' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8981' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8981' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8981' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8981' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8981' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8982' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8982' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8982' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8726' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8982' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8982' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8982' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8982' (I am process '8981') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8982' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8982' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8981' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8981' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8981' (I am process '8982') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8980' (I am process '8979') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8979' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir +INFO:theano.gof.compilelock:Waiting for existing lock by process '8979' (I am process '8980') +INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.5-64/lock_dir Using Theano backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 41 0.4887 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 41 0.1717 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 33 0.494 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 33 0.1646 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.0777 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.029 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 65 0.074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 65 0.0307 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1285 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0603 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1258 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 49 0.0433 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1869 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0362 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1909 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 49 0.0536 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.318 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.2523 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 0.3023 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.274 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.4374 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.49 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.2323 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.036 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.3491 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.2219 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.297 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.3699 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.5862 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1.0428 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.5474 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.6746 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.7238 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 41 0.4897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 41 0.0495 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 33 0.4918 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 33 0.0623 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.0797 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.0261 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 65 0.0718 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 65 0.0283 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1295 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0531 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 49 0.035 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1921 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0496 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1918 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 49 0.052 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.078 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.3638 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 0.0841 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.3318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.1412 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.6381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.1293 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0215 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.1302 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.4071 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.1148 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.3486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.318 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1.3157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.2426 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.9648 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 9.8916 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 1910117.9312 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 1910117.9312 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 1910119.4346 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 1910117.9312 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 1910119.4346 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 1910119.4346 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 1910119.4346 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 1910117.9312 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 1910118.6822 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910118.6822 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 1910118.6822 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 1910118.6836 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 1910119.4346 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910119.3186 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 1910119.4346 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 1910118.0497 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.9999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.236 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 0.9999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.2842 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.092 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.0492 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.2669 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 1.0 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.2375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.0926 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.0562 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.1 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0726 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0784 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.5501 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.1993 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.55 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.2139 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.2688 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.1825 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.2694 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.1286 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_LSTM(33) 41 0.4105 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_MLP(33) 41 0.084 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_LSTM(33) 33 0.4178 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_MLP(33) 33 0.0733 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_LSTM(33) 73 0.0662 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_MLP(33) 73 0.0305 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_LSTM(33) 65 0.0649 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_MLP(33) 65 0.0235 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_LSTM(33) 57 0.1154 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_MLP(33) 57 0.0496 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_LSTM(33) 49 0.1156 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_MLP(33) 49 0.0335 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_LSTM(33) 57 0.1292 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_MLP(33) 57 0.0365 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_LSTM(33) 49 0.1296 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_MLP(33) 49 0.0441 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_LSTM(33) 73 0.2732 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_MLP(33) 73 0.2209 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_LSTM(33) 65 0.2603 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_MLP(33) 65 0.2298 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_LSTM(33) 105 0.4244 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_MLP(33) 105 0.4541 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_LSTM(33) 97 0.2218 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_MLP(33) 97 0.0394 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_LSTM(33) 89 0.2851 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_MLP(33) 89 0.159 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_LSTM(33) 81 0.2535 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_MLP(33) 81 0.3577 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_LSTM(33) 89 0.3869 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_MLP(33) 89 0.6867 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_LSTM(33) 81 0.345 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_MLP(33) 81 0.366 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_LSTM(33) 73 0.7108 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.1134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.1044 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.2675 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.0924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.2681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.0897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_LSTM(33) 41 0.4137 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_MLP(33) 41 0.0368 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_LSTM(33) 33 0.419 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_MLP(33) 33 0.0354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_LSTM(33) 73 0.0682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_MLP(33) 73 0.0212 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_LSTM(33) 65 0.0635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_MLP(33) 65 0.0209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_LSTM(33) 57 0.1157 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_MLP(33) 57 0.0428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_LSTM(33) 49 0.1162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_MLP(33) 49 0.041 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_LSTM(33) 57 0.1307 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_MLP(33) 57 0.0398 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_LSTM(33) 49 0.1285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_MLP(33) 49 0.0427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_LSTM(33) 73 0.0924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_MLP(33) 73 0.3249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_LSTM(33) 65 0.0846 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_MLP(33) 65 0.3128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_LSTM(33) 105 0.1373 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_MLP(33) 105 0.5993 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_LSTM(33) 97 0.1434 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_MLP(33) 97 0.0359 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_LSTM(33) 89 0.1313 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_MLP(33) 89 0.3531 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_LSTM(33) 81 0.1087 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_MLP(33) 81 0.2961 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_LSTM(33) 89 0.178 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_MLP(33) 89 0.8799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_LSTM(33) 81 0.1036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_MLP(33) 81 0.5411 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_LSTM(33) 73 1.8859 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_MLP(33) 73 2183869.37 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_LSTM(33) 65 2183869.37 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_MLP(33) 65 2183870.8022 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_LSTM(33) 105 1949317.1077 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_LSTM(33) 105 2183869.37 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_MLP(33) 105 2183870.8022 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_LSTM(33) 97 2183870.8022 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_MLP(33) 97 2183870.8022 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_LSTM(33) 97 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_MLP(33) 97 2183869.37 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_LSTM(33) 89 2183870.0849 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_MLP(33) 89 2183870.0874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_MLP(33) 89 2183870.0849 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_LSTM(33) 81 2183870.0849 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_MLP(33) 81 2183870.0874 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_LSTM(33) 89 2183870.1427 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_MLP(33) 89 2183870.0849 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_LSTM(33) 81 2183870.1427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_MLP(33) 81 2183870.0849 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_LSTM(33) 73 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_MLP(33) 73 0.1965 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_LSTM(33) 65 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_MLP(33) 65 0.2246 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_LSTM(33) 105 0.0969 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_MLP(33) 105 0.0603 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_MLP(33) 81 2183870.0874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_LSTM(33) 73 1.0002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_MLP(33) 73 0.2849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_LSTM(33) 65 1.0003 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_MLP(33) 65 0.2202 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_LSTM(33) 105 0.1005 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_MLP(33) 105 0.0788 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_LSTM(33) 97 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_MLP(33) 97 0.0627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_MLP(33) 97 0.0846 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_LSTM(33) 89 0.4856 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_MLP(33) 89 0.1955 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_LSTM(33) 81 0.4857 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_MLP(33) 81 0.1962 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_LSTM(33) 89 0.2137 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_MLP(33) 89 0.1811 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_LSTM(33) 81 0.2148 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_MLP(33) 81 0.1317 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 496.6165306568146 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_MLP(33) 89 0.114 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_LSTM(33) 81 0.4856 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_MLP(33) 81 0.1231 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_LSTM(33) 89 0.2136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_MLP(33) 89 0.0885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_LSTM(33) 81 0.2133 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_MLP(33) 81 0.0874 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 467.09670066833496 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)' [LinearTrend + NoCycle + MLP(33)] -INFO:pyaf.std:TREND_DETAIL '_AirPassengers_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)' [MLP(33)] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1934 MAPE_Forecast=0.2438 MAPE_Test=0.2689 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1841 SMAPE_Forecast=0.2866 SMAPE_Test=0.3423 -INFO:pyaf.std:MODEL_MASE MASE_Fit=2.2326 MASE_Forecast=2.6171 MASE_Test=2.7548 -INFO:pyaf.std:MODEL_L1 L1_Fit=41.738135843043324 L1_Forecast=96.94640293756167 L1_Test=123.96625134046549 -INFO:pyaf.std:MODEL_L2 L2_Fit=51.28659488523553 L2_Forecast=123.98138036804812 L2_Test=162.1857017924399 -INFO:pyaf.std:MODEL_COMPLEXITY 49 +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)' [Lag1Trend + NoCycle + MLP(33)] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_Lag1Trend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)' [MLP(33)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1351 MAPE_Forecast=0.1677 MAPE_Test=0.192 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1348 SMAPE_Forecast=0.1666 SMAPE_Test=0.1942 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.6414 MASE_Forecast=1.7184 MASE_Test=1.8459 +INFO:pyaf.std:MODEL_L1 L1_Fit=30.685665206362803 L1_Forecast=63.655000964800514 L1_Test=83.06661049524944 +INFO:pyaf.std:MODEL_L2 L2_Fit=40.57357218327296 L2_Forecast=80.46819386914491 L2_Test=107.33733293676976 +INFO:pyaf.std:MODEL_COMPLEXITY 65 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (114.90523344816275, array([197.60619977])) +INFO:pyaf.std:LAG1_TREND Lag1Trend 112 INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_LinearTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_Lag1Trend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 33.083558559417725 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 11.833175897598267 INFO:pyaf.std:START_FORECASTING '['AirPassengers']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 1.9633114337921143 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.5140588283538818 Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.0307 0.2797 -1 None _AirPassengers ... 0.0290 0.2846 -2 None _AirPassengers ... 0.0433 0.3925 -3 None _AirPassengers ... 0.0362 0.3579 -4 None Diff_AirPassengers ... 0.0360 0.3362 +0 None _AirPassengers ... 0.0283 0.2631 +1 None _AirPassengers ... 0.0261 0.2450 +2 None _AirPassengers ... 0.0623 0.5814 +3 None Diff_AirPassengers ... 0.0215 0.2005 +4 None _AirPassengers ... 0.0495 0.4403 [5 rows x 20 columns] Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', - '_AirPassengers', '_AirPassengers_LinearTrend', - '_AirPassengers_LinearTrend_residue', - '_AirPassengers_LinearTrend_residue_zeroCycle', - '_AirPassengers_LinearTrend_residue_zeroCycle_residue', - '_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)', - '_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)_residue', + '_AirPassengers', '_AirPassengers_Lag1Trend', + '_AirPassengers_Lag1Trend_residue', + '_AirPassengers_Lag1Trend_residue_zeroCycle', + '_AirPassengers_Lag1Trend_residue_zeroCycle_residue', + '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)', + '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)_residue', '_AirPassengers_Trend', '_AirPassengers_Trend_residue', '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', '_AirPassengers_AR', '_AirPassengers_AR_residue', @@ -287,18 +491,18 @@ Forecasts 129 1959.750000 ... NaN 130 1959.833333 ... NaN 131 1959.916667 ... NaN -132 1960.000000 ... 675.357137 -133 1960.083333 ... 646.649551 -134 1960.166667 ... 615.513665 -135 1960.250000 ... 896.013747 -136 1960.333333 ... 792.230656 -137 1960.416667 ... 880.195848 -138 1960.500000 ... 1357.643291 -139 1960.583333 ... 1459.247036 -140 1960.666667 ... 1380.683472 -141 1960.750000 ... 1650.255558 -142 1960.833333 ... 2147.040217 -143 1960.916667 ... 2440.678539 +132 1960.000000 ... 6.105752e+02 +133 1960.083333 ... 8.289923e+02 +134 1960.166667 ... 1.282353e+03 +135 1960.250000 ... 2.554072e+03 +136 1960.333333 ... 5.822047e+03 +137 1960.416667 ... 1.554822e+04 +138 1960.500000 ... 4.968792e+04 +139 1960.583333 ... 1.839899e+05 +140 1960.666667 ... 7.216017e+05 +141 1960.750000 ... 2.850242e+06 +142 1960.833333 ... 1.097284e+07 +143 1960.916667 ... 4.047502e+07 [24 rows x 5 columns] @@ -320,17 +524,17 @@ Forecasts }, "Model": { "AR_Model": "MLP(33)", - "Best_Decomposition": "_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33)", + "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)", "Cycle": "NoCycle", "Signal_Transoformation": "NoTransf", - "Trend": "LinearTrend" + "Trend": "Lag1Trend" }, "Model_Performance": { - "COMPLEXITY": "49", - "MAE": "96.94640293756167", - "MAPE": "0.2438", - "MASE": "2.6171", - "RMSE": "123.98138036804812" + "COMPLEXITY": "65", + "MAE": "63.655000964800514", + "MAPE": "0.1677", + "MASE": "1.7184", + "RMSE": "80.46819386914491" } } } @@ -340,7 +544,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":396.2165101715,"121":393.148710137,"122":435.2989276804,"123":471.6033200285,"124":371.9360708362,"125":301.4026488532,"126":344.5768265101,"127":408.5202512223,"128":411.0988840642,"129":431.6415805504,"130":478.9227795394,"131":444.1046132937,"132":432.3536314963,"133":405.6761131389,"134":453.4443612304,"135":502.0539484333,"136":405.2467341835,"137":294.2653713742,"138":363.7059598588,"139":434.9321814306,"140":394.2833000055,"141":376.079208467,"142":468.6379419406,"143":572.0598269645},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":189.3501259749,"133":164.7026756618,"134":291.3750579175,"135":108.0941503639,"136":18.2628124281,"137":-291.6651053133,"138":-630.2313716527,"139":-589.3826731146,"140":-592.1168716556,"141":-898.0971414155,"142":-1209.7643331953,"143":-1296.5588855331},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":675.3571370177,"133":646.6495506159,"134":615.5136645433,"135":896.0137465027,"136":792.230655939,"137":880.1958480617,"138":1357.6432913704,"139":1459.2470359758,"140":1380.6834716667,"141":1650.2555583495,"142":2147.0402170765,"143":2440.6785394621}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":386.6651039124,"121":384.1817893982,"122":428.9889526367,"123":396.7807426453,"124":415.1244239807,"125":438.647233963,"126":430.6401290894,"127":570.1843128204,"128":471.9312973022,"129":397.0732269287,"130":322.5833587646,"131":385.8829078674,"132":452.85754776,"133":491.0273246765,"134":578.6015472412,"135":571.093711853,"136":601.7211456299,"137":574.797580719,"138":574.2867832184,"139":557.9753195047,"140":447.5689550638,"141":386.5812231302,"142":329.0168997049,"143":384.720993638},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":295.1398877765,"133":153.0623982958,"134":-125.1496154454,"135":-1411.8843161293,"136":-4618.6052006093,"137":-14398.6277365425,"138":-48539.3430383693,"139":-182873.9549726092,"140":-720706.5959528497,"141":-2849468.6764668259,"142":-10972179.6917252541,"143":-40474254.8892115131},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":610.5752077435,"133":828.9922510573,"134":1282.3527099278,"135":2554.0717398353,"136":5822.047491869,"137":15548.2228979804,"138":49687.9166048061,"139":183989.9056116187,"140":721601.7338629774,"141":2850241.8389130863,"142":10972837.7255246639,"143":40475024.3311987892}} diff --git a/tests/references/neuralnet_test_air_passengers_GPU_tensorflow.log b/tests/references/neuralnet_test_air_passengers_GPU_tensorflow.log index 27b80c248..0932216f6 100644 --- a/tests/references/neuralnet_test_air_passengers_GPU_tensorflow.log +++ b/tests/references/neuralnet_test_air_passengers_GPU_tensorflow.log @@ -71,7 +71,7 @@ INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integra INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_NoAR 48 0.4856 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_NoAR 56 0.2138 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_NoAR 48 0.2138 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 9.60141897201538 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.6429100036621094 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -98,19 +98,19 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 21.083531379699707 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 10.604730606079102 INFO:pyaf.std:START_FORECASTING '['AirPassengers']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.7363481521606445 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.2239055633544922 INFO:pyaf.std:START_TRAINING 'AirPassengers' Using TensorFlow backend. WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. @@ -118,191 +118,191 @@ WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/tensorf Instructions for updating: If using Keras pass *_constraint arguments to layers. WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs. -2020-07-29 18:35:22.102608: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 -2020-07-29 18:35:22.108652: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero -2020-07-29 18:35:22.109070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: +2020-07-30 09:35:17.106866: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 +2020-07-30 09:35:17.110444: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-30 09:35:17.110716: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: GeForce GT 730 major: 3 minor: 5 memoryClockRate(GHz): 0.9015 pciBusID: 0000:0f:00.0 -2020-07-29 18:35:22.109310: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.2'; dlerror: libcudart.so.10.2: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib -2020-07-29 18:35:22.112546: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 -2020-07-29 18:35:22.116314: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 -2020-07-29 18:35:22.117225: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 -2020-07-29 18:35:22.120904: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 -2020-07-29 18:35:22.122744: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 -2020-07-29 18:35:22.123096: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib -2020-07-29 18:35:22.123125: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. +2020-07-30 09:35:17.110854: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.2'; dlerror: libcudart.so.10.2: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-30 09:35:17.112811: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 +2020-07-30 09:35:17.114358: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 +2020-07-30 09:35:17.114740: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 +2020-07-30 09:35:17.116703: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 +2020-07-30 09:35:17.117837: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 +2020-07-30 09:35:17.117966: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-30 09:35:17.117985: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... -2020-07-29 18:35:22.123733: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 +2020-07-30 09:35:17.118378: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags. -2020-07-29 18:35:22.164068: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2660215000 Hz -2020-07-29 18:35:22.169873: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5e18b60 initialized for platform Host (this does not guarantee that XLA will be used). Devices: -2020-07-29 18:35:22.170009: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version -2020-07-29 18:35:22.446079: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero -2020-07-29 18:35:22.446691: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5e8d510 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: -2020-07-29 18:35:22.446777: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GT 730, Compute Capability 3.5 -2020-07-29 18:35:22.446993: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: -2020-07-29 18:35:22.447055: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] -2020-07-29 18:35:22.447157: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 24. Tune using inter_op_parallelism_threads for best performance. +2020-07-30 09:35:17.128668: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2659815000 Hz +2020-07-30 09:35:17.130733: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x671a840 initialized for platform Host (this does not guarantee that XLA will be used). Devices: +2020-07-30 09:35:17.130773: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version +2020-07-30 09:35:17.281188: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-30 09:35:17.281648: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x678efc0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: +2020-07-30 09:35:17.281697: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GT 730, Compute Capability 3.5 +2020-07-30 09:35:17.281810: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: +2020-07-30 09:35:17.281838: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] +2020-07-30 09:35:17.281897: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 24. Tune using inter_op_parallelism_threads for best performance. WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 41 0.4953 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 41 0.0343 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 33 0.4916 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 33 0.0696 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.0761 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.0313 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 65 0.0709 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 65 0.04 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1271 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0535 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 49 0.129 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 49 0.05 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1908 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0334 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1858 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 49 0.0327 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.0891 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.2123 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 0.1925 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.2347 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.373 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.4381 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.1929 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0396 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.0985 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.2447 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.1173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.3009 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.4995 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.873 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.3827 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.6001 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.878 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 41 0.4942 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 41 0.054 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 33 0.4957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 33 0.0645 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.0757 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.0371 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 65 0.0732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 65 0.0327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1273 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 49 0.0249 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 57 0.1955 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 57 0.0655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 49 0.1931 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 49 0.0648 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.2487 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.1259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 0.1348 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.1789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.369 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.2906 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.1452 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.1789 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.2136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.2732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.1971 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.5246 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.6542 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.4749 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.5364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 0.7517 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 1910117.9312 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 1910117.9312 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 1910119.4346 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 146088.3262 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 1910119.4346 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 129402.6223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 4.6119 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 24798.2525 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 1910117.9312 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 1910118.6822 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910118.6836 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 1910118.6822 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 1910118.6836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 1910118.6822 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 1910119.4346 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910117.9312 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 1910119.1921 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 1910119.4346 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 1910119.3186 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 1910117.9312 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 73 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.1103 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(33) 73 0.266 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(33) 65 1.0 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.129 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.0932 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.0639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33) 65 0.3016 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(33) 105 0.0917 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(33) 105 0.0568 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_LSTM(33) 97 0.1001 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33) 97 0.0821 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.55 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.1053 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.55 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.1052 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.2687 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.0851 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.2687 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.0829 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_LSTM(33) 41 0.4194 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_MLP(33) 41 0.0336 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_LSTM(33) 33 0.4185 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_MLP(33) 33 0.0362 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_LSTM(33) 73 0.0641 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_MLP(33) 73 0.0199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_LSTM(33) 65 0.0596 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_MLP(33) 65 0.0281 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_LSTM(33) 57 0.1169 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_MLP(33) 57 0.04 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_LSTM(33) 49 0.1165 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_MLP(33) 49 0.0402 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_LSTM(33) 57 0.1293 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_MLP(33) 57 0.0376 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_LSTM(33) 49 0.1278 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_MLP(33) 49 0.0341 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_LSTM(33) 73 0.096 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_MLP(33) 73 0.1823 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_LSTM(33) 65 0.1329 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_MLP(33) 65 0.2084 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_LSTM(33) 105 0.3788 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_MLP(33) 105 0.418 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_LSTM(33) 97 0.1699 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_MLP(33) 97 0.0263 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_LSTM(33) 89 0.0811 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_MLP(33) 89 0.195 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_LSTM(33) 81 0.0948 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_MLP(33) 81 0.2605 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_LSTM(33) 89 0.2858 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_MLP(33) 89 0.5973 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_LSTM(33) 81 0.2022 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_MLP(33) 81 0.3354 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_LSTM(33) 73 0.7454 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.154 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_LSTM(33) 81 0.5501 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_LinearTrend_residue_zeroCycle_residue_MLP(33) 81 0.1886 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(33) 89 0.2688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(33) 89 0.0786 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_LSTM(33) 81 0.2688 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers_PolyTrend_residue_zeroCycle_residue_MLP(33) 81 0.0779 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_LSTM(33) 41 0.4199 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_Cycle_None_MLP(33) 41 0.0277 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_LSTM(33) 33 0.4207 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_MLP(33) 33 0.033 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_LSTM(33) 73 0.0642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_Cycle_MLP(33) 73 0.0271 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_LSTM(33) 65 0.063 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_Lag1Trend_NoCycle_MLP(33) 65 0.0284 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_LSTM(33) 57 0.1173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_Cycle_None_MLP(33) 57 0.0451 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_LSTM(33) 49 0.1159 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_LinearTrend_NoCycle_MLP(33) 49 0.0389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_LSTM(33) 57 0.1351 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_Cycle_None_MLP(33) 57 0.0509 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_LSTM(33) 49 0.1337 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_PolyTrend_NoCycle_MLP(33) 49 0.0486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_LSTM(33) 73 0.2004 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_Cycle_None_MLP(33) 73 0.1263 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_LSTM(33) 65 0.1177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_ConstantTrend_NoCycle_MLP(33) 65 0.1682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_LSTM(33) 105 0.381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_Cycle_MLP(33) 105 0.2772 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_LSTM(33) 97 0.1227 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_Lag1Trend_NoCycle_MLP(33) 97 0.036 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_LSTM(33) 89 0.1242 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_Cycle_None_MLP(33) 89 0.1939 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_LSTM(33) 81 0.2205 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_LinearTrend_NoCycle_MLP(33) 81 0.1834 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_LSTM(33) 89 0.337 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_Cycle_MLP(33) 89 0.4455 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_LSTM(33) 81 0.2655 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_AirPassengers Difference_PolyTrend_NoCycle_MLP(33) 81 0.327 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_LSTM(33) 73 0.7161 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_Cycle_MLP(33) 73 2183869.37 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_LSTM(33) 65 2183869.37 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_MLP(33) 65 2183870.8022 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_LSTM(33) 105 18228.4137 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_MLP(33) 105 2183870.8022 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_LSTM(33) 97 18975.6207 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_ConstantTrend_NoCycle_MLP(33) 65 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_LSTM(33) 105 1.082 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_Cycle_None_MLP(33) 105 2183869.37 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_LSTM(33) 97 2435.3087 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_Lag1Trend_NoCycle_MLP(33) 97 2183869.37 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_LSTM(33) 89 2183870.0849 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_Cycle_None_MLP(33) 89 2183870.0874 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_LSTM(33) 81 2183870.0849 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_MLP(33) 81 2183870.0874 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_LinearTrend_NoCycle_MLP(33) 81 2183870.0849 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_LSTM(33) 89 2183870.1427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_MLP(33) 89 2183870.0298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_Cycle_None_MLP(33) 89 2183870.0874 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_LSTM(33) 81 2183870.1427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_MLP(33) 81 2183870.0849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_AirPassengers RelativeDifference_PolyTrend_NoCycle_MLP(33) 81 2183869.9706 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_LSTM(33) 73 1.0002 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_MLP(33) 73 0.1416 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_LSTM(33) 65 1.0002 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_MLP(33) 65 0.1373 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_LSTM(33) 105 0.1007 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_MLP(33) 105 0.0709 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_Cycle_None_MLP(33) 73 0.259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_LSTM(33) 65 1.0005 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_ConstantTrend_NoCycle_MLP(33) 65 0.2948 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_LSTM(33) 105 0.0986 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_Cycle_MLP(33) 105 0.0692 INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_LSTM(33) 97 0.0951 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_MLP(33) 97 0.0714 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_LSTM(33) 89 0.4855 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_MLP(33) 89 0.0901 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_LSTM(33) 81 0.4856 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_MLP(33) 81 0.1051 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_LSTM(33) 89 0.2127 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_MLP(33) 89 0.0756 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_LSTM(33) 81 0.2129 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_MLP(33) 81 0.0801 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 1899.278201341629 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_Lag1Trend_NoCycle_MLP(33) 97 0.0737 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_LSTM(33) 89 0.4857 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_Cycle_None_MLP(33) 89 0.1507 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_LSTM(33) 81 0.4857 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_LinearTrend_NoCycle_MLP(33) 81 0.1891 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_LSTM(33) 89 0.214 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_Cycle_None_MLP(33) 89 0.0768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_LSTM(33) 81 0.2134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_AirPassengers Integration_PolyTrend_NoCycle_MLP(33) 81 0.0765 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 1178.9011659622192 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)' [Lag1Trend + NoCycle + MLP(33)] -INFO:pyaf.std:TREND_DETAIL '_AirPassengers_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_Lag1Trend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)' [MLP(33)] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0392 MAPE_Forecast=0.0281 MAPE_Test=0.04 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0395 SMAPE_Forecast=0.0282 SMAPE_Test=0.0404 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4147 MASE_Forecast=0.2893 MASE_Test=0.3801 -INFO:pyaf.std:MODEL_L1 L1_Fit=7.753587457661827 L1_Forecast=10.717032750447592 L1_Test=17.106223861376446 -INFO:pyaf.std:MODEL_L2 L2_Fit=9.50525325843745 L2_Forecast=14.1704707078929 L2_Test=19.987808107132345 -INFO:pyaf.std:MODEL_COMPLEXITY 65 +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33)' [ConstantTrend + NoCycle + MLP(33)] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33)' [MLP(33)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0509 MAPE_Forecast=0.033 MAPE_Test=0.0645 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.05 SMAPE_Forecast=0.0336 SMAPE_Test=0.0675 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5259 MASE_Forecast=0.3353 MASE_Test=0.5959 +INFO:pyaf.std:MODEL_L1 L1_Fit=9.830861430201262 L1_Forecast=12.419429990980348 L1_Test=26.813919279310408 +INFO:pyaf.std:MODEL_L2 L2_Fit=13.092396493845095 L2_Forecast=16.26456574637846 L2_Test=31.377224171261695 +INFO:pyaf.std:MODEL_COMPLEXITY 33 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LAG1_TREND Lag1Trend 112 +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 213.70833333333334 INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_Lag1Trend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 13.299287796020508 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 11.439980506896973 INFO:pyaf.std:START_FORECASTING '['AirPassengers']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.6153459548950195 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.5701861381530762 Split Transformation ... TestMAPE TestMASE 0 None _AirPassengers ... 0.1025 0.9824 1 None _AirPassengers ... 0.1000 0.9593 @@ -411,20 +411,20 @@ Forecasts Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.0313 0.2821 -1 None Diff_AirPassengers ... 0.0396 0.3796 -2 None _AirPassengers ... 0.0400 0.3801 -3 None _AirPassengers ... 0.0343 0.3268 -4 None _AirPassengers ... 0.0327 0.3120 +0 None _AirPassengers ... 0.0371 0.3453 +1 None _AirPassengers ... 0.0540 0.4995 +2 None _AirPassengers ... 0.0327 0.3158 +3 None _AirPassengers ... 0.0645 0.5959 +4 None Diff_AirPassengers ... 0.0258 0.2629 [5 rows x 20 columns] Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', - '_AirPassengers', '_AirPassengers_Lag1Trend', - '_AirPassengers_Lag1Trend_residue', - '_AirPassengers_Lag1Trend_residue_zeroCycle', - '_AirPassengers_Lag1Trend_residue_zeroCycle_residue', - '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)', - '_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)_residue', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33)_residue', '_AirPassengers_Trend', '_AirPassengers_Trend_residue', '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', '_AirPassengers_AR', '_AirPassengers_AR_residue', @@ -460,18 +460,18 @@ Forecasts 129 1959.750000 ... NaN 130 1959.833333 ... NaN 131 1959.916667 ... NaN -132 1960.000000 ... 5.235678e+02 -133 1960.083333 ... 5.874755e+02 -134 1960.166667 ... 6.760443e+02 -135 1960.250000 ... 8.406387e+02 -136 1960.333333 ... 1.629702e+03 -137 1960.416667 ... 4.162309e+03 -138 1960.500000 ... 1.216012e+04 -139 1960.583333 ... 3.577379e+04 -140 1960.666667 ... 1.042746e+05 -141 1960.750000 ... 2.758606e+05 -142 1960.833333 ... 6.386067e+05 -143 1960.916667 ... 1.485270e+06 +132 1960.000000 ... 163.828635 +133 1960.083333 ... 118.002034 +134 1960.166667 ... -4.302795 +135 1960.250000 ... -2.720263 +136 1960.333333 ... 142.910794 +137 1960.416667 ... 257.597138 +138 1960.500000 ... 664.687787 +139 1960.583333 ... 743.208838 +140 1960.666667 ... 959.923738 +141 1960.750000 ... 962.395493 +142 1960.833333 ... 810.956260 +143 1960.916667 ... 770.593941 [24 rows x 5 columns] @@ -493,17 +493,17 @@ Forecasts }, "Model": { "AR_Model": "MLP(33)", - "Best_Decomposition": "_AirPassengers_Lag1Trend_residue_zeroCycle_residue_MLP(33)", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_MLP(33)", "Cycle": "NoCycle", "Signal_Transoformation": "NoTransf", - "Trend": "Lag1Trend" + "Trend": "ConstantTrend" }, "Model_Performance": { - "COMPLEXITY": "65", - "MAE": "10.717032750447592", - "MAPE": "0.0281", - "MASE": "0.2893", - "RMSE": "14.1704707078929" + "COMPLEXITY": "33", + "MAE": "12.419429990980348", + "MAPE": "0.033", + "MASE": "0.3353", + "RMSE": "16.26456574637846" } } } @@ -513,7 +513,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":417.1336975098,"121":391.4572906494,"122":350.0742492676,"123":398.1986560822,"124":474.5759887695,"125":380.4394378662,"126":430.4254150391,"127":522.2077999115,"128":417.8685302734,"129":375.1405029297,"130":371.494758606,"131":467.0784301758,"132":495.7937011719,"133":536.8444061279,"134":579.6602973938,"135":565.533946991,"136":633.3712730408,"137":589.8858985901,"138":522.5616607666,"139":445.5340805054,"140":280.794670105,"141":205.0109710693,"142":212.8752365112,"143":390.9945011139},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":468.0195785844,"133":486.2132921084,"134":483.2763325867,"135":290.4292094442,"136":-362.9594085609,"137":-2982.5374203055,"138":-11114.9984940824,"139":-34882.7264667011,"140":-103713.0034670222,"141":-275450.6197955635,"142":-638180.9110635865,"143":-1484487.6945061714},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":523.5678237593,"133":587.4755201475,"134":676.0442622009,"135":840.6386845378,"136":1629.7019546425,"137":4162.3092174856,"138":12160.1218156156,"139":35773.7946277118,"140":104274.5928072321,"141":275860.6417377021,"142":638606.6615366089,"143":1485269.6835083992}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":156.3670171102,"121":123.8546346029,"122":150.6203816732,"123":146.0527165731,"124":202.7491505941,"125":234.4016927083,"126":307.2834370931,"127":359.6543477376,"128":303.5984242757,"129":287.7324956258,"130":154.0657704671,"131":106.2292149862,"132":131.9500859578,"133":67.9488728841,"134":-70.6441752116,"135":-90.0539347331,"136":14.4606221517,"137":75.1550649007,"138":415.6338094076,"139":424.3286794027,"140":605.2435811361,"141":623.372853597,"142":509.9702351888,"143":498.7885030111},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":100.0715370949,"133":17.8957115049,"134":-136.9855556568,"135":-177.3876061104,"136":-113.9895496749,"137":-107.2870078847,"138":166.5798316986,"139":105.4485208982,"140":250.5634239995,"141":284.3502141033,"142":208.9842105388,"143":226.9830646115},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":163.8286348207,"133":118.0020342633,"134":-4.3027947664,"135":-2.7202633558,"136":142.9107939783,"137":257.5971376861,"138":664.6877871165,"139":743.2088379071,"140":959.9237382726,"141":962.3954930907,"142":810.9562598388,"143":770.5939414106}} diff --git a/tests/references/neuralnet_test_air_passengers_tensorflow.log b/tests/references/neuralnet_test_air_passengers_tensorflow.log index 0d156d13f..69f9328d8 100644 --- a/tests/references/neuralnet_test_air_passengers_tensorflow.log +++ b/tests/references/neuralnet_test_air_passengers_tensorflow.log @@ -5,36 +5,36 @@ WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/tensorf Instructions for updating: If using Keras pass *_constraint arguments to layers. WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs. -2020-07-29 18:34:43.529635: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 -2020-07-29 18:34:43.535783: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero -2020-07-29 18:34:43.536226: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: +2020-07-30 09:34:47.803718: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 +2020-07-30 09:34:47.807527: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-30 09:34:47.807814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: name: GeForce GT 730 major: 3 minor: 5 memoryClockRate(GHz): 0.9015 pciBusID: 0000:0f:00.0 -2020-07-29 18:34:43.536480: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.2'; dlerror: libcudart.so.10.2: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib -2020-07-29 18:34:43.539886: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 -2020-07-29 18:34:43.543153: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 -2020-07-29 18:34:43.543960: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 -2020-07-29 18:34:43.547670: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 -2020-07-29 18:34:43.549802: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 -2020-07-29 18:34:43.550097: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib -2020-07-29 18:34:43.550128: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. +2020-07-30 09:34:47.807951: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudart.so.10.2'; dlerror: libcudart.so.10.2: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-30 09:34:47.809961: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10 +2020-07-30 09:34:47.811669: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10 +2020-07-30 09:34:47.812083: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10 +2020-07-30 09:34:47.814282: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10 +2020-07-30 09:34:47.815704: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10 +2020-07-30 09:34:47.815835: W tensorflow/stream_executor/platform/default/dso_loader.cc:55] Could not load dynamic library 'libcudnn.so.7'; dlerror: libcudnn.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/OpenBLAS/lib::/u01/app/oracle/product/11.2.0/xe/lib:/opt/teradata/client/ODBC_64/lib +2020-07-30 09:34:47.815855: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1641] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... -2020-07-29 18:34:43.551164: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 +2020-07-30 09:34:47.816284: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags. -2020-07-29 18:34:43.575825: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2660215000 Hz -2020-07-29 18:34:43.584095: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x674dd90 initialized for platform Host (this does not guarantee that XLA will be used). Devices: -2020-07-29 18:34:43.584197: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version -2020-07-29 18:34:44.015010: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero -2020-07-29 18:34:44.015960: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x67b0090 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: -2020-07-29 18:34:44.016051: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GT 730, Compute Capability 3.5 -2020-07-29 18:34:44.016234: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: -2020-07-29 18:34:44.016264: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] -2020-07-29 18:34:44.016361: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 24. Tune using inter_op_parallelism_threads for best performance. +2020-07-30 09:34:47.827566: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2659815000 Hz +2020-07-30 09:34:47.829743: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x65ddb70 initialized for platform Host (this does not guarantee that XLA will be used). Devices: +2020-07-30 09:34:47.829785: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version +2020-07-30 09:34:48.004322: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero +2020-07-30 09:34:48.004799: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x663fe70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices: +2020-07-30 09:34:48.004847: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): GeForce GT 730, Compute Capability 3.5 +2020-07-30 09:34:48.004952: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: +2020-07-30 09:34:48.004969: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] +2020-07-30 09:34:48.005019: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 24. Tune using inter_op_parallelism_threads for best performance. WARNING:tensorflow:From /home/antoine/.local/lib/python3.8/site-packages/keras/backend/tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead. -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(7) 7 0.489 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_LSTM(7) 7 0.4157 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 22.086617469787598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(7) 7 0.4902 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _AirPassengers NoTransf_ConstantTrend_NoCycle_LSTM(7) 7 0.4171 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 8.218303442001343 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=104 Max=559 Mean=262.49242424242425 StdDev=106.22114554451818 @@ -43,11 +43,11 @@ INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycl INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_LSTM(7)' [LSTM(7)] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3031 MAPE_Forecast=0.4157 MAPE_Test=0.489 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2749 SMAPE_Forecast=0.5321 SMAPE_Test=0.6536 -INFO:pyaf.std:MODEL_MASE MASE_Fit=3.1121 MASE_Forecast=4.3376 MASE_Test=4.7624 -INFO:pyaf.std:MODEL_L1 L1_Fit=58.18080847436148 L1_Forecast=160.68024763589105 L1_Test=214.30654821296528 -INFO:pyaf.std:MODEL_L2 L2_Fit=70.74218465256418 L2_Forecast=171.17002779726153 L2_Test=224.48413096866847 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3046 MAPE_Forecast=0.4171 MAPE_Test=0.4902 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2763 SMAPE_Forecast=0.5344 SMAPE_Test=0.6557 +INFO:pyaf.std:MODEL_MASE MASE_Fit=3.1292 MASE_Forecast=4.3519 MASE_Test=4.7735 +INFO:pyaf.std:MODEL_L1 L1_Fit=58.49886327552505 L1_Forecast=161.20851501449943 L1_Test=214.80822840457157 +INFO:pyaf.std:MODEL_L2 L2_Fit=71.11221413118335 L2_Forecast=171.70615037751602 L2_Test=224.99258259560423 INFO:pyaf.std:MODEL_COMPLEXITY 7 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None @@ -61,21 +61,21 @@ INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 22.008764266967773 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 11.47410798072815 INFO:pyaf.std:START_FORECASTING '['AirPassengers']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.9376730918884277 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.43982529640197754 Split Transformation ... TestMAPE TestMASE -0 None _AirPassengers ... 0.489 4.7624 +0 None _AirPassengers ... 0.4902 4.7735 [1 rows x 20 columns] Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', @@ -120,18 +120,18 @@ Forecasts 129 1959.750000 ... NaN 130 1959.833333 ... NaN 131 1959.916667 ... NaN -132 1960.000000 ... 549.491233 -133 1960.083333 ... 550.149269 -134 1960.166667 ... 550.571344 -135 1960.250000 ... 550.710263 -136 1960.333333 ... 550.783062 -137 1960.416667 ... 550.656374 -138 1960.500000 ... 550.614539 -139 1960.583333 ... 550.416500 -140 1960.666667 ... 550.351127 -141 1960.750000 ... 550.335517 -142 1960.833333 ... 550.269106 -143 1960.916667 ... 550.248637 +132 1960.000000 ... 550.169526 +133 1960.083333 ... 550.003097 +134 1960.166667 ... 549.733361 +135 1960.250000 ... 549.721890 +136 1960.333333 ... 550.004194 +137 1960.416667 ... 549.725820 +138 1960.500000 ... 549.970621 +139 1960.583333 ... 549.913109 +140 1960.666667 ... 550.015630 +141 1960.750000 ... 549.998966 +142 1960.833333 ... 550.027512 +143 1960.916667 ... 550.018476 [24 rows x 5 columns] @@ -160,10 +160,10 @@ Forecasts }, "Model_Performance": { "COMPLEXITY": "7", - "MAE": "160.68024763589105", - "MAPE": "0.4157", - "MASE": "4.3376", - "RMSE": "171.17002779726153" + "MAE": "161.20851501449943", + "MAPE": "0.4171", + "MASE": "4.3519", + "RMSE": "171.70615037751602" } } } @@ -173,7 +173,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":213.9979785581,"121":213.9982652565,"122":214.0798063179,"123":214.0797006985,"124":214.0792912145,"125":213.9980399509,"126":214.0797586342,"127":214.0798063179,"128":214.0797731777,"129":214.0455419918,"130":213.9336151977,"131":213.8698441287,"132":213.9979785581,"133":213.9028331538,"134":213.9979783197,"135":213.9979785581,"136":214.0117547413,"137":213.8600750466,"138":213.8075464865,"139":213.6052374815,"140":213.5380055358,"141":213.5216736098,"142":213.4549358686,"143":213.4343378445},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":-121.4952759245,"133":-122.3436021924,"134":-122.5753868679,"135":-122.7143056747,"136":-122.7595522167,"137":-122.9362240216,"138":-122.9994460018,"139":-123.2060254856,"140":-123.275116264,"141":-123.292169952,"142":-123.3592339875,"143":-123.3799617816},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":549.4912330408,"133":550.1492685,"134":550.5713435074,"135":550.710262791,"136":550.7830616992,"137":550.6563741148,"138":550.6145389747,"139":550.4165004486,"140":550.3511273356,"141":550.3355171716,"142":550.2691057247,"143":550.2486374705}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":213.603230541,"121":213.4865795573,"122":213.4865795573,"123":213.3489892085,"124":213.8575999935,"125":213.1182807287,"126":213.8575645884,"127":213.1779032548,"128":213.1182804306,"129":213.9951897462,"130":213.6255307545,"131":213.6255307843,"132":213.6254713138,"133":213.4865795573,"134":213.2113988896,"135":213.211473157,"136":213.4890290995,"137":213.2113988896,"138":213.4550940891,"139":213.3978073796,"140":213.5004866322,"141":213.4838731488,"142":213.5124109586,"143":213.5033527513},"AirPassengers_Forecast_Lower_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":-122.9185834261,"133":-123.0299379323,"134":-123.310563164,"135":-123.2989440378,"136":-123.0261359798,"137":-123.3030224499,"138":-123.0604323603,"139":-123.1174937428,"140":-123.0146571116,"141":-123.0312195218,"142":-123.0026897613,"143":-123.0117702908},"AirPassengers_Forecast_Upper_Bound":{"120":null,"121":null,"122":null,"123":null,"124":null,"125":null,"126":null,"127":null,"128":null,"129":null,"130":null,"131":null,"132":550.1695260537,"133":550.0030970468,"134":549.7333609433,"135":549.7218903518,"136":550.0041941789,"137":549.7258202291,"138":549.9706205386,"139":549.9131085019,"140":550.015630376,"141":549.9989658193,"142":550.0275116785,"143":550.0184757935}} diff --git a/tests/references/neuralnet_test_ozone__CPU_theano.log b/tests/references/neuralnet_test_ozone__CPU_theano.log index e7f661b16..72aae4445 100644 --- a/tests/references/neuralnet_test_ozone__CPU_theano.log +++ b/tests/references/neuralnet_test_ozone__CPU_theano.log @@ -3,262 +3,238 @@ Using Theano backend. /home/antoine/.local/lib/python3.8/site-packages/theano/configdefaults.py:1952: UserWarning: Theano does not recognise this flag: lib.cnmem warnings.warn('Theano does not recognise this flag: {0}'.format(key)) WARNING:root:Limited tf.compat.v2.summary API due to missing TensorBoard installation. -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20490' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20489' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20492' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:theano.gof.compilelock:Waiting for existing lock by process '20491' (I am process '20180') -INFO:theano.gof.compilelock:To manually release the lock, delete /home/antoine/.theano/compiledir_Linux-5.7--amd64-x86_64-with-glibc2.29--3.8.4-64/lock_dir -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 55 0.3228 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 55 0.349 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 59 0.3506 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 59 0.3201 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 51 0.2964 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 51 0.2349 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.2429 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.2269 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.2562 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.267 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 83 0.2612 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 83 0.2552 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.2369 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.2537 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.2326 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.1922 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 67 0.2181 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 67 0.2432 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.3398 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.3867 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.3672 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.2592 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 67 0.3459 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 67 0.3537 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.4199 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.2485 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.3588 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.9682 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.227 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 2.1686 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 5.406 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 1.732 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 1.7069 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.2537 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 1.8213 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 2.5386 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.9365 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 6.2099 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.4038 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 3.2414 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.3312 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 1.1145 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 2.4513 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 2.3964 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 7.0377 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 1.5478 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 2.8629 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 3.8353 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.4561 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 55 0.3585 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 55 0.3134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 59 0.3957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 59 0.3492 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 51 0.3705 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 51 0.2427 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.2615 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.2151 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.2479 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.2025 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 83 0.2618 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 83 0.2625 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.245 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.2576 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.2586 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.1947 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 67 0.2517 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 67 0.286 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.3106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.3576 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.3635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.2691 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 67 0.3897 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 67 0.3795 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.5149 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.3984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 2.0908 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 4.9798 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.5775 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 2.6064 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.7677 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 8.3628 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.3012 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 1.3417 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 1.0957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.7066 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.1564 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 5.1799 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 4.8703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 5.4118 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 2.3484 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 2.8358 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.4844 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 2.3463 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 5.6435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 6.2992 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 2.3923 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 3.0496 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 302.5056 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 15397914.8652 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.4637 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.4637 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 2.7165 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 4.2855 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.4637 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 4781.7999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.4634 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.5823 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 1264.849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 1.0115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 66556665.9435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.465 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 904.2468 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.4637 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.4637 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.4639 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 7.998 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4637 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5374 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 9.7949 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 97.5453 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 18.3999 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 2460.4644 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 2768630.196 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 67040794.9223 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 60.8511 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 9364.3397 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 92.6462 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 67040794.3863 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 67040794.9705 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 19420884.6831 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 190.9901 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 67040795.2719 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 128901.1483 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 1.7484 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 3.1025 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 1.5452 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 2.1402 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.9086 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.5418 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.2414 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.3973 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.244 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.4056 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.1851 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.2867 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.5569 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.5184 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5261 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.1725 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.6048 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 0.1879 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.19 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.44 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 1.296 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.2322 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 1.2203 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 0.2664 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 55 0.185 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 55 0.202 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_LSTM(51) 59 0.1792 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_MLP(51) 59 0.2164 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_LSTM(51) 51 0.1703 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_MLP(51) 51 0.1894 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 87 0.2084 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 87 0.1836 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_LSTM(51) 91 0.214 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_MLP(51) 91 0.2077 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_LSTM(51) 83 0.2021 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_MLP(51) 83 0.2217 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1723 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1891 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_LSTM(51) 75 0.2436 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_MLP(51) 75 0.1913 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_LSTM(51) 67 0.2401 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_MLP(51) 67 0.1965 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1613 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1835 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_LSTM(51) 75 0.1831 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_MLP(51) 75 0.1525 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_LSTM(51) 67 0.1971 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_MLP(51) 67 0.1885 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 0.1528 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 0.3101 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_LSTM(51) 91 0.9605 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_MLP(51) 91 0.29 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_LSTM(51) 83 0.5655 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_MLP(51) 83 2.0427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 4.348 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 1.3877 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_LSTM(51) 123 0.9375 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_MLP(51) 123 0.2489 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_LSTM(51) 115 1.6017 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_MLP(51) 115 1.824 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 1.3596 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 4.0548 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_LSTM(51) 107 0.1753 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_MLP(51) 107 2.16 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_LSTM(51) 99 0.4668 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_MLP(51) 99 0.6952 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.4899 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.6458 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_LSTM(51) 107 3.2074 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_MLP(51) 107 1.2263 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_LSTM(51) 99 0.5474 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_MLP(51) 99 0.877 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 0.5615 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 0.5681 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_LSTM(51) 91 0.5668 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_MLP(51) 91 0.5684 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_LSTM(51) 83 75.6919 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_MLP(51) 83 2.803 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.5684 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 46.9157 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_LSTM(51) 123 0.4998 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_MLP(51) 123 24.8743 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_LSTM(51) 115 0.5686 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_MLP(51) 115 0.5683 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 2.5911 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.5684 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_LSTM(51) 107 2.963 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_MLP(51) 107 43.4915 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_LSTM(51) 99 562.5496 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_MLP(51) 99 5011.0756 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 6178888.9596 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 52667817.101 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_LSTM(51) 107 3393474.6247 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_MLP(51) 107 242.24 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_LSTM(51) 99 13002318.4871 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_MLP(51) 99 17995.7341 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 1.8454 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 2.7423 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_LSTM(51) 91 1.63 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_MLP(51) 91 1.8441 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_LSTM(51) 83 1.0783 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_MLP(51) 83 0.6186 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.2632 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.2856 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_LSTM(51) 123 0.2663 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_MLP(51) 123 0.3064 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_LSTM(51) 115 0.2108 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_MLP(51) 115 0.2435 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.2759 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.3073 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_LSTM(51) 107 0.3471 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_MLP(51) 107 0.2072 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_LSTM(51) 99 0.2875 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_MLP(51) 99 0.2107 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.5539 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.3537 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_LSTM(51) 107 0.7497 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_MLP(51) 107 0.2556 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_LSTM(51) 99 0.7387 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_MLP(51) 99 0.2799 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 1309.1332459449768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.6632 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 67040795.3136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 67040795.1497 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 25937257.1739 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 66370.4322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 1.8774 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 2.0628 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 1.6872 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 1.5196 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 1.0258 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.4959 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.2703 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.4568 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.4596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.221 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.3667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.5134 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4176 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5351 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.2402 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.595 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 0.2705 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.2274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.5006 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 1.2578 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.3899 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 1.2366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 0.3862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 55 0.1962 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 55 0.1976 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_LSTM(51) 59 0.1863 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_MLP(51) 59 0.2735 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_LSTM(51) 51 0.1695 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_MLP(51) 51 0.2015 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 87 0.2237 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 87 0.2197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_LSTM(51) 91 0.2259 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_MLP(51) 91 0.1782 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_LSTM(51) 83 0.222 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_MLP(51) 83 0.1883 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_MLP(51) 71 0.2171 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_LSTM(51) 75 0.3002 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_MLP(51) 75 0.1375 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_LSTM(51) 67 0.291 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_MLP(51) 67 0.1791 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1771 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1761 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_LSTM(51) 75 0.1982 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_MLP(51) 75 0.1616 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_LSTM(51) 67 0.1947 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_MLP(51) 67 0.1998 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 0.7921 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 0.5986 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_LSTM(51) 91 0.5853 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_MLP(51) 91 2.9298 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_LSTM(51) 83 0.4353 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_MLP(51) 83 1.3218 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.3394 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 5.9618 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_LSTM(51) 123 0.3448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_MLP(51) 123 0.9976 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_LSTM(51) 115 0.6126 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_MLP(51) 115 0.5493 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.1368 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 3.2976 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_LSTM(51) 107 3.7139 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_MLP(51) 107 3.488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_LSTM(51) 99 1.9924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_MLP(51) 99 1.7026 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.5043 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.5756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_LSTM(51) 107 1.8744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_MLP(51) 107 1.765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_LSTM(51) 99 0.599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_MLP(51) 99 0.7769 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 1239.5575 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 191307.0569 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_LSTM(51) 91 0.5694 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_MLP(51) 91 66.862 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_LSTM(51) 83 21.0966 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_MLP(51) 83 16837336.4659 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.5768 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 1.6951 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_LSTM(51) 123 0.568 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_MLP(51) 123 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_LSTM(51) 115 0.5682 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_MLP(51) 115 53.7055 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 773141.4744 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 37604290.0676 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_LSTM(51) 107 201.5106 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_MLP(51) 107 117.3908 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_LSTM(51) 99 0.5681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_MLP(51) 99 77.9364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 10806919.1536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.5658 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_LSTM(51) 107 16842250.6241 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_MLP(51) 107 13248517.1851 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_LSTM(51) 99 15533218.0509 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_MLP(51) 99 34273.1366 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 1.8804 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 1.7825 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_LSTM(51) 91 1.6453 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_MLP(51) 91 1.1139 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_LSTM(51) 83 1.1243 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_MLP(51) 83 0.3287 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.2759 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.3086 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_LSTM(51) 123 0.2848 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_MLP(51) 123 0.2806 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_LSTM(51) 115 0.2179 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_MLP(51) 115 0.2784 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.2667 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.2486 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_LSTM(51) 107 0.4496 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_MLP(51) 107 0.2354 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_LSTM(51) 99 0.4297 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_MLP(51) 99 0.2344 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.6598 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.3599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_LSTM(51) 107 0.7627 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_MLP(51) 107 0.2757 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_LSTM(51) 99 0.7584 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_MLP(51) 99 0.2879 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 974.1313784122467 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)' [PolyTrend + Seasonal_MonthOfYear + LSTM(51)] -INFO:pyaf.std:TREND_DETAIL '_Ozone_PolyTrend' [PolyTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)' [LSTM(51)] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1019 MAPE_Forecast=0.1613 MAPE_Test=0.3398 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0972 SMAPE_Forecast=0.1542 SMAPE_Test=0.2883 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4211 MASE_Forecast=0.6403 MASE_Test=1.7897 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.37011499425023997 L1_Forecast=0.4970492879526981 L1_Test=0.8460567430838161 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.5549147083016078 L2_Forecast=0.6173755653473005 L2_Test=0.9531228457629786 -INFO:pyaf.std:MODEL_COMPLEXITY 71 +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)' [LinearTrend + Cycle_None + MLP(51)] +INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_bestCycle_byMAPE' [Cycle_None] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)' [MLP(51)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1619 MAPE_Forecast=0.1375 MAPE_Test=0.1947 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1638 SMAPE_Forecast=0.1499 SMAPE_Test=0.2035 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7474 MASE_Forecast=0.6021 MASE_Test=0.9475 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.6569105792687696 L1_Forecast=0.4674333294567373 L1_Test=0.44789993307718295 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9010114714440033 L2_Forecast=0.6990717696112625 L2_Test=0.513633768124512 +INFO:pyaf.std:MODEL_COMPLEXITY 75 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (5.161127322430005, array([-2.3971979 , -0.44987431, 1.1799632 ])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_PolyTrend_residue_Seasonal_MonthOfYear -0.027148841187878858 {1: -1.712786405574908, 2: -1.4674755026528925, 3: -0.8891695609010877, 4: -0.22811041171681623, 5: -0.12272742114544766, 6: 0.7107545164158466, 7: 1.3676800999398635, 8: 1.3990582743641302, 9: 1.0239538864888074, 10: 0.8859338153602634, 11: -0.5063790111465141, 12: -1.397296313966326} +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Ozone_LinearTrend_residue_bestCycle_byMAPE None 0.012969125577120266 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Inconsolata-g-Powerline.ttf: In FT2Font: Can not load face. Unknown file format. +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/TestHVAROne.otf: In FT2Font: Can not load face. INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /usr/share/fonts/truetype/unifont/unifont_sample.ttf: In FT2Font: Could not set the fontsize -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Unicode.org/fonts/AdobeVFPrototype-Subset.otf: In FT2Font: Can not load face. -INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/Apple Color Emoji.ttf: In FT2Font: Could not set the fontsize +INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/Apple/fonts/NISC18030.ttf: In FT2Font: Could not set the fontsize INFO:matplotlib.font_manager:Could not open font file /home/antoine/.fonts/UnicodeFonts/truetype/noto/NotoColorEmoji.ttf: In FT2Font: Can not load face. Unknown file format. INFO:matplotlib.font_manager:generated new fontManager -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 12.95119833946228 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 10.46910572052002 INFO:pyaf.std:START_FORECASTING '['Ozone']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 1.1198012828826904 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.6913001537322998 INFO:pyaf.std:START_TRAINING 'Ozone' Month Ozone Time 0 1955-01 2.7 1955-01-01 @@ -267,20 +243,19 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.2592 1.4177 -1 None Diff_Ozone ... 0.4199 1.9538 -2 None _Ozone ... 0.3398 1.7897 -3 None _Ozone ... 0.2964 1.5503 -4 None _Ozone ... 0.2369 1.0869 +0 None Diff_Ozone ... 0.1564 0.7514 +1 None _Ozone ... 0.1947 0.9475 +2 None _Ozone ... 0.2691 1.3895 +3 None _Ozone ... 0.3705 1.7976 +4 None _Ozone ... 0.2450 1.1420 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', - 'Time_Normalized_^2', 'Time_Normalized_^3', '_Ozone_PolyTrend', - '_Ozone_PolyTrend_residue', - '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear', - '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue', - '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)', - '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)_residue', + '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', + '_Ozone_LinearTrend_residue_bestCycle_byMAPE', + '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue', + '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)', + '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -300,18 +275,18 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 2.553197 -205 1972-02-01 NaN 2.855487 -206 1972-03-01 NaN 3.460138 -207 1972-04-01 NaN 4.024380 -208 1972-05-01 NaN 4.172608 -209 1972-06-01 NaN 4.966478 -210 1972-07-01 NaN 5.686246 -211 1972-08-01 NaN 5.685405 -212 1972-09-01 NaN 5.249028 -213 1972-10-01 NaN 5.195516 -214 1972-11-01 NaN 3.865777 -215 1972-12-01 NaN 2.931199 +204 1972-01-01 NaN 4.576986 +205 1972-02-01 NaN 5.261625 +206 1972-03-01 NaN 3.830884 +207 1972-04-01 NaN 4.154520 +208 1972-05-01 NaN 2.979715 +209 1972-06-01 NaN 0.983938 +210 1972-07-01 NaN 2.540961 +211 1972-08-01 NaN 1.158529 +212 1972-09-01 NaN 1.101930 +213 1972-10-01 NaN 0.918416 +214 1972-11-01 NaN 1.920433 +215 1972-12-01 NaN 1.766505 @@ -330,18 +305,18 @@ Forecasts "Training_Signal_Length": 204 }, "Model": { - "AR_Model": "LSTM(51)", - "Best_Decomposition": "_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51)", - "Cycle": "Seasonal_MonthOfYear", + "AR_Model": "MLP(51)", + "Best_Decomposition": "_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)", + "Cycle": "Cycle_None", "Signal_Transoformation": "NoTransf", - "Trend": "PolyTrend" + "Trend": "LinearTrend" }, "Model_Performance": { - "COMPLEXITY": "71", - "MAE": "0.4970492879526981", - "MAPE": "0.1613", - "MASE": "0.6403", - "RMSE": "0.6173755653473005" + "COMPLEXITY": "75", + "MAE": "0.4674333294567373", + "MAPE": "0.1375", + "MASE": "0.6021", + "RMSE": "0.6990717696112625" } } } @@ -351,7 +326,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":2.2866025215,"193":2.5598511083,"194":3.1025994673,"195":3.7748461898,"196":3.8193939076,"197":4.8642668749,"198":5.3990388908,"199":5.5803580328,"200":5.3456233707,"201":4.9882558734,"202":3.6976891058,"203":3.0773060883,"204":2.5531969033,"205":2.8554870322,"206":3.4601382459,"207":4.0243803587,"208":4.1726079736,"209":4.966478428,"210":5.686246246,"211":5.6854051694,"212":5.2490277514,"213":5.1955158556,"214":3.8657771893,"215":2.9311987141}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":3.9129845717,"193":4.3892990457,"194":4.1266024388,"195":3.9863297507,"196":2.5140243517,"197":1.1607758636,"198":1.5782690463,"199":1.360918731,"200":1.0716554118,"201":2.3171946779,"202":2.7131622695,"203":2.9005255248,"204":4.5769864652,"205":5.2616246563,"206":3.8308839299,"207":4.1545196757,"208":2.9797149467,"209":0.9839382227,"210":2.5409608272,"211":1.1585294133,"212":1.1019304409,"213":0.9184155659,"214":1.9204330528,"215":1.7665054466}} @@ -361,213 +336,213 @@ Forecasts 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 55 0.3551 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 55 0.2577 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 59 0.335 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 59 0.4336 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 51 0.3527 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 51 0.3683 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.2255 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.2198 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.2567 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.2263 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 83 0.2606 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 83 0.2907 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.2327 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.2582 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.2317 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.1765 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 67 0.2423 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 67 0.258 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.3488 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.4554 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.3703 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.3675 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 67 0.4187 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 67 0.2376 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.5158 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 1.748 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.2627 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 8.2814 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.4879 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 1.4158 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 2.3375 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.5332 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.6949 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.3008 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.7104 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 2.9124 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.5421 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 2.9742 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 1.2602 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 5.0805 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.2347 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 1.5091 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 2.2717 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 1.503 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 3.0998 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 8.3962 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 2.3146 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 5.8912 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 496.5805 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.4606 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 364.3 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 5175.868 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.4634 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.4635 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.4635 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.464 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 55 0.3672 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 55 0.3466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 59 0.357 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 59 0.255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 51 0.433 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 51 0.2145 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 0.2363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.2177 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.2559 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.2433 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 83 0.2618 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 83 0.2343 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.2374 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.297 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.2535 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.1674 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 67 0.2175 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 67 0.1921 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 71 0.3549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 71 0.4253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 75 0.3653 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 75 0.3188 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 67 0.3753 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 67 0.3488 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 1.1446 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 7.4128 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 1.3731 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 0.6534 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 1.8058 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 5.0826 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.6536 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.8859 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 1.1083 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 2.3235 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.8255 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 1.2413 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.7889 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 7.511 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 3.7383 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.6849 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.6363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 2.7591 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 2.0783 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 7.2143 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.977 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 1.0358 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 5.0989 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 4.5854 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 3.0183 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 645532.8285 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 0.4635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 67040794.7657 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.4428 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.4637 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.4637 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.4975 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 173.4848 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.4637 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 8.6002 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 255.7064 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 20937.9237 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5555 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.459 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 218.4862 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 638.4753 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 67040794.3863 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 25731926.2848 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 35376.4158 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 110.4213 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 37365560.4282 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 22109751.6262 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 2.1917 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 2.6733 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 1.7731 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 2.0212 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 1.2732 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.5834 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.2406 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.4133 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.2753 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.4114 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.197 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.272 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.5398 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4651 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.5811 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.1833 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.6632 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 0.1849 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.1173 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4669 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 1.2628 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.2697 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 1.2888 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 0.2224 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 55 0.1886 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 55 0.1907 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_LSTM(51) 59 0.1755 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_MLP(51) 59 0.3044 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_LSTM(51) 51 0.1822 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_MLP(51) 51 0.2343 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 87 0.2064 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 87 0.1739 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_LSTM(51) 91 0.217 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_MLP(51) 91 0.2122 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_LSTM(51) 83 0.2093 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_MLP(51) 83 0.2059 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1776 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1809 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_LSTM(51) 75 0.2461 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_MLP(51) 75 0.1304 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_LSTM(51) 67 0.2465 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_MLP(51) 67 0.2159 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1608 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_MLP(51) 71 0.2168 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_LSTM(51) 75 0.1847 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_MLP(51) 75 0.1763 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_LSTM(51) 67 0.1959 -INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_MLP(51) 67 0.1642 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 0.1831 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 1.5999 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_LSTM(51) 91 0.7827 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_MLP(51) 91 5.3155 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_LSTM(51) 83 0.3218 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_MLP(51) 83 1.5254 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 1.1258 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.2279 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_LSTM(51) 123 0.4601 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_MLP(51) 123 0.2872 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_LSTM(51) 115 0.4232 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_MLP(51) 115 2.0737 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.4134 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 1.8004 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_LSTM(51) 107 1.1188 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_MLP(51) 107 3.4442 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_LSTM(51) 99 0.2969 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_MLP(51) 99 0.9047 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.4099 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.9125 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_LSTM(51) 107 0.5858 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_MLP(51) 107 3.4631 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_LSTM(51) 99 0.5835 -INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_MLP(51) 99 2.1703 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 1012.4542 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 0.6909 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_LSTM(51) 91 5576.6192 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_MLP(51) 91 462.1249 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_LSTM(51) 83 0.4594 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_MLP(51) 83 0.5781 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.589 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 256.909 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 4037.2597 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.4637 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 6.6618 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.4642 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 5240.7855 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 103518.7203 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 2059265.0605 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 67040795.3136 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 48352557.2845 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 180356.5435 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 33168458.3138 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 87 1.736 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 87 2.6378 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 91 1.7308 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_MLP(51) 91 1.7197 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_LSTM(51) 83 1.0507 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_MLP(51) 83 0.5152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 119 0.2635 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_MLP(51) 119 0.419 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_LSTM(51) 123 0.2693 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_MLP(51) 123 0.3996 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_LSTM(51) 115 0.2213 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_MLP(51) 115 0.3372 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 0.5173 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4381 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 0.639 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.3233 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_LSTM(51) 99 0.5822 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51) 99 0.2792 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_LSTM(51) 103 1.1612 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_MLP(51) 103 0.4409 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_LSTM(51) 107 1.2711 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_MLP(51) 107 0.3363 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_LSTM(51) 99 1.2827 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_MLP(51) 99 0.4702 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 55 0.1932 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 55 0.2082 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_LSTM(51) 59 0.1831 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_Cycle_MLP(51) 59 0.1995 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_LSTM(51) 51 0.1916 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_ConstantTrend_NoCycle_MLP(51) 51 0.209 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 87 0.2198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 87 0.1767 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_LSTM(51) 91 0.2352 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_Cycle_MLP(51) 91 0.2211 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_LSTM(51) 83 0.2211 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_Lag1Trend_NoCycle_MLP(51) 83 0.1854 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1683 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1698 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_LSTM(51) 75 0.2958 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_Cycle_None_MLP(51) 75 0.1547 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_LSTM(51) 67 0.274 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_LinearTrend_NoCycle_MLP(51) 67 0.1443 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 71 0.1732 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Seasonal_MonthOfYear_MLP(51) 71 0.1876 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_LSTM(51) 75 0.1836 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_Cycle_None_MLP(51) 75 0.1681 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_LSTM(51) 67 0.1965 +INFO:pyaf.std:collectPerformanceIndices : MAPE None _Ozone NoTransf_PolyTrend_NoCycle_MLP(51) 67 0.2331 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 0.3756 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 4.8678 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_LSTM(51) 91 0.2861 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_Cycle_MLP(51) 91 0.9316 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_LSTM(51) 83 0.5202 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_ConstantTrend_NoCycle_MLP(51) 83 3.1364 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.3161 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.4984 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_LSTM(51) 123 0.546 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_Cycle_None_MLP(51) 123 1.6155 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_LSTM(51) 115 0.8887 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_Lag1Trend_NoCycle_MLP(51) 115 0.933 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 1.365 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 4.9466 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_LSTM(51) 107 2.9596 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_Cycle_None_MLP(51) 107 0.2115 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_LSTM(51) 99 0.6778 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_LinearTrend_NoCycle_MLP(51) 99 1.7549 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.4599 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 3.1699 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_LSTM(51) 107 1.1415 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_Cycle_MLP(51) 107 1.9542 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_LSTM(51) 99 1.8198 +INFO:pyaf.std:collectPerformanceIndices : MAPE None Diff_Ozone Difference_PolyTrend_NoCycle_MLP(51) 99 1.0337 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 50.7247 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 0.57 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_LSTM(51) 91 0.6191 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_Cycle_None_MLP(51) 91 22112.6879 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_LSTM(51) 83 0.6915 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_ConstantTrend_NoCycle_MLP(51) 83 43806232.7633 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.5572 INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.5684 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_LSTM(51) 123 0.5684 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_MLP(51) 123 0.5683 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_LSTM(51) 115 0.5754 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_MLP(51) 115 4.1696 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 70.8872 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 1589.0072 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_LSTM(51) 107 25.7601 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_MLP(51) 107 1.5652 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_LSTM(51) 99 588.4892 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_MLP(51) 99 2005.5927 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 42807160.2037 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 9873383.5357 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_LSTM(51) 107 1626.3264 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_MLP(51) 107 202.8211 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_LSTM(51) 99 12475851.4268 -INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_MLP(51) 99 6555113.4984 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 2.0805 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 2.3625 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_LSTM(51) 91 1.8591 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_MLP(51) 91 1.745 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_LSTM(51) 83 1.1087 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_MLP(51) 83 0.6673 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.2663 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.2625 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_LSTM(51) 123 0.2756 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_MLP(51) 123 0.2536 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_LSTM(51) 115 0.2205 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_MLP(51) 115 0.2427 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.2616 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.2809 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_LSTM(51) 107 0.3416 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_MLP(51) 107 0.2149 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_LSTM(51) 99 0.3499 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_MLP(51) 99 0.2021 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.6793 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.3574 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_LSTM(51) 107 0.75 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_MLP(51) 107 0.2695 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_LSTM(51) 99 0.7498 -INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_MLP(51) 99 0.2268 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 242.65574836730957 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_LSTM(51) 123 0.5647 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_Cycle_MLP(51) 123 0.7095 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_LSTM(51) 115 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_Lag1Trend_NoCycle_MLP(51) 115 33.3888 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 1212.1401 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.5684 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_LSTM(51) 107 0.5278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_Cycle_None_MLP(51) 107 0.5762 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_LSTM(51) 99 0.7716 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_LinearTrend_NoCycle_MLP(51) 99 6462.162 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 1390.8189 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 115237.9924 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_LSTM(51) 107 18392064.6724 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_Cycle_MLP(51) 107 10532091.5816 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_LSTM(51) 99 5399.6282 +INFO:pyaf.std:collectPerformanceIndices : MAPE None RelDiff_Ozone RelativeDifference_PolyTrend_NoCycle_MLP(51) 99 935942.0332 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_LSTM(51) 87 2.1116 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Seasonal_MonthOfYear_MLP(51) 87 2.2805 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_LSTM(51) 91 1.5868 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_Cycle_MLP(51) 91 1.152 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_LSTM(51) 83 1.2885 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_ConstantTrend_NoCycle_MLP(51) 83 0.3301 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_LSTM(51) 119 0.2861 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Seasonal_MonthOfYear_MLP(51) 119 0.3117 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_LSTM(51) 123 0.289 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_Cycle_MLP(51) 123 0.2658 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_LSTM(51) 115 0.2322 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_Lag1Trend_NoCycle_MLP(51) 115 0.2765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.2743 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Seasonal_MonthOfYear_MLP(51) 103 0.2448 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_LSTM(51) 107 0.3765 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_Cycle_None_MLP(51) 107 0.253 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_LSTM(51) 99 0.3794 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_LinearTrend_NoCycle_MLP(51) 99 0.2576 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_LSTM(51) 103 0.6278 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Seasonal_MonthOfYear_MLP(51) 103 0.3602 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_LSTM(51) 107 0.7532 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_Cycle_None_MLP(51) 107 0.2719 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_LSTM(51) 99 0.7389 +INFO:pyaf.std:collectPerformanceIndices : MAPE None CumSum_Ozone Integration_PolyTrend_NoCycle_MLP(51) 99 0.3212 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 203.24377131462097 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401185 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)' [LinearTrend + Cycle_None + MLP(51)] +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)' [LinearTrend + NoCycle + MLP(51)] INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_bestCycle_byMAPE' [Cycle_None] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)' [MLP(51)] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.7411 MAPE_Forecast=0.7659 MAPE_Test=0.74 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.6008 SMAPE_Forecast=0.8688 SMAPE_Test=0.8437 -INFO:pyaf.std:MODEL_MASE MASE_Fit=2.8272 MASE_Forecast=3.0615 MASE_Test=3.8603 -INFO:pyaf.std:MODEL_L1 L1_Fit=2.484975673837583 L1_Forecast=2.3766678924940257 L1_Test=1.824867000768849 -INFO:pyaf.std:MODEL_L2 L2_Fit=2.9784368695519468 L2_Forecast=2.908296284707063 L2_Test=2.1635683110116113 -INFO:pyaf.std:MODEL_COMPLEXITY 75 +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)' [MLP(51)] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.3727 MAPE_Forecast=0.4205 MAPE_Test=0.4299 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3543 SMAPE_Forecast=0.529 SMAPE_Test=0.53 +INFO:pyaf.std:MODEL_MASE MASE_Fit=1.5439 MASE_Forecast=1.622 MASE_Test=2.0793 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.3570271355794186 L1_Forecast=1.259189355250377 L1_Test=0.9829284585103816 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.7172130603691027 L2_Forecast=1.5855451171430126 L2_Test=1.311352162482202 +INFO:pyaf.std:MODEL_COMPLEXITY 67 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END @@ -575,28 +550,28 @@ INFO:pyaf.std:TREND_DETAIL_START INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (5.022578136250887, array([-1.82712926])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Ozone_LinearTrend_residue_bestCycle_byMAPE None 0.012969125577120266 {} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_LinearTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_PLOTTING -INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 4.280886888504028 +INFO:pyaf.std:END_PLOTTING_TIME_IN_SECONDS 3.9990017414093018 INFO:pyaf.std:START_FORECASTING '['Ozone']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.7102987766265869 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.6021823883056641 Split Transformation ... TestMAPE TestMASE -0 None _Ozone ... 0.1765 0.8466 -1 None _Ozone ... 0.3488 1.8337 -2 None _Ozone ... 0.2376 1.2498 -3 None _Ozone ... 0.2198 1.0488 -4 None _Ozone ... 0.3350 1.6763 +0 None _Ozone ... 0.1921 0.9324 +1 None _Ozone ... 0.1674 0.8173 +2 None _Ozone ... 0.3188 1.6767 +3 None _Ozone ... 0.2374 1.1032 +4 None _Ozone ... 0.2970 1.2822 [5 rows x 20 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', '_Ozone_LinearTrend', '_Ozone_LinearTrend_residue', - '_Ozone_LinearTrend_residue_bestCycle_byMAPE', - '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue', - '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)', - '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)_residue', + '_Ozone_LinearTrend_residue_zeroCycle', + '_Ozone_LinearTrend_residue_zeroCycle_residue', + '_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)', + '_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)_residue', '_Ozone_Trend', '_Ozone_Trend_residue', '_Ozone_Cycle', '_Ozone_Cycle_residue', '_Ozone_AR', '_Ozone_AR_residue', '_Ozone_TransformedForecast', 'Ozone_Forecast', @@ -616,18 +591,18 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 0.834821 -205 1972-02-01 NaN 3.076219 -206 1972-03-01 NaN 2.542779 -207 1972-04-01 NaN 3.716504 -208 1972-05-01 NaN 3.742330 -209 1972-06-01 NaN 4.151718 -210 1972-07-01 NaN 2.887562 -211 1972-08-01 NaN 2.784342 -212 1972-09-01 NaN 1.807982 -213 1972-10-01 NaN 0.919284 -214 1972-11-01 NaN 0.215240 -215 1972-12-01 NaN 2.316296 +204 1972-01-01 NaN 1.405321 +205 1972-02-01 NaN 2.096524 +206 1972-03-01 NaN 2.045986 +207 1972-04-01 NaN 2.888041 +208 1972-05-01 NaN 2.326756 +209 1972-06-01 NaN 2.472293 +210 1972-07-01 NaN 3.694481 +211 1972-08-01 NaN 2.216916 +212 1972-09-01 NaN 2.284755 +213 1972-10-01 NaN 2.065581 +214 1972-11-01 NaN 1.111479 +215 1972-12-01 NaN 0.730354 @@ -647,17 +622,17 @@ Forecasts }, "Model": { "AR_Model": "MLP(51)", - "Best_Decomposition": "_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_MLP(51)", - "Cycle": "Cycle_None", + "Best_Decomposition": "_Ozone_LinearTrend_residue_zeroCycle_residue_MLP(51)", + "Cycle": "NoCycle", "Signal_Transoformation": "NoTransf", "Trend": "LinearTrend" }, "Model_Performance": { - "COMPLEXITY": "75", - "MAE": "2.3766678924940257", - "MAPE": "0.7659", - "MASE": "3.0615", - "RMSE": "2.908296284707063" + "COMPLEXITY": "67", + "MAE": "1.259189355250377", + "MAPE": "0.4205", + "MASE": "1.622", + "RMSE": "1.5855451171430126" } } } @@ -667,7 +642,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.1726430276,"193":1.6119011747,"194":2.4964373983,"195":2.577899681,"196":6.0493591176,"197":3.9395795698,"198":4.5494668898,"199":4.1020230954,"200":2.0002328945,"201":1.4943175093,"202":1.84887084,"203":0.82862471,"204":0.8348213765,"205":3.0762186867,"206":2.5427786209,"207":3.7165038571,"208":3.7423299121,"209":4.1517183836,"210":2.8875618217,"211":2.7843424207,"212":1.8079817905,"213":0.9192838863,"214":0.2152404273,"215":2.3162956502}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":2.0,"194":2.2,"195":3.0,"196":2.4,"197":3.5,"198":3.5,"199":3.3,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.4228758433,"193":1.6328182327,"194":2.0264660395,"195":0.9845500752,"196":3.2613484608,"197":3.3306405778,"198":3.8888914999,"199":4.2141610091,"200":3.6760833455,"201":1.1557646767,"202":1.1271406674,"203":2.8039877857,"204":1.4053211543,"205":2.0965237242,"206":2.0459858276,"207":2.8880413756,"208":2.3267563748,"209":2.472293144,"210":3.6944805024,"211":2.2169158584,"212":2.2847547247,"213":2.065581222,"214":1.1114788735,"215":0.7303538587}} diff --git a/tests/references/perf_test_cycles_full_long_long.log b/tests/references/perf_test_cycles_full_long_long.log index 1d9486215..e69de29bb 100644 --- a/tests/references/perf_test_cycles_full_long_long.log +++ b/tests/references/perf_test_cycles_full_long_long.log @@ -1,744 +0,0 @@ -INFO:pyaf.std:START_TRAINING 'Signal' -TEST_CYCLES_START 2 -GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_2_None_0.1_0 -TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 110.21578073501587 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-02T02:00:00.000000 TimeDelta= Horizon=4 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=1.8491743129800016 Mean=1.465622874891886 StdDev=0.09943794145139832 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=1.8491743129800016 Mean=1.465622874891886 StdDev=0.09943794145139832 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0543 MAPE_Forecast=0.0556 MAPE_Test=0.0507 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0539 SMAPE_Forecast=0.0552 SMAPE_Test=0.0488 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7069 MASE_Forecast=0.7113 MASE_Test=0.6606 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.0788026742868748 L1_Forecast=0.08061744963149645 L1_Test=0.06906760489553276 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09905795697203003 L2_Forecast=0.10094976147793763 L2_Test=0.0891003145657369 -INFO:pyaf.std:MODEL_COMPLEXITY 0 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.4657142713807132 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Signal_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING '['Signal']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 5.727567434310913 -INFO:pyaf.std:START_TRAINING 'Signal' -Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', - '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - '_Signal_ConstantTrend_residue_zeroCycle', - '_Signal_ConstantTrend_residue_zeroCycle_residue', - '_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR', - '_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', - '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', - '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', - '_Signal_TransformedForecast', 'Signal_Forecast', - '_Signal_TransformedResidue', 'Signal_Residue', - 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], - dtype='object') - -RangeIndex: 31992 entries, 0 to 31991 -Data columns (total 3 columns): - # Column Non-Null Count Dtype ---- ------ -------------- ----- - 0 Date 31992 non-null datetime64[ns] - 1 Signal 31988 non-null float64 - 2 Signal_Forecast 31992 non-null float64 -dtypes: datetime64[ns](1), float64(2) -memory usage: 749.9 KB -None -Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 1.4657142713807132] - [Timestamp('2003-08-25 21:00:00') nan 1.4657142713807132] - [Timestamp('2003-08-25 22:00:00') nan 1.4657142713807132] - [Timestamp('2003-08-25 23:00:00') nan 1.4657142713807132]] - - - -{ - "Signal": { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 4, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" - }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", - "Cycle": "NoCycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "0", - "MAE": "0.08061744963149645", - "MAPE": "0.0556", - "MASE": "0.7113", - "RMSE": "0.10094976147793763" - } - } -} - - - - - - -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null},"Signal_Forecast":{"31988":1.4657142714,"31989":1.4657142714,"31990":1.4657142714,"31991":1.4657142714}} - - - -TEST_CYCLES_END 2 -TEST_CYCLES_START 6 -GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_6_None_0.1_0 -TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 76.19726920127869 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T19:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=8.333821360571514 Mean=4.6514340347620795 StdDev=2.1536083159682464 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.333821360571514 Mean=4.6514340347620795 StdDev=2.1536083159682464 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR' [ConstantTrend + Seasonal_Hour + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour' [Seasonal_Hour] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0241 MAPE_Forecast=0.0243 MAPE_Test=0.03 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.024 SMAPE_Forecast=0.0242 SMAPE_Test=0.0299 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.024 MASE_Forecast=0.024 MASE_Test=0.0238 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08014185956783082 L1_Forecast=0.08013380214010248 L1_Test=0.07923594759930998 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10041282577628087 L2_Forecast=0.10041392569948236 L2_Test=0.09087476936115621 -INFO:pyaf.std:MODEL_COMPLEXITY 4 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.651309216902765 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_Hour 0.0005642027933792093 {0: 0.00112145507781225, 1: -3.3318496867926393, 2: -0.0018319620987155005, 3: 3.340988602011187, 4: -1.6749864737829494, 5: 1.6657038775346944, 6: 0.0022965144751019295, 7: -3.327901785222622, 8: -0.0025588018864155515, 9: 3.3283914344642094, 10: -1.666704273052028, 11: 1.6633980485526036, 12: 0.005034145528322931, 13: -3.3389129989187687, 14: -0.005518464672188195, 15: 3.335847325764391, 16: -1.6693022437105134, 17: 1.6671641840878966, 18: 0.0038605214739781957, 19: -3.326517819482879, 20: 0.0049681482250516495, 21: 3.327643434132467, 22: -1.6661372996024961, 23: 1.6586381732888729} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING '['Signal']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 6.876359701156616 -INFO:pyaf.std:START_TRAINING 'Signal' -Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', - '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - '_Signal_ConstantTrend_residue_Seasonal_Hour', - '_Signal_ConstantTrend_residue_Seasonal_Hour_residue', - '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR', - '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR_residue', - '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', - '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', - '_Signal_TransformedForecast', 'Signal_Forecast', - '_Signal_TransformedResidue', 'Signal_Residue', - 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], - dtype='object') - -RangeIndex: 32000 entries, 0 to 31999 -Data columns (total 3 columns): - # Column Non-Null Count Dtype ---- ------ -------------- ----- - 0 Date 32000 non-null datetime64[ns] - 1 Signal 31988 non-null float64 - 2 Signal_Forecast 32000 non-null float64 -dtypes: datetime64[ns](1), float64(2) -memory usage: 750.1 KB -None -Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 4.656277365127817] - [Timestamp('2003-08-25 21:00:00') nan 7.978952651035232] - [Timestamp('2003-08-25 22:00:00') nan 2.9851719173002693] - [Timestamp('2003-08-25 23:00:00') nan 6.309947390191638] - [Timestamp('2003-08-26 00:00:00') nan 4.652430671980578] - [Timestamp('2003-08-26 01:00:00') nan 1.3194595301101262] - [Timestamp('2003-08-26 02:00:00') nan 4.64947725480405] - [Timestamp('2003-08-26 03:00:00') nan 7.992297818913952] - [Timestamp('2003-08-26 04:00:00') nan 2.976322743119816] - [Timestamp('2003-08-26 05:00:00') nan 6.31701309443746] - [Timestamp('2003-08-26 06:00:00') nan 4.653605731377867] - [Timestamp('2003-08-26 07:00:00') nan 1.3234074316801436]] - - - -{ - "Signal": { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 12, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" - }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_Hour_residue_NoAR", - "Cycle": "Seasonal_Hour", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "0.08013380214010248", - "MAPE": "0.0243", - "MASE": "0.024", - "RMSE": "0.10041392569948236" - } - } -} - - - - - - -{"Date":{"31988":"2003-08-25T20:00:00.000Z","31989":"2003-08-25T21:00:00.000Z","31990":"2003-08-25T22:00:00.000Z","31991":"2003-08-25T23:00:00.000Z","31992":"2003-08-26T00:00:00.000Z","31993":"2003-08-26T01:00:00.000Z","31994":"2003-08-26T02:00:00.000Z","31995":"2003-08-26T03:00:00.000Z","31996":"2003-08-26T04:00:00.000Z","31997":"2003-08-26T05:00:00.000Z","31998":"2003-08-26T06:00:00.000Z","31999":"2003-08-26T07:00:00.000Z"},"Signal":{"31988":null,"31989":null,"31990":null,"31991":null,"31992":null,"31993":null,"31994":null,"31995":null,"31996":null,"31997":null,"31998":null,"31999":null},"Signal_Forecast":{"31988":4.6562773651,"31989":7.978952651,"31990":2.9851719173,"31991":6.3099473902,"31992":4.652430672,"31993":1.3194595301,"31994":4.6494772548,"31995":7.9922978189,"31996":2.9763227431,"31997":6.3170130944,"31998":4.6536057314,"31999":1.3234074317}} - - - -TEST_CYCLES_END 6 -TEST_CYCLES_START 10 -GENERATING_RANDOM_DATASET Signal_32000_H_0_constant_10_None_0.1_0 -TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 103.08982467651367 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-12-01T13:00:00.000000 TimeDelta= Horizon=20 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=31988 Min=1.0 Max=8.700169238172657 Mean=4.751653298073094 StdDev=2.247677208865058 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=8.700169238172657 Mean=4.751653298073094 StdDev=2.247677208865058 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0228 MAPE_Forecast=0.0233 MAPE_Test=0.0221 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0227 SMAPE_Forecast=0.0232 SMAPE_Test=0.0218 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0263 MASE_Forecast=0.0269 MASE_Test=0.0243 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07878495938794243 L1_Forecast=0.08064743691160367 L1_Test=0.07585865642956542 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09903920885077316 L2_Forecast=0.10097896423433593 L2_Test=0.0954013346310342 -INFO:pyaf.std:MODEL_COMPLEXITY 8 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.751863564238395 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 10 0.1146752547368104 {0: -2.4013563201199983, 1: 1.602211122589586, 2: 3.599158544297013, 3: -2.398889422062649, 4: -0.40125388302339715, 5: -3.4021823361080994, 6: -1.4036668502047256, 7: 1.5970111896443058, 8: 0.6021353433222743, 9: 2.6017161805354796} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING '['Signal']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 7.690162420272827 -INFO:pyaf.std:START_TRAINING 'Signal' -Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', - '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', - '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', - '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', - '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', - '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', - '_Signal_TransformedForecast', 'Signal_Forecast', - '_Signal_TransformedResidue', 'Signal_Residue', - 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], - dtype='object') - -RangeIndex: 32008 entries, 0 to 32007 -Data columns (total 3 columns): - # Column Non-Null Count Dtype ---- ------ -------------- ----- - 0 Date 32008 non-null datetime64[ns] - 1 Signal 31988 non-null float64 - 2 Signal_Forecast 32008 non-null float64 -dtypes: datetime64[ns](1), float64(2) -memory usage: 750.3 KB -None -Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 5.353998907560669] - [Timestamp('2003-08-25 21:00:00') nan 7.353579744773874] - [Timestamp('2003-08-25 22:00:00') nan 2.3505072441183965] - [Timestamp('2003-08-25 23:00:00') nan 6.354074686827981] - [Timestamp('2003-08-26 00:00:00') nan 8.351022108535407] - [Timestamp('2003-08-26 01:00:00') nan 2.352974142175746] - [Timestamp('2003-08-26 02:00:00') nan 4.350609681214998] - [Timestamp('2003-08-26 03:00:00') nan 1.3496812281302955] - [Timestamp('2003-08-26 04:00:00') nan 3.3481967140336693] - [Timestamp('2003-08-26 05:00:00') nan 6.348874753882701] - [Timestamp('2003-08-26 06:00:00') nan 5.353998907560669] - [Timestamp('2003-08-26 07:00:00') nan 7.353579744773874] - [Timestamp('2003-08-26 08:00:00') nan 2.3505072441183965] - [Timestamp('2003-08-26 09:00:00') nan 6.354074686827981] - [Timestamp('2003-08-26 10:00:00') nan 8.351022108535407] - [Timestamp('2003-08-26 11:00:00') nan 2.352974142175746] - [Timestamp('2003-08-26 12:00:00') nan 4.350609681214998] - [Timestamp('2003-08-26 13:00:00') nan 1.3496812281302955] - [Timestamp('2003-08-26 14:00:00') nan 3.3481967140336693] - [Timestamp('2003-08-26 15:00:00') nan 6.348874753882701]] - - - -{ - "Signal": { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 20, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" - }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": 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'_Signal_ConstantTrend_residue_Seasonal_HourOfWeek' [Seasonal_HourOfWeek] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0214 MAPE_Forecast=0.0223 MAPE_Test=0.0174 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0213 SMAPE_Forecast=0.0222 SMAPE_Test=0.0174 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0365 MASE_Forecast=0.0377 MASE_Test=0.0294 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07841817742208272 L1_Forecast=0.08106350300805767 L1_Test=0.06378458189608335 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.09893948792096707 L2_Forecast=0.10150581080497878 L2_Test=0.08388505168971785 -INFO:pyaf.std:MODEL_COMPLEXITY 4 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 4.914135611511643 -INFO:pyaf.std:TREND_DETAIL_END 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[Timestamp('2003-08-26 02:00:00') nan 4.151050185084388] - [Timestamp('2003-08-26 03:00:00') nan 2.00184785125818] - [Timestamp('2003-08-26 04:00:00') nan 3.431419672994254] - [Timestamp('2003-08-26 05:00:00') nan 5.568502443278032] - [Timestamp('2003-08-26 06:00:00') nan 4.864240099171656] - [Timestamp('2003-08-26 07:00:00') nan 6.288243687752381] - [Timestamp('2003-08-26 08:00:00') nan 6.29614579502398] - [Timestamp('2003-08-26 09:00:00') nan 9.15684187142951] - [Timestamp('2003-08-26 10:00:00') nan 7.71828125847931] - [Timestamp('2003-08-26 11:00:00') nan 1.2884162444152008] - [Timestamp('2003-08-26 12:00:00') nan 2.7137216400608395] - [Timestamp('2003-08-26 13:00:00') nan 5.576918865475979] - [Timestamp('2003-08-26 14:00:00') nan 7.008096744787299] - [Timestamp('2003-08-26 15:00:00') nan 2.725705095531281] - [Timestamp('2003-08-26 16:00:00') nan 4.1442788461224165] - [Timestamp('2003-08-26 17:00:00') nan 2.014860219949029] - [Timestamp('2003-08-26 18:00:00') nan 3.4552919877871044] - [Timestamp('2003-08-26 19:00:00') nan 5.579804774081413] - [Timestamp('2003-08-26 20:00:00') nan 4.842070680562014] - [Timestamp('2003-08-26 21:00:00') nan 6.288141491587831] - [Timestamp('2003-08-26 22:00:00') nan 6.300583287298524] - [Timestamp('2003-08-26 23:00:00') nan 9.15731294483159]] - - - -{ - "Signal": { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 28, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" - }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_HourOfWeek_residue_NoAR", - "Cycle": "Seasonal_HourOfWeek", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "4", - "MAE": "0.08106350300805767", - "MAPE": "0.0223", - "MASE": "0.0377", - "RMSE": "0.10150581080497878" - } - } -} - - - - - - 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'_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0185 MAPE_Forecast=0.0181 MAPE_Test=0.0185 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0185 SMAPE_Forecast=0.0181 SMAPE_Test=0.0181 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.023 MASE_Forecast=0.0227 MASE_Test=0.0198 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.07958924340299912 L1_Forecast=0.07832565396296981 L1_Test=0.07002568368804168 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10011698257279802 L2_Forecast=0.09829060464825726 L2_Test=0.0852649926930095 -INFO:pyaf.std:MODEL_COMPLEXITY 8 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 5.633453794953687 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 18 -0.6718146495871293 {0: -3.1791669103965314, 1: -0.9577375212383066, 2: 0.146711186111816, 3: -2.622064593343656, 4: -1.5152844165964323, 5: 1.8191817883665733, 6: -4.290428403648969, 7: -1.5097390376254975, 8: -0.9561993521071699, 9: 2.935819719171061, 10: 4.599750234741594, 11: -2.0663613648920727, 12: 2.379912394752645, 13: -0.406214935857629, 14: 0.15951855376602087, 15: 4.044687276052934, 16: -2.061640301708385, 17: 3.4861072437956375} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING '['Signal']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 9.716177463531494 -INFO:pyaf.std:START_TRAINING 'Signal' -Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', - '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', - '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', - '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', - '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', - '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', - '_Signal_TransformedForecast', 'Signal_Forecast', - '_Signal_TransformedResidue', 'Signal_Residue', - 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], - dtype='object') - -RangeIndex: 32024 entries, 0 to 32023 -Data columns (total 3 columns): - # Column Non-Null Count Dtype ---- ------ -------------- ----- - 0 Date 32024 non-null datetime64[ns] - 1 Signal 31988 non-null float64 - 2 Signal_Forecast 32024 non-null float64 -dtypes: datetime64[ns](1), float64(2) -memory usage: 750.7 KB -None -Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 5.780164981065504] - [Timestamp('2003-08-25 21:00:00') nan 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14:00:00') nan 5.780164981065504] - [Timestamp('2003-08-26 15:00:00') nan 3.011389201610031] - [Timestamp('2003-08-26 16:00:00') nan 4.118169378357255] - [Timestamp('2003-08-26 17:00:00') nan 7.45263558332026] - [Timestamp('2003-08-26 18:00:00') nan 1.3430253913047183] - [Timestamp('2003-08-26 19:00:00') nan 4.123714757328189] - [Timestamp('2003-08-26 20:00:00') nan 4.677254442846517] - [Timestamp('2003-08-26 21:00:00') nan 8.569273514124749] - [Timestamp('2003-08-26 22:00:00') nan 10.233204029695282] - [Timestamp('2003-08-26 23:00:00') nan 3.5670924300616145] - [Timestamp('2003-08-27 00:00:00') nan 8.013366189706332] - [Timestamp('2003-08-27 01:00:00') nan 5.227238859096058] - [Timestamp('2003-08-27 02:00:00') nan 5.792972348719708] - [Timestamp('2003-08-27 03:00:00') nan 9.678141071006621] - [Timestamp('2003-08-27 04:00:00') nan 3.571813493245302] - [Timestamp('2003-08-27 05:00:00') nan 9.119561038749325] - [Timestamp('2003-08-27 06:00:00') nan 2.4542868845571557] - [Timestamp('2003-08-27 07:00:00') nan 4.675716273715381]] - - - -{ - "Signal": { - "Dataset": { - "Signal": "Signal", - "Time": { - "Horizon": 36, - "TimeMinMax": [ - "2000-01-01 00:00:00", - "2003-08-25 19:00:00" - ], - "TimeVariable": "Date" - }, - "Training_Signal_Length": 31988 - }, - "Model": { - "AR_Model": "NoAR", - "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", - "Cycle": "Cycle", - "Signal_Transoformation": "NoTransf", - "Trend": "ConstantTrend" - }, - "Model_Performance": { - "COMPLEXITY": "8", - "MAE": "0.07832565396296981", - "MAPE": "0.0181", - "MASE": "0.0227", - "RMSE": "0.09829060464825726" - } - } -} - - - - - - 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'_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0179 MAPE_Forecast=0.018 MAPE_Test=0.0222 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0178 SMAPE_Forecast=0.0179 SMAPE_Test=0.0221 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0299 MASE_Forecast=0.03 MASE_Test=0.0353 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.08032611138875244 L1_Forecast=0.08075293102223974 L1_Test=0.09032240650280009 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.10078902909585007 L2_Forecast=0.10091825923290786 L2_Test=0.1149716067777537 -INFO:pyaf.std:MODEL_COMPLEXITY 8 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.0981444673224665 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 22 -0.8289203680368509 {0: -3.8584048930599213, 1: -2.044771209716883, 2: -1.1313973381021545, 3: 3.4104909287428056, 4: 4.3291513599874065, 5: 2.958284496745141, 6: -3.4118851906278436, 7: -2.498326554648907, 8: 0.2208718697573886, 9: -4.774180091357353, 10: -2.499232322795973, 11: -2.051079991143092, 12: 1.1293139040958398, 13: 2.500372795885049, 14: -2.958150138832557, 15: 0.6773172828592138, 16: -1.583588645659888, 17: -1.12902668929003, 18: 3.864863385068819, 19: 3.4013741031993536, 20: 2.0444213681217, 21: 3.411227384830518} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_FORECASTING '['Signal']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 12.542189121246338 -Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', - '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', - 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', - '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', - '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', - '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', - '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', - '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', - '_Signal_TransformedForecast', 'Signal_Forecast', - '_Signal_TransformedResidue', 'Signal_Residue', - 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], - dtype='object') - -RangeIndex: 32032 entries, 0 to 32031 -Data columns (total 3 columns): - # Column Non-Null Count Dtype ---- ------ -------------- ----- - 0 Date 32032 non-null datetime64[ns] - 1 Signal 31988 non-null float64 - 2 Signal_Forecast 32032 non-null float64 -dtypes: datetime64[ns](1), float64(2) -memory usage: 750.9 KB -None -Forecasts - [[Timestamp('2003-08-25 20:00:00') nan 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- - - -TEST_CYCLES_END 22 diff --git a/tests/references/perf_test_ozone_ar_speed.log b/tests/references/perf_test_ozone_ar_speed.log index 8c118967e..29b10a01a 100644 --- a/tests/references/perf_test_ozone_ar_speed.log +++ b/tests/references/perf_test_ozone_ar_speed.log @@ -29,1230 +29,1230 @@ min 1.200000 75% 4.825000 max 8.700000 Name: Ozone, dtype: float64 -INFO:pyaf.std:TREND_TIME_IN_SECONDS _Ozone 0.3789534568786621 -INFO:pyaf.std:TREND_TIME_IN_SECONDS RelDiff_Ozone 0.2544698715209961 -INFO:pyaf.std:TREND_TIME_IN_SECONDS CumSum_Ozone 0.20190143585205078 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:TREND_TIME_IN_SECONDS CumSum_Ozone 0.12608790397644043 +INFO:pyaf.std:TREND_TIME_IN_SECONDS _Ozone 0.11654543876647949 +INFO:pyaf.std:TREND_TIME_IN_SECONDS Diff_Ozone 0.17267394065856934 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle' 0.01 -INFO:pyaf.std:TREND_TIME_IN_SECONDS 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'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_zeroCycle' 0.01 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS _Ozone 1.2917230129241943 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS CumSum_Ozone 1.32731294631958 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear' 0.03 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth' 0.04 -INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth' 0.09 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE' 0.09 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear' 0.02 INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth' 0.04 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS _Ozone 2.0282821655273438 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS Diff_Ozone 1.8767259120941162 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:CYCLE_TIME_IN_SECONDS RelDiff_Ozone 2.2203547954559326 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear' 0.02 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth' 0.02 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek' 0.02 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear' 0.02 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS Diff_Ozone 1.4874343872070312 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth' 0.03 +INFO:pyaf.std:CYCLE_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth' 0.03 +INFO:pyaf.std:CYCLE_TIME_IN_SECONDS RelDiff_Ozone 1.3751604557037354 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 6 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.3043599128723145 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.7619097232818604 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_ConstantTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.6637704372406006 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.9078023433685303 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.1024794578552246 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596040312.1677449 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596093183.696758 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596040312.1722329 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596093183.7002366 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596093183.8526864 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596093183.8557842 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.757079839706421 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596040312.3808508 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596093184.1136131 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596040312.3846316 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.7789273262023926 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596093184.1180828 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.1371734142303467 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596040312.473332 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596040312.4769304 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596040312.7098076 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 0 1596093184.363612 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596040312.7136698 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 20.13 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue' 10200 1000 1596093184.367604 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 13.45 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 20.29 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 21.75 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 13.45 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 14.55 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.2885894775390625 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.4042589664459229 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093199.1190848 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093199.1211 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.969207525253296 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 22.68 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040335.1587017 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093199.4855368 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040335.1617963 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.7458584308624268 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040335.9964035 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040336.000491 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.9465482234954834 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093199.489718 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.8144125938415527 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.1802005767822266 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040337.2916653 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093200.2423677 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040337.295574 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040337.5292912 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040337.533136 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 19.1 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093200.2457604 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 15.99 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.871436595916748 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093202.4031968 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093202.406216 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 12.59 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 19.82 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 12.44 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 21.12 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 22.65 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.323432445526123 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.8076183795928955 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040359.2443485 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093213.7055194 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040359.2474694 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.722188949584961 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093213.7094495 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.679206132888794 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040360.101729 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093214.550043 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040360.105901 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.229313850402832 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040360.722807 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040360.7325375 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.433290481567383 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093214.5538259 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 16.78 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.767503023147583 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040361.6466317 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040361.6542735 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 19.9 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093218.2234612 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093218.2266974 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 17.43 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.1385114192962646 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093222.1885426 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093222.1917744 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.39 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 19.53 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 16.93 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.4020838737487793 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.4997711181640625 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.2439756393432617 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596040381.776747 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596093233.90193 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596040381.7803204 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.294100522994995 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596093233.9058335 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596040382.2379346 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596093234.0140955 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596040382.2420735 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 23.08 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 22.83 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596093234.0193393 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 18.23 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.7192046642303467 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040386.8420188 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040386.8454149 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.59816575050354 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.3859193325042725 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040387.3688486 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093239.1233718 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040387.373014 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 16.92 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.833404779434204 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596040400.8604894 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040400.863919 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.23 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093239.1270077 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 19.14 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.279844284057617 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093243.8858628 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093243.8918643 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 20.44 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.8914897441864014 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 22.09 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.4292056560516357 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596040403.564928 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596093257.1515973 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040403.567928 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 17.66 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 18.99 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093257.1544633 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.3698890209198 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596093258.750183 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093258.766268 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 20.41 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.4259979724884033 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040408.3354173 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.02 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040408.3510942 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.3143746852874756 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.3776869773864746 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040410.1957753 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093262.1269143 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040410.199317 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 25.04 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 23.24 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093262.1299672 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 20.88 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9704570770263672 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093266.948495 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093266.952235 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 19.26 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.466141700744629 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 18.37 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.629544258117676 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093279.4016285 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093279.4099572 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 17.32 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.327397584915161 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040428.625351 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093279.7720568 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040428.629379 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.5903165340423584 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040429.6267967 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040429.6319177 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 24.23 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093279.7762632 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.8773393630981445 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093281.5132573 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093281.5164394 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 16.95 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 25.04 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.9661319255828857 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.946760892868042 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040435.8682761 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093286.0728617 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040435.8728013 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.8251826763153076 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040438.342883 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040438.3470232 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 20.83 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093286.076381 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 17.36 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 21.92 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 17.48 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.659019947052002 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.9537675380706787 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040453.3663104 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093298.9463568 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040453.3697581 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.7159457206726074 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093298.9492748 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.8942005634307861 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.77 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 6 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040454.7880664 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093299.4175043 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040454.7927897 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 20.29 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 20.63 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.825521469116211 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040459.3897326 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040459.3963625 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.9296810626983643 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093299.4215763 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.8995106220245361 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040462.179418 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093301.3855107 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040462.183291 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 19.23 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 19.31 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093301.3887568 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.66 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9743742942810059 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093305.9248803 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093305.928449 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 15.7 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.840743064880371 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040474.6659305 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040474.669122 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.68318510055542 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 17.2 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.862452507019043 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040476.0088527 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093317.201241 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040476.0111856 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 19.0 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 17.02 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093317.2044055 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.085689067840576 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 16.88 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.8594675064086914 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093318.4301004 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093318.4334836 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.8316454887390137 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093320.285219 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093320.2884405 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 16.75 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9772441387176514 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040481.5502768 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093324.8836493 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040481.5540502 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.7343246936798096 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040482.3478794 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040482.3539517 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 18.58 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 17.29 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093324.8860557 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 16.92 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.396247386932373 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.6456880569458008 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 17.59 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040496.0200706 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040496.0272393 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.843369960784912 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093336.0303707 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093336.0338922 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 17.13 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.027069568634033 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040496.4096255 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093338.2774665 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040496.4137526 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 22.82 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 23.27 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.693547248840332 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040507.4080493 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040507.4136703 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.7857258319854736 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093338.280539 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.8614270687103271 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040508.746335 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093339.4702804 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040508.7505982 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 16.38 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.39 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093339.4739783 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 15.1 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.7686653137207031 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093341.9485233 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093341.952334 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 16.83 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.021821975708008 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040515.0496092 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040515.0529974 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.634748935699463 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.8841726779937744 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040516.2949765 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093354.9407203 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040516.2982614 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 24.01 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 17.49 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 24.34 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093354.943576 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.4 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 17.32 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.769804000854492 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.3797848224639893 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093358.277591 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093358.2809277 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.0497426986694336 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596093359.0743492 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596093359.0835621 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_ConstantTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 17.79 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9807090759277344 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596040534.4588623 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596093361.928355 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596040534.4621058 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.4100515842437744 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040535.253199 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040535.2579687 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 19.33 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596093361.931648 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 18.15 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.2454147338867188 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 0 1596040536.6186178 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue' 10200 1000 1596040536.6221242 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.9149155616760254 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.937718391418457 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040538.884528 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040538.8930116 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 20.04 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.8 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093375.2253098 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093375.228406 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 18.39 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 18.12 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.2281434535980225 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596040557.0623937 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040557.0667021 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 22.73 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.174940586090088 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.997243881225586 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093378.882651 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093378.885708 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.254945993423462 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596040558.8020675 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596093379.7178626 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040558.8044875 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.074044942855835 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040560.2918088 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040560.295324 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 22.26 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093379.7214386 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_zeroCycle_residue_AR(1000)' 10200 1000 18.28 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.197556734085083 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 0 1596093382.651274 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093382.6543748 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 19.01 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.9153664112091064 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.4579968452453613 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040563.2938092 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093396.9668853 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040563.2974803 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 17.04 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 17.57 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093396.9711175 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 19.52 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 19.8 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.687365770339966 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.250927448272705 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093400.9291267 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093400.9323676 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.1874945163726807 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093401.9352345 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093401.9393678 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 21.48 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.565502643585205 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040577.1440742 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093406.94318 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040577.1490767 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 18.21 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.0162320137023926 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040579.703025 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040579.7090352 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.442737340927124 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596040581.2041771 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596040581.2087693 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 19.16 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093406.9471684 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 20.73 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.6437015533447266 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.3001205921173096 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596040585.4719954 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596093420.266932 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 18.33 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596040585.476191 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 19.84 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 24.39 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 22.3 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596093420.2714267 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.8178176879882812 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040603.1066463 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040603.1101434 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.2343974113464355 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 20.21 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.1278886795043945 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093422.6400251 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 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'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.0810630321502686 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 18.51 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 6 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040604.1679635 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093427.1847317 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040604.1710906 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.7822823524475098 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040605.105642 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040605.1118228 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.234571695327759 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093427.1881716 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 21.53 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 20.21 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.6327645778656006 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040606.5224185 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093444.737992 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040606.5262253 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 17.52 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.5518813133239746 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040623.5782108 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040623.5829804 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 19.4 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 22.42 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 22.39 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.906980037689209 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040629.185006 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040629.1904273 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.9538726806640625 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040629.946246 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040629.9522588 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.4126715660095215 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093444.7421064 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.2875237464904785 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040631.2906835 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093445.3919613 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040631.2945287 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 20.01 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.2854723930358887 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040646.126191 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040646.1294482 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 21.15 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.1029579639434814 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040652.7352803 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040652.7392743 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 24.93 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 23.66 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093445.3949757 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 18.56 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.240050792694092 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093448.2368138 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093448.2398694 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 25.33 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.815464496612549 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093452.248926 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093452.252535 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 16.33 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.664591073989868 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.8862521648406982 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.9709014892578125 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040658.952329 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040658.9586012 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040659.2520225 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040659.2590764 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 16.83 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.1342933177948 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040665.315172 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040665.3181016 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 16.74 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.335491895675659 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040672.0283375 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040672.031736 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 16.3 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 23.26 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 23.57 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093463.910702 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093463.9143965 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 18.62 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 22.41 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.1114203929901123 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.439054012298584 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093469.4971232 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093469.5005257 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093469.6562157 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093469.6600556 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 19.9 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.755620002746582 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093475.3252566 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093475.3308308 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 19.16 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.393597364425659 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040684.3248532 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040684.3298242 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.3405039310455322 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040684.8549104 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040684.8581243 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.3591384887695312 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.308236837387085 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040685.4644704 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040685.4693038 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 18.57 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.5250611305236816 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040694.4969664 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040694.5015469 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 21.87 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 22.32 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 22.19 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093486.6764362 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093486.6824894 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 19.9 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 20.97 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.5953927040100098 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) 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+INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093493.0102417 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 21.56 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 3.549914836883545 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093500.8283432 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093500.8326075 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 18.82 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.4623873233795166 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040708.9004483 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040708.9043329 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.181396484375 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040710.7208352 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040710.7245712 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.276689052581787 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.5449910163879395 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'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.497117042541504 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040717.6703134 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040717.6735687 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 20.79 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.6437175273895264 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040732.7700431 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040732.773061 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 25.91 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 27.48 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093507.2185307 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 15.41 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9007995128631592 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093510.5681982 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093510.5703642 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 20.86 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.1718666553497314 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093515.6238036 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093515.6266923 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 16.38 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.2889609336853027 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093519.706249 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093519.7090101 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 13.46 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 21.6 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 4.232936382293701 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040741.1483927 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040741.1542845 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.9731791019439697 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040742.6130855 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040742.6161075 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 4.474721908569336 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.7813982963562012 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040743.8055189 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040743.8110673 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 25.45 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.5599403381347656 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040761.2247143 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040761.229332 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 25.75 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.1028544902801514 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040771.7202206 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040771.723706 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 33.61 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.429553270339966 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596040778.7519555 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596040778.7564569 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 36.91 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093522.6301472 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093522.6326196 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 13.93 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.8372948169708252 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093526.5019157 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093526.5040374 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 16.31 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.8395438194274902 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093533.9681082 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093533.9707057 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_Lag1Trend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 11.48 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 20.94 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.677426815032959 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 15.33 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.3113524913787842 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596040784.7427392 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596093535.6407857 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596040784.7473414 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.795532703399658 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596040785.3709395 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596040785.3770235 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 22.06 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.971742868423462 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596040797.2678256 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596040797.2710187 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 22.8 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 30.04 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596093535.642914 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.4854414463043213 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 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'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 0 1596093538.7232733 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue' 10200 1000 1596093538.7256517 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 11.97 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 10.47 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.3120689392089844 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 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+INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093547.4715996 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 11.75 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 10.87 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.861513376235962 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040811.3542807 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.02 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040811.3706386 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.4602811336517334 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.2286980152130127 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093549.8650563 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093549.867243 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.3716633319854736 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040812.5749176 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093551.1428626 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040812.5787988 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 30.88 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 20.79 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.7001307010650635 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040819.7770214 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.02 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040819.7941647 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.0818076133728027 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596040820.364117 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596040820.3673913 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 17.52 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.744513988494873 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040832.1721344 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.02 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040832.18766 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 22.48 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 30.61 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.332418441772461 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040846.5162818 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040846.5214238 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.3393876552581787 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040846.8178823 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040846.8221319 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 28.95 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093551.1453018 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 9.2 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 9.68 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.125420570373535 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.132383108139038 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093557.8618827 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093557.8642452 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.2282800674438477 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596040852.3163745 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093558.513182 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596040852.3195462 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 22.59 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.803062915802002 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040857.831385 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040857.8348484 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 20.54 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.753509998321533 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040870.2036855 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040870.2072635 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 29.41 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 20.78 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 26.66 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'_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093559.9912844 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 9.42 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.338756799697876 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093562.05283 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093562.0548296 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 8.77 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.061394453048706 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093567.8131537 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 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-INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040880.2731636 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.2817046642303467 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040881.4283154 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040881.44282 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.973721742630005 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 8.6 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.260051965713501 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596040882.2493546 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093569.5652623 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596040882.2537634 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 16.68 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.031751871109009 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040889.1688175 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040889.1726365 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 17.6 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.9179909229278564 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040902.2394364 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040902.2436442 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 22.36 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 20.46 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093569.5672126 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.1967847347259521 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093569.9251745 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093569.9273794 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 9.46 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.2603893280029297 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093572.9411244 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'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596093577.9631116 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 9.1 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 9.65 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.995532751083374 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.262575626373291 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596093580.427061 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596093580.429058 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.2874834537506104 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040904.948477 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093580.6471133 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'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596040905.4216266 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093584.1141925 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596040905.4257631 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 19.31 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 3.18043851852417 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040911.9663286 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040911.97046 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 20.05 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 18.88 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093584.1166024 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 9.57 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.1830670833587646 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093588.8503845 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093588.8522463 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 9.47 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 10.5 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.0435101985931396 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 19.04 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040924.5762336 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040924.579343 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.1279730796813965 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.3788414001464844 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093591.4576924 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093591.4596856 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.2959678173065186 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040926.2350354 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093592.6108077 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040926.2391477 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.287842035293579 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093592.6131914 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 9.93 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.6413865089416504 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596040928.1993628 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596040928.2107105 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 16.29 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.3814754486083984 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040930.893687 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040930.8972242 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 19.06 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.5813283920288086 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040946.4942644 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040946.500665 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 23.05 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093595.98452 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093595.987475 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 11.42 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.4132742881774902 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093601.8116333 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093601.8139403 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 11.49 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.1987221240997314 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 11.62 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 20.14 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 23.86 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.837641954421997 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093604.3436968 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093604.34579 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.2336995601654053 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040953.569611 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.02 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040953.5857925 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.6018590927124023 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040953.934697 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040953.9380696 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.7609992027282715 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093605.5979373 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093605.6008701 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 11.21 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.4357316493988037 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596040956.258666 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093608.8290803 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596040956.263057 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 20.45 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.238525152206421 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040969.4459195 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040969.4490232 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 19.29 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.6358776092529297 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596040976.2183275 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596040976.2248363 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 25.57 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093608.831172 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 9.87 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.3775687217712402 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093613.2173698 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093613.2197049 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 10.19 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 10.1 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 23.26 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.430424928665161 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.1766352653503418 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093615.8523707 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093615.8542495 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.173508882522583 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040981.9451048 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093616.9993763 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040981.9495811 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.704497814178467 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093617.0012662 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 9.57 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.2771000862121582 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596040982.564058 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596040982.569457 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 19.71 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS _Ozone 679.7576198577881 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 18.29 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS RelDiff_Ozone 684.8745217323303 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 18.61 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093619.8139365 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093619.816261 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 10.66 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 9.48 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.8665492534637451 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093626.0073853 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093626.0108337 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.7458956241607666 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093627.2692516 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093627.2726388 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 10.31 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 18.53 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.5434675216674805 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.6743295192718506 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596041003.3825166 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093629.1694233 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596041003.3866487 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.7112767696380615 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093629.1728318 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 10.21 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.2380306720733643 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596041004.1011724 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093631.416141 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596041004.1050956 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 24.16 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 25.52 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093631.4184854 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 14.28 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 11.85 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.5684025287628174 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596041032.2124972 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.02 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596041032.2287815 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.8322489261627197 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 14.18 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.9186351299285889 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.304016351699829 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093642.429635 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093642.4330614 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596041033.2204745 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596093642.4667277 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596041033.225415 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 26.58 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 26.18 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596093642.4689662 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.3484816551208496 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093643.0042722 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093643.007359 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_LinearTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 15.66 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9312243461608887 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 0 1596093649.2179382 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue' 10200 1000 1596093649.2210605 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 10.76 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 3.4775025844573975 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596041062.669101 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596041062.6727583 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.6013638973236084 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.3695690631866455 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596041063.6814108 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093654.7419362 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596041063.6853917 -INFO:pyaf.std:PERF_TIME_IN_SECONDS _Ozone 60 91.37157034873962 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS _Ozone 774.755464553833 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 22.08 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 22.38 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093654.744057 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 13.6 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.0873911380767822 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093658.9808617 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093658.9850674 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 17.19 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.605557918548584 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093662.5176995 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093662.5226064 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_zeroCycle_residue_AR(1000)' 10200 1000 16.83 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.920685052871704 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 0 1596093668.2022567 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue' 10200 1000 1596093668.2082696 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 13.75 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.5391790866851807 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:PERF_TIME_IN_SECONDS RelDiff_Ozone 60 92.82952737808228 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS RelDiff_Ozone 781.5373413562775 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596041087.6107178 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596041087.6144104 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 3.013439655303955 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.084214448928833 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596041089.515869 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596041089.5195007 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 20.75 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 20.25 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093670.8071704 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093670.812757 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 20.9 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 2.0464839935302734 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093682.1755645 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.01 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093682.1813245 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 20.59 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 2.1487550735473633 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093685.5144904 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093685.5184755 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_bestCycle_byMAPE_residue_AR(1000)' 10200 1000 17.65 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 15.43 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9652419090270996 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596041110.5601692 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596041110.5633376 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.0146048069000244 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.4283246994018555 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9333522319793701 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596041111.984931 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093687.8638005 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596041111.9892042 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 15.97 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 14.72 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093687.8667006 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 0 1596093688.0369596 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue' 10200 1000 1596093688.0411928 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 11.33 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS _Ozone 1006 1.5677053928375244 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093695.2887866 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093695.291219 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 14.83 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS RelDiff_Ozone 1006 1.537480115890503 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093702.154829 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093702.1575513 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 17.27 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9277024269104004 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.9700899124145508 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_MonthOfYear_residue_AR(1000)' 10200 1000 18.32 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.4999279975891113 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596041128.697947 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596041128.7015784 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS '_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 12.58 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS _Ozone 526.2695708274841 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596041128.9259274 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093707.9226506 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596041128.9296181 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 19.12 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093707.9275126 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.365185022354126 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 0 1596093708.979114 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue' 10200 1000 1596093708.982316 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'RelDiff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 12.97 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS RelDiff_Ozone 533.1219906806946 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfYear_residue_AR(1000)' 10200 1000 18.24 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 6 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 20.37 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 19.73 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.8885695934295654 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.9865741729736328 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 0 1596093729.4492748 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue' 10200 1000 1596093729.4524176 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.225054979324341 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.618694305419922 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596041151.1807115 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093730.747433 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596041151.1849542 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596041151.562119 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596041151.5665786 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 24.6 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093730.7516217 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 15.7 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 24.53 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.882734537124634 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfMonth_residue_AR(1000)' 10200 1000 18.25 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.6921391487121582 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.776552677154541 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596041178.9733977 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.01 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596041178.9788914 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596041179.1912034 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596041179.1950362 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 21.19 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 21.82 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093748.3247693 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093748.3272338 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.818249225616455 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 0 1596093749.7175677 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue' 10200 1000 1596093749.7206724 +INFO:pyaf.std:PERF_TIME_IN_SECONDS _Ozone 60 53.5978684425354 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS _Ozone 581.9942724704742 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 15.26 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 2.696073055267334 -INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596041203.3479264 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596041203.3519275 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.57761549949646 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.7965123653411865 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596041203.7496612 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093765.5795207 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596041203.7541585 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 18.08 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093765.58316 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfWeek_residue_AR(1000)' 10200 1000 16.45 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 6 +INFO:pyaf.std:PERF_TIME_IN_SECONDS RelDiff_Ozone 60 52.69561314582825 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS RelDiff_Ozone 588.3416340351105 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.834097146987915 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 0 1596093768.1996357 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue' 10200 1000 1596093768.2035153 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 16.51 INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 -INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 2.7465450763702393 +INFO:pyaf.std:LAG_TIME_IN_SECONDS CumSum_Ozone 1006 1.7137813568115234 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 21.26 -INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596041224.861508 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093783.9834745 INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596041224.8659277 -INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 4.174878120422363 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093783.9870589 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfYear_residue_AR(1000)' 10200 1000 16.23 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.7482407093048096 +INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) +INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1006 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 0 1596093786.3716674 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue' 10200 1000 1596093786.3748426 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 16.37 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS CumSum_Ozone 618.7510542869568 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_WeekOfMonth_residue_AR(1000)' 10200 1000 16.17 +INFO:pyaf.std:AR_MODEL_ADD_LAGS_START 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 6 +INFO:pyaf.std:LAG_TIME_IN_SECONDS Diff_Ozone 1006 1.7866628170013428 INFO:pyaf.std:AR_MODEL_LAG_SAMPLING_ACTIVATED 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' (10200, 8192, 1960, 0.8031372549019608) INFO:pyaf.std:AR_MODEL_ADD_LAGS_END 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1006 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596041229.138291 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.02 -INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596041229.1543927 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'CumSum_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 21.97 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS CumSum_Ozone 937.655433177948 -INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 20.22 -INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS Diff_Ozone 939.9128792285919 -INFO:pyaf.std:PERF_TIME_IN_SECONDS Diff_Ozone 72 89.05299735069275 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS Diff_Ozone 1032.6335008144379 -INFO:pyaf.std:PERF_TIME_IN_SECONDS CumSum_Ozone 72 94.63697648048401 -INFO:pyaf.std:TRAINING_TIME_IN_SECONDS CumSum_Ozone 1035.6705675125122 -INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone 0.01689457893371582 -INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone 0.012613296508789062 -INFO:pyaf.std:PREDICTION_INTERVAL_TIME_IN_SECONDS Ozone 22.779833555221558 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 1062.8499641418457 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 0 1596093804.5239651 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_NoAR' 10200 0 0.0 +INFO:pyaf.std:AR_MODEL_START_TRAINING_TIME 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue' 10200 1000 1596093804.5274155 +INFO:pyaf.std:AR_MODEL_TRAINING_TIME_IN_SECONDS 'Diff_Ozone_PolyTrend_residue_Seasonal_DayOfNthWeekOfMonth_residue_AR(1000)' 10200 1000 16.25 +INFO:pyaf.std:AUTOREG_TIME_IN_SECONDS Diff_Ozone 638.9783072471619 +INFO:pyaf.std:PERF_TIME_IN_SECONDS CumSum_Ozone 72 72.1123104095459 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS CumSum_Ozone 692.9907295703888 +INFO:pyaf.std:PERF_TIME_IN_SECONDS Diff_Ozone 72 76.5074200630188 +INFO:pyaf.std:TRAINING_TIME_IN_SECONDS Diff_Ozone 717.808075428009 +INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone 0.023618698120117188 +INFO:pyaf.std:MODEL_SELECTION_TIME_IN_SECONDS Ozone 0.016466617584228516 +INFO:pyaf.std:PREDICTION_INTERVAL_TIME_IN_SECONDS Ozone 16.649637699127197 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 738.1489660739899 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 diff --git a/tests/references/perf_test_ozone_ar_speed_many.log b/tests/references/perf_test_ozone_ar_speed_many.log index 40a561fee..74eddf865 100644 --- a/tests/references/perf_test_ozone_ar_speed_many.log +++ b/tests/references/perf_test_ozone_ar_speed_many.log @@ -1,760 +1,6 @@ -INFO:pyaf.std:START_TRAINING 'Ozone' Month Ozone Time 0 1955-01 2.7 1955-01-01 1 1955-02 2.0 1955-02-01 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -(10200,) (10200,) count 10200 -unique 10200 -top 1966-07-30 00:00:00 -freq 1 -first 1955-01-01 00:00:00 -last 2150-06-20 00:00:00 -dtype: object count 10200.000000 -mean 3.835784 -std 1.491632 -min 1.200000 -25% 2.600000 -50% 3.750000 -75% 4.825000 -max 8.700000 -Name: Ozone, dtype: float64 -count 10200.000000 -mean 3.835784 -std 1.491632 -min 1.200000 -25% 2.600000 -50% 3.750000 -75% 4.825000 -max 8.700000 -Name: Ozone, dtype: float64 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 14.992173671722412 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=39125.00000000028 Mean=19605.363725490337 StdDev=11294.348459632758 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' -INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [Lag1Trend + Cycle + NoAR] -INFO:pyaf.std:TREND_DETAIL 'CumSum_Ozone_Lag1Trend' [Lag1Trend] -INFO:pyaf.std:CYCLE_DETAIL 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE' [Cycle] -INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2167 MAPE_Forecast=0.2166 MAPE_Test=0.1912 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2147 SMAPE_Forecast=0.2146 SMAPE_Test=0.2239 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9142 MASE_Forecast=0.9136 MASE_Test=0.899 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7711411042944856 L1_Forecast=0.7710009813544707 L1_Test=0.42500000000048516 -INFO:pyaf.std:MODEL_L2 L2_Fit=1.017215011867223 L2_Forecast=1.0169394519200639 L2_Test=0.5074445782552324 -INFO:pyaf.std:MODEL_COMPLEXITY 72 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LAG1_TREND Lag1Trend 2.7 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE 12 3.7999999999992724 {0: 2.099999999998545, 1: 2.400000000000091, 2: 2.7000000000007276, 3: 3.7999999999992724, 4: 3.7999999999992724, 5: 4.299999999999272, 6: 4.900000000001455, 7: 5.0, 8: 4.799999999999272, 9: 4.799999999999272, 10: 2.7999999999992724, 11: 2.2000000000007276} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 30.85416007041931 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(50)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(50)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1652 MAPE_Forecast=0.1653 MAPE_Test=0.1903 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1595 SMAPE_Forecast=0.1596 SMAPE_Test=0.1768 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6901 MASE_Forecast=0.6898 MASE_Test=0.9253 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.5820898625007142 L1_Forecast=0.5821675672022341 L1_Test=0.4373917755728303 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.7784586364606513 L2_Forecast=0.7781974157435858 L2_Test=0.5140916849060585 -INFO:pyaf.std:MODEL_COMPLEXITY 50 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.46716516523134277 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.16591202108286163 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.15982183881941225 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.1417171675471157 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1409110401759769 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.14000940091747033 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag48 0.12963386728695633 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1211252731468459 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.12091149023601114 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag37 0.1193596496924073 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 49.27895402908325 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(100)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(100)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1376 MAPE_Forecast=0.1374 MAPE_Test=0.2341 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1347 SMAPE_Forecast=0.1345 SMAPE_Test=0.2311 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5747 MASE_Forecast=0.5732 MASE_Test=1.2078 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.4847747374430702 L1_Forecast=0.4837075610510263 L1_Test=0.5709826667757363 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.6465396535544338 L2_Forecast=0.6438252890953697 L2_Test=0.7128249778075041 -INFO:pyaf.std:MODEL_COMPLEXITY 100 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.47308502041071965 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.3186179266469543 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.23866357449829342 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.22757561427267176 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag87 0.20658693809942721 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.19716488770967844 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag97 0.17935881914959179 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.1787415712301379 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag4 0.16990491275172798 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.16719937185282224 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 70.77789783477783 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(150)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(150)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1151 MAPE_Forecast=0.114 MAPE_Test=0.2296 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1131 SMAPE_Forecast=0.1122 SMAPE_Test=0.2202 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4806 MASE_Forecast=0.4759 MASE_Test=1.1891 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.40537524131974373 L1_Forecast=0.4015882302738338 L1_Test=0.5621372840947728 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.5311394579412342 L2_Forecast=0.5237706567242836 L2_Test=0.6545460221337277 -INFO:pyaf.std:MODEL_COMPLEXITY 150 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5947151721662776 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.3859792149061864 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag97 0.3470922572809153 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag44 0.32389308559970453 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag88 -0.3054561110151719 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.30297756491043626 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag140 0.27935923649387895 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag128 -0.2781261838662811 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.27636844903312185 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag110 -0.2738335556231832 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 98.1719024181366 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(200)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(200)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0672 MAPE_Forecast=0.0632 MAPE_Test=0.0905 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0669 SMAPE_Forecast=0.0627 SMAPE_Test=0.0865 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2671 MASE_Forecast=0.2501 MASE_Test=0.4408 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.22529254188884426 L1_Forecast=0.21107599858154158 L1_Test=0.20836525169937734 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.29870968153860633 L2_Forecast=0.2541821407606399 L2_Test=0.258529536376885 -INFO:pyaf.std:MODEL_COMPLEXITY 200 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.4903068319692706 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag164 -0.48075663894218773 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag160 0.3970138078342637 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.39173250861564346 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag120 0.38862497639713045 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag56 -0.38251351087420776 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag108 -0.3661613162947641 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag44 0.3592112690532089 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.3548423678376884 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag127 0.3448081547009084 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 149.22827768325806 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(250)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(250)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0088 MAPE_Forecast=0.0049 MAPE_Test=0.0058 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0087 SMAPE_Forecast=0.0049 SMAPE_Test=0.0058 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0359 MASE_Forecast=0.0188 MASE_Test=0.0273 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.030307356380726767 L1_Forecast=0.01587382602455877 L1_Test=0.01291514469252599 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.12108092799712486 L2_Forecast=0.019760565120536696 L2_Test=0.015573438356317332 -INFO:pyaf.std:MODEL_COMPLEXITY 250 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9345105216381727 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3908829226849115 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3900384236001172 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.17928655561750645 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag219 0.16542904823177937 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag240 -0.1649857214333443 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.1621368674335584 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.14802749123571246 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.14548217659742887 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.13715339856301567 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 206.2936851978302 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(300)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(300)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0074 MAPE_Forecast=0.0033 MAPE_Test=0.0035 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0073 SMAPE_Forecast=0.0033 SMAPE_Test=0.0035 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0303 MASE_Forecast=0.013 MASE_Test=0.0179 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.025546582629409287 L1_Forecast=0.010930580520685669 L1_Test=0.008474671244716939 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.11596559712577058 L2_Forecast=0.013414584061184339 L2_Test=0.009770670685919412 -INFO:pyaf.std:MODEL_COMPLEXITY 300 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9520873812395108 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3821398619228936 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3702598513081168 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.2032383669328519 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.18746881774482826 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.1742557772849714 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.1741826520960998 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.17232761249645182 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.16277834894133203 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag85 -0.16161573238694138 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 209.41051125526428 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(350)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(350)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0063 MAPE_Forecast=0.0024 MAPE_Test=0.0032 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0062 SMAPE_Forecast=0.0024 SMAPE_Test=0.0032 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0264 MASE_Forecast=0.0092 MASE_Test=0.0145 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.022280038066611958 L1_Forecast=0.00778965200803154 L1_Test=0.006854847953943606 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.11456268523471376 L2_Forecast=0.009854131082537214 L2_Test=0.009323682454962772 -INFO:pyaf.std:MODEL_COMPLEXITY 350 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9618112957907636 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.34760036144942064 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3326234742087 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.20343363394757877 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.19583272105950797 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.19373059335819276 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.1923412465423171 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.18729254241253507 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.18445251122839856 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.1697970836068921 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 222.9368076324463 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(400)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(400)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0063 MAPE_Forecast=0.0023 MAPE_Test=0.0031 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0063 SMAPE_Forecast=0.0023 SMAPE_Test=0.0031 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0264 MASE_Forecast=0.0086 MASE_Test=0.0138 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.022234340591402002 L1_Forecast=0.007277633465034995 L1_Test=0.0065443203955065825 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.11648718069403617 L2_Forecast=0.008993381111994679 L2_Test=0.0076669341328632346 -INFO:pyaf.std:MODEL_COMPLEXITY 400 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.965855337898474 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35211259058292355 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3329309664958524 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag57 -0.21785955555605618 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.21281018203223517 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.20076957777878612 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.1991869421975928 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag261 0.19660518004308275 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.1919881594806609 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag250 0.18429111157929967 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 181.16642212867737 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(450)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(450)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0061 MAPE_Forecast=0.0017 MAPE_Test=0.0021 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.006 SMAPE_Forecast=0.0017 SMAPE_Test=0.0021 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0252 MASE_Forecast=0.0066 MASE_Test=0.0092 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.021288154826919578 L1_Forecast=0.0055625690332575025 L1_Test=0.004338372823267582 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.12002731921962427 L2_Forecast=0.006907274160356605 L2_Test=0.005147831054884069 -INFO:pyaf.std:MODEL_COMPLEXITY 450 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9164592102191809 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.39418365179958204 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3325727795745933 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.2295027110769384 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.22283086782395337 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag57 -0.21882857709552916 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.2106733893492524 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.20812093651070762 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.20432243605700132 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.20320566537770263 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 193.2739281654358 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(500)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(500)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0061 MAPE_Forecast=0.0016 MAPE_Test=0.0018 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0061 SMAPE_Forecast=0.0016 SMAPE_Test=0.0018 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0256 MASE_Forecast=0.0059 MASE_Test=0.0078 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02155763127987575 L1_Forecast=0.0049896766028562824 L1_Test=0.0036706256518068505 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.1253140666500127 L2_Forecast=0.006170182235641204 L2_Test=0.004159893727816083 -INFO:pyaf.std:MODEL_COMPLEXITY 500 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9266284416831129 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.4001125083231848 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3275235402294996 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.24663740706276047 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.24524129969230835 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.2375752591294788 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.23371277085314826 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.22381867648886494 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.2202012036774198 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2191643333085786 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 228.86425471305847 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(550)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(550)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0063 MAPE_Forecast=0.0012 MAPE_Test=0.0012 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0063 SMAPE_Forecast=0.0012 SMAPE_Test=0.0012 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0259 MASE_Forecast=0.0045 MASE_Test=0.0047 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02185442063208843 L1_Forecast=0.0037571723606069775 L1_Test=0.002232759234724454 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.13458000787970648 L2_Forecast=0.004680534596964971 L2_Test=0.0030051530877741803 -INFO:pyaf.std:MODEL_COMPLEXITY 550 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.906430585181047 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3638395743250211 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32520187070462697 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.28020799569860716 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.27969375016701026 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.27081992104349506 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.24829523925639527 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2462523124032715 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.23884710494446437 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.22978743762979542 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 197.63337421417236 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(600)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(600)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0065 MAPE_Forecast=0.001 MAPE_Test=0.0011 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0066 SMAPE_Forecast=0.001 SMAPE_Test=0.0011 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0266 MASE_Forecast=0.004 MASE_Test=0.0046 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.022406498328260797 L1_Forecast=0.0033564447177333633 L1_Test=0.0021625798999914867 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.14177262995492115 L2_Forecast=0.004158310493415702 L2_Test=0.0026212738349729306 -INFO:pyaf.std:MODEL_COMPLEXITY 600 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9039215279497292 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.34693581228757864 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33345938867353825 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.329124609969479 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3173010454543127 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.30136370934928164 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.2634973813455129 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.26089074230724096 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.246680989866069 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag243 0.24568153191725126 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 214.95140480995178 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(650)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(650)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0065 MAPE_Forecast=0.0009 MAPE_Test=0.0009 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0068 SMAPE_Forecast=0.0009 SMAPE_Test=0.0009 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0267 MASE_Forecast=0.0033 MASE_Test=0.0044 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.022520033684121747 L1_Forecast=0.002781334340950565 L1_Test=0.0020816699164746266 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.1486763411701132 L2_Forecast=0.0035120063615152602 L2_Test=0.0024409838064478203 -INFO:pyaf.std:MODEL_COMPLEXITY 650 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.8949071648193107 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3431792721889384 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3353631518294812 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33194962949178075 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32774411792837876 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3216255905751091 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2669068158569821 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.26676839751954806 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.24929073912225203 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.24012943262001818 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 213.43421149253845 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(700)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(700)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0067 MAPE_Forecast=0.0007 MAPE_Test=0.0009 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.007 SMAPE_Forecast=0.0007 SMAPE_Test=0.0009 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0276 MASE_Forecast=0.0027 MASE_Test=0.0044 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02324252649320325 L1_Forecast=0.0023138164537433612 L1_Test=0.0020893555199669733 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.15539131542145818 L2_Forecast=0.0029604294728701 L2_Test=0.00261784965810577 -INFO:pyaf.std:MODEL_COMPLEXITY 700 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.8983630100597664 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35423600563247226 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3300663864879737 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3299298916852147 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3274687934640446 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.324117555190398 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.27573377246044817 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2742074513567465 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.2601385193759526 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.25905901909691564 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 259.8100264072418 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(750)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(750)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0067 MAPE_Forecast=0.0006 MAPE_Test=0.001 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0069 SMAPE_Forecast=0.0006 SMAPE_Test=0.001 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0275 MASE_Forecast=0.0025 MASE_Test=0.0047 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.023234315422083776 L1_Forecast=0.0020976505575114653 L1_Test=0.0022256598624392963 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.15763413469038345 L2_Forecast=0.0026276824540217834 L2_Test=0.0026773460904021378 -INFO:pyaf.std:MODEL_COMPLEXITY 750 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.891909711250419 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35835022816052753 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.33400931173461257 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3338321522546194 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.328533183464755 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.31818219771353967 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2858485080492868 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.27720433713079873 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.26223580641096245 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.25883717031443004 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 258.57784509658813 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(800)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(800)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0068 MAPE_Forecast=0.0006 MAPE_Test=0.0007 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.007 SMAPE_Forecast=0.0006 SMAPE_Test=0.0007 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0279 MASE_Forecast=0.0023 MASE_Test=0.0034 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.023544768371943227 L1_Forecast=0.0019245688102899543 L1_Test=0.001591321882642401 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.16004510619144224 L2_Forecast=0.0023560258644979132 L2_Test=0.002015419184996649 -INFO:pyaf.std:MODEL_COMPLEXITY 800 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.898903863021344 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3635505490434918 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.33922547996808844 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33443328703194897 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33126610337978474 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3272210624689798 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2913510370172112 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2827164184785814 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.2684810417564479 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.26730268351685416 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 296.2282602787018 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(850)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(850)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0069 MAPE_Forecast=0.0005 MAPE_Test=0.0004 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0071 SMAPE_Forecast=0.0005 SMAPE_Test=0.0004 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0281 MASE_Forecast=0.0019 MASE_Test=0.0021 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.02373658939825961 L1_Forecast=0.0015908942172913791 L1_Test=0.000985997462375987 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.16207926547708698 L2_Forecast=0.001967937630960144 L2_Test=0.0011903548496953905 -INFO:pyaf.std:MODEL_COMPLEXITY 850 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9009148608228692 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.36774958196525365 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.34255247075100065 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33651435140563 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33195491963022317 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3278830743915233 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.29106698417854926 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.28360977835871004 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.27513719664019465 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.270673765977303 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 322.00103282928467 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(900)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(900)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.007 MAPE_Forecast=0.0005 MAPE_Test=0.0005 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0072 SMAPE_Forecast=0.0005 SMAPE_Test=0.0005 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0285 MASE_Forecast=0.0019 MASE_Test=0.0028 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.024078689227328517 L1_Forecast=0.0016180239625596987 L1_Test=0.0013333951655400227 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.16391619683045353 L2_Forecast=0.0019581052787011792 L2_Test=0.0016516630104200158 -INFO:pyaf.std:MODEL_COMPLEXITY 900 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9006326337891377 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3710268258384325 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.343079751980385 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.34133508841005783 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3297120887933968 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32810392702059626 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2936446502975182 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.28746578969817926 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.2743900772964196 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.2702172201928167 -INFO:pyaf.std:AR_MODEL_DETAIL_END -INFO:pyaf.std:START_TRAINING 'Ozone' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 393.73567485809326 -INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' -INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(950)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(950)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0071 MAPE_Forecast=0.0004 MAPE_Test=0.0005 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0073 SMAPE_Forecast=0.0004 SMAPE_Test=0.0005 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0288 MASE_Forecast=0.0016 MASE_Test=0.0025 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.024326956478251543 L1_Forecast=0.001378721980223524 L1_Test=0.0011583989384891986 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.16507978841917972 L2_Forecast=0.0016889156300594285 L2_Test=0.0014557527712000138 -INFO:pyaf.std:MODEL_COMPLEXITY 950 -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None -INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END -INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 -INFO:pyaf.std:TREND_DETAIL_END -INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} -INFO:pyaf.std:CYCLE_MODEL_DETAIL_END -INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9012607657295476 -INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3759664579758385 -INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3414413605023231 -INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3397829973782326 -INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3281736166189236 -INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3276318296453165 -INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2913309000902372 -INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2913026529727761 -INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.27875039027830695 -INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.2732458486694681 -INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_1.log b/tests/references/temporal_hierarchy_test_temporal_demo_1.log index b824e4187..9ba52458b 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_1.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_1.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.7030074596405029 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.36476969718933105 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -8,10 +8,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, 'Q': 4} INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_Q']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 73.10181832313538 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 36.60933494567871 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_Q']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 36.014233350753784 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 22.286489248275757 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -20,7 +20,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'TH_W_start', 'TH_Q_start', 'Signal_D', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_Q_start', 'TH_D_start', 'TH_W_start', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', @@ -76,7 +76,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 94, 'MAPE': 0.6209, 'RMSE': 363.2995151004631, 'MAE': 362.49301943996755, 'SMAPE': 0.9005, 'ErrorMean': -362.49301943996755, 'ErrorStdDev': 24.19397713330535, 'R2': -86.09809673662467, 'Pearson': 0.9914232675117872} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 376, 'MAPE': 1.0192, 'RMSE': 322.1917471430979, 'MAE': 291.8070999296352, 'SMAPE': 1.9737, 'ErrorMean': -291.2375015888688, 'ErrorStdDev': 137.79782144647865, 'R2': -4.713627261850848, 'Pearson': 0.09778886526916761} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 94, 'MAPE': 1.0, 'RMSE': 585.0828979545455, 'MAE': 583.7864502550929, 'SMAPE': 2.0, 'ErrorMean': -583.7864502550929, 'ErrorStdDev': 38.92785606024334, 'R2': -224.89875435276088, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 109.90229535102844 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 59.433797121047974 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-05T00:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_D' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_D' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -180,22 +180,22 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 42.27106428146362 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 23.131307125091553 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 42.38111615180969 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 23.25691246986389 Int64Index: 4015 entries, 0 to 4014 Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_D_start 4015 non-null datetime64[ns] - 1 TH_W_start 574 non-null datetime64[ns] - 2 TH_Q_start 44 non-null datetime64[ns] + 0 TH_Q_start 44 non-null datetime64[ns] + 1 TH_D_start 4015 non-null datetime64[ns] + 2 TH_W_start 574 non-null datetime64[ns] 3 Signal_D 3650 non-null float64 4 Signal_D_Forecast 4015 non-null float64 5 Signal_D_Forecast_Lower_Bound 365 non-null float64 @@ -225,11 +225,11 @@ Data columns (total 30 columns): 29 Signal_Q_OC_Forecast 574 non-null float64 dtypes: datetime64[ns](3), float64(27) memory usage: 1.1 MB - TH_D_start TH_W_start ... Signal_W_OC_Forecast Signal_Q_OC_Forecast -4010 2012-01-18 NaT ... NaN NaN -4011 2012-01-19 2012-01-19 ... 244.897663 244.897663 -4012 2012-01-20 NaT ... NaN NaN -4013 2012-01-21 NaT ... NaN NaN -4014 2012-01-22 NaT ... NaN NaN + TH_Q_start TH_D_start ... Signal_W_OC_Forecast Signal_Q_OC_Forecast +4010 NaT 2012-01-18 ... NaN NaN +4011 NaT 2012-01-19 ... 244.897663 244.897663 +4012 NaT 2012-01-20 ... NaN NaN +4013 NaT 2012-01-21 ... NaN NaN +4014 NaT 2012-01-22 ... NaN NaN [5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W.log index 33b6c4549..a3ea0e39c 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.1210517883300781 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.516310453414917 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, '2W': 1209600.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -8,10 +8,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, '2W': 26} INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_2W']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W']' 90.66763114929199 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W']' 37.47258496284485 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_2W']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W']' 53.65689539909363 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W']' 23.07081627845764 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -20,7 +20,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'TH_2W_start', 'TH_W_start', 'Signal_D', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_2W_start', 'TH_W_start', 'TH_D_start', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', @@ -67,7 +67,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 94, 'MAPE': 0.3323, 'RMSE': 258.08011821084057, 'MAE': 194.16435372240417, 'SMAPE': 0.4715, 'ErrorMean': -169.0089967407891, 'ErrorStdDev': 195.04180689378722, 'R2': -42.95295986017446, 'Pearson': 0.09941587880194638} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 376, 'MAPE': 0.553, 'RMSE': 230.8181811374365, 'MAE': 156.85785323670936, 'SMAPE': 1.0411, 'ErrorMean': -148.10460852130504, 'ErrorStdDev': 177.03688225436366, 'R2': -1.9323993200418466, 'Pearson': 0.37535695840357897} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 94, 'MAPE': 0.5137, 'RMSE': 414.7175986288537, 'MAE': 300.5475159431032, 'SMAPE': 1.0137, 'ErrorMean': -292.21419080240844, 'ErrorStdDev': 294.2814185540375, 'R2': -112.49688933861748, 'Pearson': 0.02356610001797782} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 145.6242320537567 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 61.07785773277283 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-05T00:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_D' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_D' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -174,22 +174,22 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W']' 44.5496187210083 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W']' 22.534905672073364 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 44.74139928817749 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 22.62450361251831 Int64Index: 4015 entries, 0 to 4014 Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_D_start 4015 non-null datetime64[ns] - 1 TH_2W_start 287 non-null datetime64[ns] - 2 TH_W_start 574 non-null datetime64[ns] + 0 TH_2W_start 287 non-null datetime64[ns] + 1 TH_W_start 574 non-null datetime64[ns] + 2 TH_D_start 4015 non-null datetime64[ns] 3 Signal_D 3650 non-null float64 4 Signal_D_Forecast 4015 non-null float64 5 Signal_D_Forecast_Lower_Bound 365 non-null float64 @@ -219,11 +219,11 @@ Data columns (total 30 columns): 29 Signal_2W_OC_Forecast 574 non-null float64 dtypes: datetime64[ns](3), float64(27) memory usage: 1.1 MB - TH_D_start TH_2W_start ... Signal_W_OC_Forecast Signal_2W_OC_Forecast -4010 2012-01-18 NaT ... NaN NaN -4011 2012-01-19 NaT ... 244.897663 244.897663 -4012 2012-01-20 NaT ... NaN NaN -4013 2012-01-21 NaT ... NaN NaN -4014 2012-01-22 NaT ... NaN NaN + TH_2W_start TH_W_start ... Signal_W_OC_Forecast Signal_2W_OC_Forecast +4010 NaT NaT ... NaN NaN +4011 NaT 2012-01-19 ... 244.897663 244.897663 +4012 NaT NaT ... NaN NaN +4013 NaT NaT ... NaN NaN +4014 NaT NaT ... NaN NaN [5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W_Q.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W_Q.log index 8b4a6671b..527e2a990 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W_Q.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_2W_Q.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.913963794708252 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3536813259124756 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, '2W': 1209600.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -9,10 +9,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, '2W': 26, 'Q': 4} INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' 81.93476343154907 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' 39.80104732513428 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' 47.417360067367554 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' 23.281538009643555 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -21,7 +21,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_Q_start', 'TH_W_start', 'TH_D_start', 'TH_2W_start', 'Signal_D', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'TH_D_start', 'TH_2W_start', 'TH_Q_start', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', @@ -92,7 +92,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 47, 'MAPE': 0.2172, 'RMSE': 127.62945759622944, 'MAE': 126.78780401822594, 'SMAPE': 0.2437, 'ErrorMean': -126.78780401822594, 'ErrorStdDev': 14.633222425141486, 'R2': -9.569074934509938, 'Pearson': 0.9637160010204563} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 188, 'MAPE': 1.0528, 'RMSE': 321.675960610624, 'MAE': 291.3519462466448, 'SMAPE': 1.978, 'ErrorMean': -290.6450174739586, 'ErrorStdDev': 137.84374288421662, 'R2': -4.659316380271651, 'Pearson': 0.1015045737083258} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 47, 'MAPE': 1.0, 'RMSE': 583.9384590539014, 'MAE': 582.617287017749, 'SMAPE': 2.0, 'ErrorMean': -582.617287017749, 'ErrorStdDev': 39.25838547778868, 'R2': -220.24287269778242, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 130.6584403514862 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 63.650203704833984 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-05T00:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_D' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_D' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -231,23 +231,23 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' 45.58655595779419 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_2W', 'Signal_Q']' 23.330636739730835 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 45.82208704948425 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 23.441579341888428 Int64Index: 4015 entries, 0 to 4014 Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_Q_start 44 non-null datetime64[ns] - 1 TH_W_start 574 non-null datetime64[ns] - 2 TH_D_start 4015 non-null datetime64[ns] - 3 TH_2W_start 287 non-null datetime64[ns] + 0 TH_W_start 574 non-null datetime64[ns] + 1 TH_D_start 4015 non-null datetime64[ns] + 2 TH_2W_start 287 non-null datetime64[ns] + 3 TH_Q_start 44 non-null datetime64[ns] 4 Signal_D 3650 non-null float64 5 Signal_D_Forecast 4015 non-null float64 6 Signal_D_Forecast_Lower_Bound 365 non-null float64 @@ -286,11 +286,11 @@ Data columns (total 40 columns): 39 Signal_Q_OC_Forecast 287 non-null float64 dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_Q_start TH_W_start ... Signal_2W_OC_Forecast Signal_Q_OC_Forecast -4010 NaT NaT ... NaN NaN -4011 NaT 2012-01-19 ... NaN NaN -4012 NaT NaT ... NaN NaN -4013 NaT NaT ... NaN NaN -4014 NaT NaT ... NaN NaN + TH_W_start TH_D_start ... Signal_2W_OC_Forecast Signal_Q_OC_Forecast +4010 NaT 2012-01-18 ... NaN NaN +4011 2012-01-19 2012-01-19 ... NaN NaN +4012 NaT 2012-01-20 ... NaN NaN +4013 NaT 2012-01-21 ... NaN NaN +4014 NaT 2012-01-22 ... NaN NaN [5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M.log index 4f234dea8..eef012bdf 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.7596802711486816 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.38475465774536133 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, 'M': 2419200.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -8,10 +8,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, 'M': 13} INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_M']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M']' 71.53393411636353 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M']' 40.857767820358276 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_M']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M']' 40.859419107437134 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M']' 22.715515851974487 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -20,7 +20,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'TH_W_start', 'TH_D_start', 'Signal_D', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'TH_M_start', 'TH_W_start', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', @@ -66,7 +66,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 94, 'MAPE': 0.6258, 'RMSE': 366.55568040809214, 'MAE': 365.35641130354577, 'SMAPE': 0.8895, 'ErrorMean': -336.0003435892582, 'ErrorStdDev': 146.51565086140056, 'R2': -87.66637158460122, 'Pearson': 0.16602317932953364} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 376, 'MAPE': 0.9854, 'RMSE': 319.64101037088886, 'MAE': 286.3337758111641, 'SMAPE': 1.931, 'ErrorMean': -285.6647860093253, 'ErrorStdDev': 143.408526751965, 'R2': -4.623517757052106, 'Pearson': 0.07861263627714493} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 94, 'MAPE': 0.9689, 'RMSE': 575.3061738739809, 'MAE': 565.3248645679956, 'SMAPE': 1.937, 'ErrorMean': -565.3248645679956, 'ErrorStdDev': 106.70047421964209, 'R2': -217.4123028203962, 'Pearson': 0.06653159831430137} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 113.42508840560913 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 64.04975318908691 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-05T00:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_D' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_D' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -173,22 +173,22 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M']' 41.31870365142822 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M']' 22.301262617111206 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 41.49259328842163 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 22.38631296157837 Int64Index: 4015 entries, 0 to 4014 Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_M_start 133 non-null datetime64[ns] - 1 TH_W_start 574 non-null datetime64[ns] - 2 TH_D_start 4015 non-null datetime64[ns] + 0 TH_D_start 4015 non-null datetime64[ns] + 1 TH_M_start 133 non-null datetime64[ns] + 2 TH_W_start 574 non-null datetime64[ns] 3 Signal_D 3650 non-null float64 4 Signal_D_Forecast 4015 non-null float64 5 Signal_D_Forecast_Lower_Bound 365 non-null float64 @@ -218,11 +218,11 @@ Data columns (total 30 columns): 29 Signal_M_OC_Forecast 574 non-null float64 dtypes: datetime64[ns](3), float64(27) memory usage: 1.1 MB - TH_M_start TH_W_start ... Signal_W_OC_Forecast Signal_M_OC_Forecast -4010 NaT NaT ... NaN NaN + TH_D_start TH_M_start ... Signal_W_OC_Forecast Signal_M_OC_Forecast +4010 2012-01-18 NaT ... NaN NaN 4011 2012-01-19 2012-01-19 ... 1348.704416 1348.704416 -4012 NaT NaT ... NaN NaN -4013 NaT NaT ... NaN NaN -4014 NaT NaT ... NaN NaN +4012 2012-01-20 NaT ... NaN NaN +4013 2012-01-21 NaT ... NaN NaN +4014 2012-01-22 NaT ... NaN NaN [5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M_Q.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M_Q.log index 3b59c62c8..82b3d816c 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M_Q.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_M_Q.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.454874038696289 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.4121592044830322 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, 'M': 2419200.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -9,10 +9,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, 'M': 13, 'Q': 4} INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' 74.0800268650055 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' 38.9012885093689 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' 48.47303485870361 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' 24.144730806350708 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -21,7 +21,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'TH_D_start', 'TH_W_start', 'TH_Q_start', 'Signal_D', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'TH_M_start', 'TH_D_start', 'TH_Q_start', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', @@ -92,7 +92,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 3, 'MAPE': 0.3315, 'RMSE': 196.6528511173861, 'MAE': 196.3725097523904, 'SMAPE': 0.2843, 'ErrorMean': 196.3725097523904, 'ErrorStdDev': 10.49672644895297, 'R2': -13.474075525830203, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 14, 'MAPE': 1.7776, 'RMSE': 296.73424571568427, 'MAE': 254.62599329310325, 'SMAPE': 1.8429, 'ErrorMean': -240.6856939116558, 'ErrorStdDev': 173.5557815998097, 'R2': -2.8079587100582497, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 3, 'MAPE': 1.0, 'RMSE': 596.488998895877, 'MAE': 594.2451435619568, 'SMAPE': 2.0, 'ErrorMean': -594.2451435619568, 'ErrorStdDev': 51.68979741530114, 'R2': -132.1665708842945, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 124.16288590431213 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 63.66074562072754 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-05T00:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_D' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_D' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -231,22 +231,22 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' 62.65265965461731 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_M', 'Signal_Q']' 24.53187608718872 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 62.88531303405762 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 24.651520252227783 Int64Index: 4015 entries, 0 to 4014 Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_M_start 133 non-null datetime64[ns] - 1 TH_D_start 4015 non-null datetime64[ns] - 2 TH_W_start 574 non-null datetime64[ns] + 0 TH_W_start 574 non-null datetime64[ns] + 1 TH_M_start 133 non-null datetime64[ns] + 2 TH_D_start 4015 non-null datetime64[ns] 3 TH_Q_start 44 non-null datetime64[ns] 4 Signal_D 3650 non-null float64 5 Signal_D_Forecast 4015 non-null float64 @@ -286,11 +286,11 @@ Data columns (total 40 columns): 39 Signal_Q_OC_Forecast 21 non-null float64 dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_M_start TH_D_start ... Signal_M_OC_Forecast Signal_Q_OC_Forecast -4010 NaT 2012-01-18 ... NaN NaN + TH_W_start TH_M_start ... Signal_M_OC_Forecast Signal_Q_OC_Forecast +4010 NaT NaT ... NaN NaN 4011 2012-01-19 2012-01-19 ... 1011.528312 1011.528312 -4012 NaT 2012-01-20 ... NaN NaN -4013 NaT 2012-01-21 ... NaN NaN -4014 NaT 2012-01-22 ... NaN NaN +4012 NaT NaT ... NaN NaN +4013 NaT NaT ... NaN NaN +4014 NaT NaT ... NaN NaN [5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_Q.log b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_Q.log index 3d4a3d3cb..46be682f5 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_Q.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_daily_D_W_Q.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.042081356048584 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.37039780616760254 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'D': 86400.0, 'W': 604800.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA D {'TH_D_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-26 00:00:00'), 2: Timestamp('2001-01-27 00:00:00'), 3: Timestamp('2001-01-28 00:00:00'), 4: Timestamp('2001-01-29 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -8,10 +8,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'D': 365, 'W': 52, 'Q': 4} INFO:pyaf.std:START_TRAINING '['Signal_D', 'Signal_W', 'Signal_Q']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 76.61522889137268 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 39.34504294395447 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_D', 'Signal_W', 'Signal_Q']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 48.65848731994629 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 22.76337170600891 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -20,7 +20,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'TH_Q_start', 'TH_D_start', 'Signal_D', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'TH_D_start', 'TH_Q_start', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', @@ -76,7 +76,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 94, 'MAPE': 0.6209, 'RMSE': 363.2995151004631, 'MAE': 362.49301943996755, 'SMAPE': 0.9005, 'ErrorMean': -362.49301943996755, 'ErrorStdDev': 24.19397713330535, 'R2': -86.09809673662467, 'Pearson': 0.9914232675117872} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 376, 'MAPE': 1.0192, 'RMSE': 322.1917471430979, 'MAE': 291.8070999296352, 'SMAPE': 1.9737, 'ErrorMean': -291.2375015888688, 'ErrorStdDev': 137.79782144647865, 'R2': -4.713627261850848, 'Pearson': 0.09778886526916761} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 94, 'MAPE': 1.0, 'RMSE': 585.0828979545455, 'MAE': 583.7864502550929, 'SMAPE': 2.0, 'ErrorMean': -583.7864502550929, 'ErrorStdDev': 38.92785606024334, 'R2': -224.89875435276088, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 126.47476935386658 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 62.63759183883667 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_D_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2008-04-05T00:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_D' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_D' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -180,22 +180,22 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 40.49080967903137 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_D', 'Signal_W', 'Signal_Q']' 24.490766525268555 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 40.701812505722046 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 24.579797506332397 Int64Index: 4015 entries, 0 to 4014 Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 TH_W_start 574 non-null datetime64[ns] - 1 TH_Q_start 44 non-null datetime64[ns] - 2 TH_D_start 4015 non-null datetime64[ns] + 1 TH_D_start 4015 non-null datetime64[ns] + 2 TH_Q_start 44 non-null datetime64[ns] 3 Signal_D 3650 non-null float64 4 Signal_D_Forecast 4015 non-null float64 5 Signal_D_Forecast_Lower_Bound 365 non-null float64 @@ -225,11 +225,11 @@ Data columns (total 30 columns): 29 Signal_Q_OC_Forecast 574 non-null float64 dtypes: datetime64[ns](3), float64(27) memory usage: 1.1 MB - TH_W_start TH_Q_start ... Signal_W_OC_Forecast Signal_Q_OC_Forecast -4010 NaT NaT ... NaN NaN -4011 2012-01-19 NaT ... 244.897663 244.897663 -4012 NaT NaT ... NaN NaN -4013 NaT NaT ... NaN NaN -4014 NaT NaT ... NaN NaN + TH_W_start TH_D_start ... Signal_W_OC_Forecast Signal_Q_OC_Forecast +4010 NaT 2012-01-18 ... NaN NaN +4011 2012-01-19 2012-01-19 ... 244.897663 244.897663 +4012 NaT 2012-01-20 ... NaN NaN +4013 NaT 2012-01-21 ... NaN NaN +4014 NaT 2012-01-22 ... NaN NaN [5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D.log b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D.log index 0eead17c1..1a93a6516 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.2391796112060547 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.5832300186157227 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'H': 3600.0, '6H': 21600.0, '12H': 43200.0, 'D': 86400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA H {'TH_H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 01:00:00'), 2: Timestamp('2001-01-25 02:00:00'), 3: Timestamp('2001-01-25 03:00:00'), 4: Timestamp('2001-01-25 04:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -9,10 +9,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'H': 365, '6H': 60, '12H': 30, 'D': 15} INFO:pyaf.std:START_TRAINING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' 74.07368779182434 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' 31.547768115997314 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' 30.77304744720459 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' 14.936144828796387 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -21,7 +21,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_12H_start', 'TH_6H_start', 'TH_D_start', 'TH_H_start', 'Signal_H', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'TH_12H_start', 'TH_H_start', 'TH_6H_start', 'Signal_H', 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', 'Signal_6H', 'Signal_6H_Forecast', 'Signal_6H_Forecast_Lower_Bound', 'Signal_6H_Forecast_Upper_Bound', @@ -83,7 +83,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_OC_Forecast', 'Length': 55, 'MAPE': 6.8162, 'RMSE': 612.8623643515351, 'MAE': 557.9094506012522, 'SMAPE': 1.4833, 'ErrorMean': 557.9094506012522, 'ErrorStdDev': 253.64802890691433, 'R2': -6438.445856034121, 'Pearson': 0.1042309503449344} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_PHA_TD_Forecast', 'Length': 219, 'MAPE': 0.6173, 'RMSE': 32.693550670322054, 'MAE': 23.057497742290554, 'SMAPE': 1.0813, 'ErrorMean': -20.12487780563557, 'ErrorStdDev': 25.765433214699733, 'R2': -1.6995724246375254, 'Pearson': 0.362355969822604} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_PHA_TD_Forecast', 'Length': 55, 'MAPE': 0.5381, 'RMSE': 59.161026520676636, 'MAE': 44.24548511950433, 'SMAPE': 1.0467, 'ErrorMean': -41.369426222377804, 'ErrorStdDev': 42.2918152011881, 'R2': -59.005905433984445, 'Pearson': 0.023804811367827663} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 105.93959355354309 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 47.039703607559204 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-05-14T11:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_H' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_H' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -205,23 +205,23 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' 31.079634428024292 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D']' 14.689745664596558 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 31.28459358215332 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 14.788064002990723 Int64Index: 4015 entries, 0 to 4014 Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_12H_start 335 non-null datetime64[ns] - 1 TH_6H_start 670 non-null datetime64[ns] - 2 TH_D_start 168 non-null datetime64[ns] - 3 TH_H_start 4015 non-null datetime64[ns] + 0 TH_D_start 168 non-null datetime64[ns] + 1 TH_12H_start 335 non-null datetime64[ns] + 2 TH_H_start 4015 non-null datetime64[ns] + 3 TH_6H_start 670 non-null datetime64[ns] 4 Signal_H 3650 non-null float64 5 Signal_H_Forecast 4015 non-null float64 6 Signal_H_Forecast_Lower_Bound 365 non-null float64 @@ -260,11 +260,11 @@ Data columns (total 40 columns): 39 Signal_D_OC_Forecast 335 non-null float64 dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_12H_start ... Signal_D_OC_Forecast -4010 NaT ... NaN -4011 NaT ... NaN -4012 NaT ... NaN -4013 NaT ... NaN -4014 NaT ... NaN + TH_D_start TH_12H_start ... Signal_12H_OC_Forecast Signal_D_OC_Forecast +4010 NaT NaT ... NaN NaN +4011 NaT NaT ... NaN NaN +4012 NaT NaT ... NaN NaN +4013 NaT NaT ... NaN NaN +4014 NaT NaT ... NaN NaN [5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D_W.log b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D_W.log index 226bf6a3a..415338dfe 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D_W.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_6H_12H_D_W.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.7813715934753418 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3821372985839844 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'H': 3600.0, '6H': 21600.0, '12H': 43200.0, 'D': 86400.0, 'W': 604800.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA H {'TH_H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 01:00:00'), 2: Timestamp('2001-01-25 02:00:00'), 3: Timestamp('2001-01-25 03:00:00'), 4: Timestamp('2001-01-25 04:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -10,10 +10,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'H': 365, '6H': 60, '12H': 30, 'D': 15, 'W': 2} INFO:pyaf.std:START_TRAINING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' 77.14198446273804 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' 33.53320336341858 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' 26.22898268699646 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' 15.584212303161621 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -22,7 +22,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3, 4] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'TH_H_start', 'TH_6H_start', 'TH_W_start', 'TH_12H_start', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'TH_6H_start', 'TH_H_start', 'TH_12H_start', 'TH_D_start', 'Signal_H', 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', 'Signal_6H', 'Signal_6H_Forecast', 'Signal_6H_Forecast_Lower_Bound', 'Signal_6H_Forecast_Upper_Bound', @@ -99,7 +99,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 4, 'MAPE': 0.751, 'RMSE': 10523.249623274069, 'MAE': 10506.75591031019, 'SMAPE': 1.2025, 'ErrorMean': -10506.75591031019, 'ErrorStdDev': 588.9506557426446, 'R2': -178.3495645263282, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 16, 'MAPE': 0.0, 'RMSE': 1.632702903446565e-13, 'MAE': 6.394884621840902e-14, 'SMAPE': 0.0, 'ErrorMean': 6.394884621840902e-14, 'ErrorStdDev': 1.5022560626125862e-13, 'R2': 1.0, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 4, 'MAPE': 0.0, 'RMSE': 0.0, 'MAE': 0.0, 'SMAPE': 0.0, 'ErrorMean': 0.0, 'ErrorStdDev': 0.0, 'R2': 1.0, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 104.3812563419342 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 49.75566244125366 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-05-14T11:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_H' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_H' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -246,24 +246,24 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' 23.32908606529236 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_6H', 'Signal_12H', 'Signal_D', 'Signal_W']' 14.975531578063965 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 23.583929538726807 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 15.075637102127075 Int64Index: 4015 entries, 0 to 4014 Data columns (total 50 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_D_start 168 non-null datetime64[ns] - 1 TH_H_start 4015 non-null datetime64[ns] - 2 TH_6H_start 670 non-null datetime64[ns] - 3 TH_W_start 24 non-null datetime64[ns] - 4 TH_12H_start 335 non-null datetime64[ns] + 0 TH_W_start 24 non-null datetime64[ns] + 1 TH_6H_start 670 non-null datetime64[ns] + 2 TH_H_start 4015 non-null datetime64[ns] + 3 TH_12H_start 335 non-null datetime64[ns] + 4 TH_D_start 168 non-null datetime64[ns] 5 Signal_H 3650 non-null float64 6 Signal_H_Forecast 4015 non-null float64 7 Signal_H_Forecast_Lower_Bound 365 non-null float64 @@ -311,11 +311,11 @@ Data columns (total 50 columns): 49 Signal_W_OC_Forecast 168 non-null float64 dtypes: datetime64[ns](5), float64(45) memory usage: 1.7 MB - TH_D_start TH_H_start ... Signal_D_OC_Forecast Signal_W_OC_Forecast -4010 NaT 2001-07-11 02:00:00 ... NaN NaN -4011 NaT 2001-07-11 03:00:00 ... NaN NaN -4012 NaT 2001-07-11 04:00:00 ... NaN NaN -4013 NaT 2001-07-11 05:00:00 ... NaN NaN + TH_W_start TH_6H_start ... Signal_D_OC_Forecast Signal_W_OC_Forecast +4010 NaT NaT ... NaN NaN +4011 NaT NaT ... NaN NaN +4012 NaT NaT ... NaN NaN +4013 NaT NaT ... NaN NaN 4014 NaT 2001-07-11 06:00:00 ... NaN NaN [5 rows x 50 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_D.log b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_D.log index e095312c0..60334d68f 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_D.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_hourly_H_D.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.0543439388275146 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.38388681411743164 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'H': 3600.0, 'D': 86400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA H {'TH_H_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 01:00:00'), 2: Timestamp('2001-01-25 02:00:00'), 3: Timestamp('2001-01-25 03:00:00'), 4: Timestamp('2001-01-25 04:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -7,10 +7,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'H': 365, 'D': 15} INFO:pyaf.std:START_TRAINING '['Signal_H', 'Signal_D']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_H', 'Signal_D']' 71.48737931251526 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_H', 'Signal_D']' 27.154861450195312 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_H', 'Signal_D']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_D']' 43.46589779853821 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_D']' 14.71866250038147 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -19,7 +19,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_H_start', 'TH_D_start', 'Signal_H', 'Signal_H_Forecast', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_D_start', 'TH_H_start', 'Signal_H', 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', 'Signal_D', 'Signal_D_Forecast', 'Signal_D_Forecast_Lower_Bound', 'Signal_D_Forecast_Upper_Bound', 'Signal_H_BU_Forecast', @@ -50,7 +50,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_OC_Forecast', 'Length': 657, 'MAPE': 0.9635, 'RMSE': 199.61988653068522, 'MAE': 79.57807971862903, 'SMAPE': 0.7105, 'ErrorMean': -0.4961731803278468, 'ErrorStdDev': 199.61926988820184, 'R2': -665.8313127199131, 'Pearson': -0.004747381461205392} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_H_PHA_TD_Forecast', 'Length': 2628, 'MAPE': 0.9681, 'RMSE': 45.87926485230503, 'MAE': 40.82669657478764, 'SMAPE': 1.9234, 'ErrorMean': -40.581511236427275, 'ErrorStdDev': 21.402053386431845, 'R2': -4.291884059409169, 'Pearson': 0.07192948765994124} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_H_PHA_TD_Forecast', 'Length': 657, 'MAPE': 0.9613, 'RMSE': 82.02332561368509, 'MAE': 80.16281794732217, 'SMAPE': 1.9202, 'ErrorMean': -79.92205198180929, 'ErrorStdDev': 18.44699302720046, 'R2': -111.5856717878089, 'Pearson': -0.02374645678387373} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 115.82175493240356 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 42.24021291732788 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_H_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-05-14T11:00:00.000000 TimeDelta= Horizon=365 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_H' Length=3650 Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_H' Min=-0.6219775856788415 Max=110.89054328721227 Mean=55.109633775095325 StdDev=27.026407591793713 @@ -122,21 +122,21 @@ Data columns (total 2 columns): 1 Signal 3650 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 57.2 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_D']' 32.6183717250824 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_H', 'Signal_D']' 15.570538759231567 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 32.79709267616272 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 15.62459135055542 Int64Index: 4015 entries, 0 to 4014 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_H_start 4015 non-null datetime64[ns] - 1 TH_D_start 168 non-null datetime64[ns] + 0 TH_D_start 168 non-null datetime64[ns] + 1 TH_H_start 4015 non-null datetime64[ns] 2 Signal_H 3650 non-null float64 3 Signal_H_Forecast 4015 non-null float64 4 Signal_H_Forecast_Lower_Bound 365 non-null float64 @@ -157,11 +157,11 @@ Data columns (total 20 columns): 19 Signal_D_OC_Forecast 4015 non-null float64 dtypes: datetime64[ns](2), float64(18) memory usage: 818.7 KB - TH_H_start TH_D_start ... Signal_H_OC_Forecast Signal_D_OC_Forecast -4010 2001-07-11 02:00:00 NaT ... 59.869430 59.869430 -4011 2001-07-11 03:00:00 NaT ... 49.809339 49.809339 -4012 2001-07-11 04:00:00 NaT ... 50.356958 50.356958 -4013 2001-07-11 05:00:00 NaT ... 50.822434 50.822434 -4014 2001-07-11 06:00:00 NaT ... 51.413610 51.413610 + TH_D_start TH_H_start ... Signal_H_OC_Forecast Signal_D_OC_Forecast +4010 NaT 2001-07-11 02:00:00 ... 59.869430 59.869430 +4011 NaT 2001-07-11 03:00:00 ... 49.809339 49.809339 +4012 NaT 2001-07-11 04:00:00 ... 50.356958 50.356958 +4013 NaT 2001-07-11 05:00:00 ... 50.822434 50.822434 +4014 NaT 2001-07-11 06:00:00 ... 51.413610 51.413610 [5 rows x 20 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_10T_30T_H.log b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_10T_30T_H.log index b29cb7a9f..f5f93fd32 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_10T_30T_H.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_10T_30T_H.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.9774734973907471 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3663058280944824 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'T': 60.0, '10T': 600.0, '30T': 1800.0, 'H': 3600.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA T {'TH_T_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 00:01:00'), 2: Timestamp('2001-01-25 00:02:00'), 3: Timestamp('2001-01-25 00:03:00'), 4: Timestamp('2001-01-25 00:04:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -9,10 +9,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'T': 360, '10T': 36, '30T': 12, 'H': 6} INFO:pyaf.std:START_TRAINING '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' 76.67015242576599 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' 32.10136389732361 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' 63.994211196899414 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' 23.998467206954956 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -21,7 +21,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_10T_start', 'TH_T_start', 'TH_H_start', 'TH_30T_start', 'Signal_T', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_T_start', 'TH_30T_start', 'TH_H_start', 'TH_10T_start', 'Signal_T', 'Signal_T_Forecast', 'Signal_T_Forecast_Lower_Bound', 'Signal_T_Forecast_Upper_Bound', 'Signal_10T', 'Signal_10T_Forecast', 'Signal_10T_Forecast_Lower_Bound', 'Signal_10T_Forecast_Upper_Bound', @@ -83,7 +83,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_T_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_T_OC_Forecast', 'Length': 21, 'MAPE': 16.6055, 'RMSE': 1496.2105804795378, 'MAE': 1356.706507617834, 'SMAPE': 1.7447, 'ErrorMean': 1356.706507617834, 'ErrorStdDev': 630.8673024704451, 'R2': -35716.24917047194, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_T_PHA_TD_Forecast', 'Length': 87, 'MAPE': 0.6371, 'RMSE': 32.312768390160485, 'MAE': 23.06907651546719, 'SMAPE': 1.0876, 'ErrorMean': -19.891594013678887, 'ErrorStdDev': 25.46447503152439, 'R2': -1.6688412877475987, 'Pearson': 0.36576410129928144} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_T_PHA_TD_Forecast', 'Length': 21, 'MAPE': 0.5512, 'RMSE': 58.76866398911768, 'MAE': 44.450047105746584, 'SMAPE': 1.0751, 'ErrorMean': -42.644403963093666, 'ErrorStdDev': 40.43773828613933, 'R2': -54.104135850329435, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 141.63613033294678 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 56.566752195358276 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_T_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-01-26T19:11:00.000000 TimeDelta= Horizon=360 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_T' Length=3600 Min=-0.6219775856788415 Max=110.89054328721227 Mean=54.47905330902324 StdDev=26.666240075001483 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_T' Min=-0.6219775856788415 Max=110.89054328721227 Mean=54.47905330902324 StdDev=26.666240075001483 @@ -235,23 +235,23 @@ Data columns (total 2 columns): 1 Signal 3600 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 56.4 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' 63.43291783332825 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_10T', 'Signal_30T', 'Signal_H']' 24.498213529586792 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 63.66168189048767 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 24.592234134674072 Int64Index: 3960 entries, 0 to 3959 Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_10T_start 396 non-null datetime64[ns] - 1 TH_T_start 3960 non-null datetime64[ns] + 0 TH_T_start 3960 non-null datetime64[ns] + 1 TH_30T_start 132 non-null datetime64[ns] 2 TH_H_start 66 non-null datetime64[ns] - 3 TH_30T_start 132 non-null datetime64[ns] + 3 TH_10T_start 396 non-null datetime64[ns] 4 Signal_T 3600 non-null float64 5 Signal_T_Forecast 3960 non-null float64 6 Signal_T_Forecast_Lower_Bound 360 non-null float64 @@ -290,11 +290,11 @@ Data columns (total 40 columns): 39 Signal_H_OC_Forecast 132 non-null float64 dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_10T_start ... Signal_H_OC_Forecast -3955 NaT ... NaN -3956 NaT ... NaN -3957 NaT ... NaN -3958 NaT ... NaN -3959 NaT ... NaN + TH_T_start ... Signal_H_OC_Forecast +3955 2001-01-27 17:55:00 ... NaN +3956 2001-01-27 17:56:00 ... NaN +3957 2001-01-27 17:57:00 ... NaN +3958 2001-01-27 17:58:00 ... NaN +3959 2001-01-27 17:59:00 ... NaN [5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H_12H_D.log b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H_12H_D.log index 135829c0f..ea840e2ab 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H_12H_D.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_minutely_T_H_12H_D.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.3878440856933594 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.45372796058654785 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'T': 60.0, 'H': 3600.0, '12H': 43200.0, 'D': 86400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA T {'TH_T_start': {0: Timestamp('2001-01-25 00:00:00'), 1: Timestamp('2001-01-25 00:01:00'), 2: Timestamp('2001-01-25 00:02:00'), 3: Timestamp('2001-01-25 00:03:00'), 4: Timestamp('2001-01-25 00:04:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -9,10 +9,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'T': 360, 'H': 6, '12H': 1, 'D': 1} INFO:pyaf.std:START_TRAINING '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' 68.79878973960876 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' 30.308656215667725 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' 50.156755208969116 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' 22.470651388168335 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -21,7 +21,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_12H_start', 'TH_D_start', 'TH_T_start', 'TH_H_start', 'Signal_T', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_H_start', 'TH_D_start', 'TH_T_start', 'TH_12H_start', 'Signal_T', 'Signal_T_Forecast', 'Signal_T_Forecast_Lower_Bound', 'Signal_T_Forecast_Upper_Bound', 'Signal_H', 'Signal_H_Forecast', 'Signal_H_Forecast_Lower_Bound', 'Signal_H_Forecast_Upper_Bound', @@ -83,7 +83,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_T_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_T_OC_Forecast', 'Length': 1, 'MAPE': 441.8558, 'RMSE': 33721.71010574657, 'MAE': 33721.71010574657, 'SMAPE': 1.991, 'ErrorMean': 33721.71010574657, 'ErrorStdDev': 0.0, 'R2': -1.1371537324560103e+19, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_T_PHA_TD_Forecast', 'Length': 4, 'MAPE': 4.3464, 'RMSE': 40.949621125743285, 'MAE': 34.38982726150292, 'SMAPE': 1.5134, 'ErrorMean': -19.974716048910366, 'ErrorStdDev': 35.74747808038104, 'R2': -1.5054658222101591, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_T_PHA_TD_Forecast', 'Length': 1, 'MAPE': 0.4548, 'RMSE': 34.709657817059146, 'MAE': 34.709657817059146, 'SMAPE': 0.5887, 'ErrorMean': -34.709657817059146, 'ErrorStdDev': 0.0, 'R2': -12047603457772.35, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 120.14171934127808 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 53.25173044204712 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_T_start' TimeMin=2001-01-25T00:00:00.000000 TimeMax=2001-01-26T19:11:00.000000 TimeDelta= Horizon=360 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_T' Length=3600 Min=-0.6219775856788415 Max=110.89054328721227 Mean=54.47905330902324 StdDev=26.666240075001483 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_T' Min=-0.6219775856788415 Max=110.89054328721227 Mean=54.47905330902324 StdDev=26.666240075001483 @@ -215,23 +215,23 @@ Data columns (total 2 columns): 1 Signal 3600 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 56.4 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' 53.76600742340088 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_T', 'Signal_H', 'Signal_12H', 'Signal_D']' 22.583261489868164 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 53.92520570755005 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 22.66943907737732 Int64Index: 3960 entries, 0 to 3959 Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_12H_start 6 non-null datetime64[ns] + 0 TH_H_start 66 non-null datetime64[ns] 1 TH_D_start 3 non-null datetime64[ns] 2 TH_T_start 3960 non-null datetime64[ns] - 3 TH_H_start 66 non-null datetime64[ns] + 3 TH_12H_start 6 non-null datetime64[ns] 4 Signal_T 3600 non-null float64 5 Signal_T_Forecast 3960 non-null float64 6 Signal_T_Forecast_Lower_Bound 360 non-null float64 @@ -270,11 +270,11 @@ Data columns (total 40 columns): 39 Signal_D_OC_Forecast 6 non-null float64 dtypes: datetime64[ns](4), float64(36) memory usage: 1.4 MB - TH_12H_start TH_D_start ... Signal_12H_OC_Forecast Signal_D_OC_Forecast -3955 NaT NaT ... NaN NaN -3956 NaT NaT ... NaN NaN -3957 NaT NaT ... NaN NaN -3958 NaT NaT ... NaN NaN -3959 NaT NaT ... NaN NaN + TH_H_start TH_D_start ... Signal_12H_OC_Forecast Signal_D_OC_Forecast +3955 NaT NaT ... NaN NaN +3956 NaT NaT ... NaN NaN +3957 NaT NaT ... NaN NaN +3958 NaT NaT ... NaN NaN +3959 NaT NaT ... NaN NaN [5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M.log b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M.log index 8bc42aa80..cd177668c 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.6983253955841064 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3832685947418213 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'M': 2419200.0, '2M': 5097600.0, '6M': 15638400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA M {'TH_M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-02-28 00:00:00'), 2: Timestamp('2001-03-31 00:00:00'), 3: Timestamp('2001-04-30 00:00:00'), 4: Timestamp('2001-05-31 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -8,10 +8,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'M': 36, '2M': 17, '6M': 5} INFO:pyaf.std:START_TRAINING '['Signal_M', 'Signal_2M', 'Signal_6M']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M']' 31.488470554351807 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M']' 13.57753872871399 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_M', 'Signal_2M', 'Signal_6M']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M']' 8.709264278411865 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M']' 2.219606876373291 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -20,7 +20,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'TH_6M_start', 'TH_2M_start', 'Signal_M', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_6M_start', 'TH_M_start', 'TH_2M_start', 'Signal_M', 'Signal_M_Forecast', 'Signal_M_Forecast_Lower_Bound', 'Signal_M_Forecast_Upper_Bound', 'Signal_2M', 'Signal_2M_Forecast', 'Signal_2M_Forecast_Lower_Bound', 'Signal_2M_Forecast_Upper_Bound', @@ -67,7 +67,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_OC_Forecast', 'Length': 32, 'MAPE': 0.8068, 'RMSE': 19.711528061074375, 'MAE': 11.42575782328684, 'SMAPE': 0.3712, 'ErrorMean': 10.166874332625964, 'ErrorStdDev': 16.88724384874909, 'R2': -11.58848414685206, 'Pearson': 0.2775985303863618} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 130, 'MAPE': 1.0067, 'RMSE': 12.631486410964593, 'MAE': 10.343080307790597, 'SMAPE': 1.548, 'ErrorMean': -8.36307890719707, 'ErrorStdDev': 9.466433337977861, 'R2': -2.696275236380923, 'Pearson': 0.03909983703188895} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 32, 'MAPE': 0.7965, 'RMSE': 15.447921944904252, 'MAE': 13.068545896490715, 'SMAPE': 1.4482, 'ErrorMean': -11.712612505612515, 'ErrorStdDev': 10.072388034087234, 'R2': -6.731664222638842, 'Pearson': -0.013815763862450939} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 41.66358995437622 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 16.222784519195557 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-07-31T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_M' Length=360 Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_M' Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 @@ -184,21 +184,21 @@ Data columns (total 2 columns): 1 Signal 360 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 5.8 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M']' 7.24275803565979 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M']' 2.15343976020813 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 7.477752208709717 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.228959321975708 Int64Index: 396 entries, 0 to 395 Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_M_start 396 non-null datetime64[ns] - 1 TH_6M_start 60 non-null datetime64[ns] + 0 TH_6M_start 60 non-null datetime64[ns] + 1 TH_M_start 396 non-null datetime64[ns] 2 TH_2M_start 181 non-null datetime64[ns] 3 Signal_M 360 non-null float64 4 Signal_M_Forecast 396 non-null float64 @@ -229,11 +229,11 @@ Data columns (total 30 columns): 29 Signal_6M_OC_Forecast 181 non-null float64 dtypes: datetime64[ns](3), float64(27) memory usage: 115.9 KB - TH_M_start TH_6M_start ... Signal_2M_OC_Forecast Signal_6M_OC_Forecast -391 2033-08-17 NaT ... NaN NaN -392 2033-09-16 NaT ... NaN NaN -393 2033-10-16 NaT ... NaN NaN -394 2033-11-15 NaT ... NaN NaN -395 2033-12-15 NaT ... NaN NaN + TH_6M_start TH_M_start ... Signal_2M_OC_Forecast Signal_6M_OC_Forecast +391 NaT 2033-08-17 ... NaN NaN +392 NaT 2033-09-16 ... NaN NaN +393 NaT 2033-10-16 ... NaN NaN +394 NaT 2033-11-15 ... NaN NaN +395 NaT 2033-12-15 ... NaN NaN [5 rows x 30 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M_12M.log b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M_12M.log index 4339ce67e..18acbe46c 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M_12M.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_monthly_M_2M_6M_12M.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.083630084991455 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.3448910713195801 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'M': 2419200.0, '2M': 5097600.0, '6M': 15638400.0, '12M': 31536000.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA M {'TH_M_start': {0: Timestamp('2001-01-31 00:00:00'), 1: Timestamp('2001-02-28 00:00:00'), 2: Timestamp('2001-03-31 00:00:00'), 3: Timestamp('2001-04-30 00:00:00'), 4: Timestamp('2001-05-31 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -9,10 +9,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'M': 36, '2M': 17, '6M': 5, '12M': 2} INFO:pyaf.std:START_TRAINING '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' 32.46165728569031 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' 13.119838237762451 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' 5.117384433746338 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' 2.5728023052215576 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -21,7 +21,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_12M_start', 'TH_6M_start', 'TH_M_start', 'TH_2M_start', 'Signal_M', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_12M_start', 'TH_6M_start', 'TH_2M_start', 'TH_M_start', 'Signal_M', 'Signal_M_Forecast', 'Signal_M_Forecast_Lower_Bound', 'Signal_M_Forecast_Upper_Bound', 'Signal_2M', 'Signal_2M_Forecast', 'Signal_2M_Forecast_Lower_Bound', 'Signal_2M_Forecast_Upper_Bound', @@ -83,7 +83,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_OC_Forecast', 'Length': 10, 'MAPE': 3.0253, 'RMSE': 50.65099292621485, 'MAE': 45.13662673821462, 'SMAPE': 1.1036, 'ErrorMean': 45.13662673821462, 'ErrorStdDev': 22.982776401178242, 'R2': -83.35090860579044, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 44, 'MAPE': 1.0385, 'RMSE': 10.71256983316866, 'MAE': 8.917112991303807, 'SMAPE': 1.2819, 'ErrorMean': -5.423206409132171, 'ErrorStdDev': 9.238397300098265, 'R2': -1.8702263604502463, 'Pearson': 0.0021011393507872067} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_M_PHA_TD_Forecast', 'Length': 10, 'MAPE': 0.6495, 'RMSE': 12.010162451695138, 'MAE': 10.02411236215176, 'SMAPE': 1.1433, 'ErrorMean': -8.290333286653208, 'ErrorStdDev': 8.689900811419978, 'R2': -3.7425465447411463, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 38.86502122879028 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 16.258827686309814 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_M_start' TimeMin=2001-01-31T00:00:00.000000 TimeMax=2022-07-31T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_M' Length=360 Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_M' Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 @@ -230,14 +230,14 @@ Data columns (total 2 columns): 1 Signal 360 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 5.8 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' 4.974066734313965 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_M', 'Signal_2M', 'Signal_6M', 'Signal_12M']' 2.7360048294067383 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 5.2717506885528564 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.8421595096588135 Int64Index: 396 entries, 0 to 395 Data columns (total 40 columns): @@ -245,8 +245,8 @@ Data columns (total 40 columns): --- ------ -------------- ----- 0 TH_12M_start 32 non-null datetime64[ns] 1 TH_6M_start 60 non-null datetime64[ns] - 2 TH_M_start 396 non-null datetime64[ns] - 3 TH_2M_start 181 non-null datetime64[ns] + 2 TH_2M_start 181 non-null datetime64[ns] + 3 TH_M_start 396 non-null datetime64[ns] 4 Signal_M 360 non-null float64 5 Signal_M_Forecast 396 non-null float64 6 Signal_M_Forecast_Lower_Bound 36 non-null float64 diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_2W_M_Q.log b/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_2W_M_Q.log index f0d088d9d..6367d6f80 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_2W_M_Q.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_2W_M_Q.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 1.2555201053619385 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.4403717517852783 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'W': 604800.0, '2W': 1209600.0, 'M': 2419200.0, 'Q': 7862400.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-28 00:00:00'), 1: Timestamp('2001-02-04 00:00:00'), 2: Timestamp('2001-02-11 00:00:00'), 3: Timestamp('2001-02-18 00:00:00'), 4: Timestamp('2001-02-25 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -9,10 +9,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'W': 36, '2W': 18, 'M': 9, 'Q': 2} INFO:pyaf.std:START_TRAINING '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' 31.25135564804077 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' 13.342710018157959 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' 5.840206623077393 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' 2.3858070373535156 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -21,7 +21,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2, 3] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_2W_start', 'TH_W_start', 'TH_Q_start', 'TH_M_start', 'Signal_W', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_M_start', 'TH_W_start', 'TH_2W_start', 'TH_Q_start', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', 'Signal_2W', 'Signal_2W_Forecast', 'Signal_2W_Forecast_Lower_Bound', 'Signal_2W_Forecast_Upper_Bound', @@ -92,7 +92,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 1, 'MAPE': 1.068, 'RMSE': 12.723082166140163, 'MAE': 12.723082166140163, 'SMAPE': 0.6962, 'ErrorMean': 12.723082166140163, 'ErrorStdDev': 0.0, 'R2': -1618768198062.5386, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 6, 'MAPE': 0.8489, 'RMSE': 9.3817446808881, 'MAE': 7.735731748489172, 'SMAPE': 1.6816, 'ErrorMean': -7.676289353439198, 'ErrorStdDev': 5.393673610782192, 'R2': -2.631045953382735, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 1, 'MAPE': 1.0, 'RMSE': 11.912826793784575, 'MAE': 11.912826793784575, 'SMAPE': 2.0, 'ErrorMean': -11.912826793784575, 'ErrorStdDev': 0.0, 'R2': -1419154422186.1165, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 38.797916412353516 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 16.342135429382324 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_W_start' TimeMin=2001-01-28T00:00:00.000000 TimeMax=2006-01-08T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_W' Length=360 Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_W' Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 @@ -227,23 +227,23 @@ Data columns (total 2 columns): 1 Signal 360 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 5.8 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' 7.440672874450684 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_2W', 'Signal_M', 'Signal_Q']' 2.491361618041992 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 7.77457332611084 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.6062798500061035 Int64Index: 396 entries, 0 to 395 Data columns (total 40 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_2W_start 198 non-null datetime64[ns] + 0 TH_M_start 14 non-null datetime64[ns] 1 TH_W_start 396 non-null datetime64[ns] - 2 TH_Q_start 6 non-null datetime64[ns] - 3 TH_M_start 14 non-null datetime64[ns] + 2 TH_2W_start 198 non-null datetime64[ns] + 3 TH_Q_start 6 non-null datetime64[ns] 4 Signal_W 360 non-null float64 5 Signal_W_Forecast 396 non-null float64 6 Signal_W_Forecast_Lower_Bound 36 non-null float64 @@ -282,11 +282,11 @@ Data columns (total 40 columns): 39 Signal_Q_OC_Forecast 8 non-null float64 dtypes: datetime64[ns](4), float64(36) memory usage: 146.8 KB - TH_2W_start TH_W_start ... Signal_M_OC_Forecast Signal_Q_OC_Forecast -391 NaT 2008-07-27 ... NaN NaN -392 2008-08-03 2008-08-03 ... NaN NaN -393 NaT 2008-08-10 ... NaN NaN -394 2008-08-17 2008-08-17 ... NaN NaN -395 NaT 2008-08-24 ... NaN NaN + TH_M_start TH_W_start ... Signal_M_OC_Forecast Signal_Q_OC_Forecast +391 NaT 2008-07-27 ... NaN NaN +392 NaT 2008-08-03 ... NaN NaN +393 NaT 2008-08-10 ... NaN NaN +394 NaT 2008-08-17 ... NaN NaN +395 NaT 2008-08-24 ... NaN NaN [5 rows x 40 columns] diff --git a/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_Q_A.log b/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_Q_A.log index 05ada8883..3431df802 100644 --- a/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_Q_A.log +++ b/tests/references/temporal_hierarchy_test_temporal_demo_weekly_W_Q_A.log @@ -1,5 +1,5 @@ INFO:pyaf.std:START_HIERARCHICAL_PLOTTING -INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.9521405696868896 +INFO:pyaf.std:END_HIERARCHICAL_PLOTTING_TIME_IN_SECONDS 0.6321806907653809 INFO:pyaf.std:START_HIERARCHICAL_TRAINING INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_FREQUENCIES {'W': 604800.0, 'Q': 7862400.0, 'A': 31536000.0} INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLED_DATA W {'TH_W_start': {0: Timestamp('2001-01-28 00:00:00'), 1: Timestamp('2001-02-04 00:00:00'), 2: Timestamp('2001-02-11 00:00:00'), 3: Timestamp('2001-02-18 00:00:00'), 4: Timestamp('2001-02-25 00:00:00')}, 'Signal': {0: 1.9048968112034605, 1: -0.42967395611318193, 2: 0.6856129262199164, 3: 3.0251231243596757, 4: 4.401208611312959}} @@ -8,10 +8,10 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS_FIRST_RESAMPLE INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_TEMPORAL_HORIZONS {'W': 36, 'Q': 2, 'A': 1} INFO:pyaf.std:START_TRAINING '['Signal_W', 'Signal_Q', 'Signal_A']' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_W', 'Signal_Q', 'Signal_A']' 35.772974491119385 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal_W', 'Signal_Q', 'Signal_A']' 11.78502082824707 INFO:pyaf.hierarchical:TRAINING_HIERARCHICAL_MODEL_COMPUTE_TOP_DOWN_HISTORICAL_PROPORTIONS INFO:pyaf.std:START_FORECASTING '['Signal_W', 'Signal_Q', 'Signal_A']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_Q', 'Signal_A']' 5.386401176452637 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_Q', 'Signal_A']' 2.375234603881836 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD @@ -20,7 +20,7 @@ INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD INFO:pyaf.hierarchical:STRUCTURE [0, 1, 2] -INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_W_start', 'TH_Q_start', 'TH_A_start', 'Signal_W', +INFO:pyaf.hierarchical:DATASET_COLUMNS Index(['TH_A_start', 'TH_Q_start', 'TH_W_start', 'Signal_W', 'Signal_W_Forecast', 'Signal_W_Forecast_Lower_Bound', 'Signal_W_Forecast_Upper_Bound', 'Signal_Q', 'Signal_Q_Forecast', 'Signal_Q_Forecast_Lower_Bound', 'Signal_Q_Forecast_Upper_Bound', @@ -66,7 +66,7 @@ INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_O INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_OC_Forecast', 'Length': 2, 'MAPE': 8.1819, 'RMSE': 248.23838350673773, 'MAE': 204.45693673307989, 'SMAPE': 1.4667, 'ErrorMean': 204.45693673307989, 'ErrorStdDev': 140.78230026449904, 'R2': -13859.922219759077, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_FIT_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 3, 'MAPE': 3.2619, 'RMSE': 17.394843036456045, 'MAE': 17.04012586843329, 'SMAPE': 1.8637, 'ErrorMean': -7.152661853499441, 'ErrorStdDev': 15.856228828836864, 'R2': -3.424912810028623, 'Pearson': 0.0} INFO:pyaf.hierarchical:REPORT_COMBINED_FORECASTS_VALID_PERF {'Signal': 'Signal_W_PHA_TD_Forecast', 'Length': 2, 'MAPE': 0.5112, 'RMSE': 15.232331477230026, 'MAE': 11.05518657080111, 'SMAPE': 1.0113, 'ErrorMean': -11.05518657080111, 'ErrorStdDev': 10.478872654870441, 'R2': -51.18996690696763, 'Pearson': 0.0} -INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 42.26661801338196 +INFO:pyaf.std:END_HIERARCHICAL_TRAINING_TIME_IN_SECONDS 14.574724674224854 INFO:pyaf.std:TIME_DETAIL TimeVariable='TH_W_start' TimeMin=2001-01-28T00:00:00.000000 TimeMax=2006-01-08T00:00:00.000000 TimeDelta= Horizon=36 INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal_W' Length=360 Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal_W' Min=-0.6219775856788415 Max=28.87537250288579 Mean=13.929719090432725 StdDev=6.723008300790963 @@ -163,22 +163,22 @@ Data columns (total 2 columns): 1 Signal 360 non-null float64 dtypes: datetime64[ns](1), float64(1) memory usage: 5.8 KB -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_Q', 'Signal_A']' 5.498276472091675 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal_W', 'Signal_Q', 'Signal_A']' 2.3792884349823 INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_COMBINATION_METHODS ['BU', 'TD', 'MO', 'OC'] INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_BOTTOM_UP_METHOD BU INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD AHP_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_TOP_DOWN_METHOD PHA_TD INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_MIDDLE_OUT_METHOD MO INFO:pyaf.hierarchical:FORECASTING_HIERARCHICAL_MODEL_OPTIMAL_COMBINATION_METHOD OC -INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 5.682374000549316 +INFO:pyaf.std:END_HIERARCHICAL_FORECAST_TIME_IN_SECONDS 2.4516589641571045 Int64Index: 396 entries, 0 to 395 Data columns (total 30 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- - 0 TH_W_start 396 non-null datetime64[ns] + 0 TH_A_start 2 non-null datetime64[ns] 1 TH_Q_start 6 non-null datetime64[ns] - 2 TH_A_start 2 non-null datetime64[ns] + 2 TH_W_start 396 non-null datetime64[ns] 3 Signal_W 360 non-null float64 4 Signal_W_Forecast 396 non-null float64 5 Signal_W_Forecast_Lower_Bound 36 non-null float64 @@ -208,11 +208,11 @@ Data columns (total 30 columns): 29 Signal_A_OC_Forecast 6 non-null float64 dtypes: datetime64[ns](3), float64(27) memory usage: 115.9 KB - TH_W_start TH_Q_start ... Signal_Q_OC_Forecast Signal_A_OC_Forecast -391 2008-07-27 NaT ... NaN NaN -392 2008-08-03 NaT ... NaN NaN -393 2008-08-10 NaT ... NaN NaN -394 2008-08-17 NaT ... NaN NaN -395 2008-08-24 NaT ... NaN NaN + TH_A_start TH_Q_start ... Signal_Q_OC_Forecast Signal_A_OC_Forecast +391 NaT NaT ... NaN NaN +392 NaT NaT ... NaN NaN +393 NaT NaT ... NaN NaN +394 NaT NaT ... NaN NaN +395 NaT NaT ... NaN NaN [5 rows x 30 columns] From 12939da302401a7896e5424c5656fb80fdf3a1cf Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Thu, 30 Jul 2020 10:02:39 +0200 Subject: [PATCH 14/15] Added some logs --- tests/references/perf_gen_ar_speed_tests.log | 0 .../references/perf_gen_long_cycles_tests.log | 0 ...ng_cycles_nbrows_cycle_length_1000_140.log | 390 ++++++++++++++++++ ...ong_cycles_nbrows_cycle_length_1000_20.log | 140 +++++++ ...ng_cycles_nbrows_cycle_length_1000_200.log | 117 ++++++ ...ng_cycles_nbrows_cycle_length_1000_260.log | 117 ++++++ ...ng_cycles_nbrows_cycle_length_1000_320.log | 117 ++++++ ...ng_cycles_nbrows_cycle_length_1000_380.log | 117 ++++++ ...ng_cycles_nbrows_cycle_length_1000_440.log | 107 +++++ ...ong_cycles_nbrows_cycle_length_1000_80.log | 260 ++++++++++++ ...g_cycles_nbrows_cycle_length_11000_140.log | 380 +++++++++++++++++ ...ng_cycles_nbrows_cycle_length_11000_20.log | 140 +++++++ ...g_cycles_nbrows_cycle_length_11000_200.log | 107 +++++ ...g_cycles_nbrows_cycle_length_11000_260.log | 107 +++++ ...g_cycles_nbrows_cycle_length_11000_320.log | 107 +++++ ...g_cycles_nbrows_cycle_length_11000_380.log | 107 +++++ ...g_cycles_nbrows_cycle_length_11000_440.log | 107 +++++ ...ng_cycles_nbrows_cycle_length_11000_80.log | 260 ++++++++++++ ...g_cycles_nbrows_cycle_length_21000_140.log | 380 +++++++++++++++++ ...ng_cycles_nbrows_cycle_length_21000_20.log | 140 +++++++ ...g_cycles_nbrows_cycle_length_21000_200.log | 107 +++++ ...g_cycles_nbrows_cycle_length_21000_260.log | 107 +++++ ...g_cycles_nbrows_cycle_length_21000_320.log | 107 +++++ ...g_cycles_nbrows_cycle_length_21000_380.log | 107 +++++ ...g_cycles_nbrows_cycle_length_21000_440.log | 107 +++++ ...ng_cycles_nbrows_cycle_length_21000_80.log | 260 ++++++++++++ ...g_cycles_nbrows_cycle_length_31000_140.log | 380 +++++++++++++++++ ...ng_cycles_nbrows_cycle_length_31000_20.log | 140 +++++++ ...g_cycles_nbrows_cycle_length_31000_200.log | 107 +++++ ...g_cycles_nbrows_cycle_length_31000_260.log | 107 +++++ ...g_cycles_nbrows_cycle_length_31000_320.log | 107 +++++ ...g_cycles_nbrows_cycle_length_31000_380.log | 107 +++++ ...g_cycles_nbrows_cycle_length_31000_440.log | 107 +++++ ...ng_cycles_nbrows_cycle_length_31000_80.log | 260 ++++++++++++ ...g_cycles_nbrows_cycle_length_41000_140.log | 380 +++++++++++++++++ ...ng_cycles_nbrows_cycle_length_41000_20.log | 140 +++++++ ...g_cycles_nbrows_cycle_length_41000_200.log | 107 +++++ ...g_cycles_nbrows_cycle_length_41000_260.log | 107 +++++ ...g_cycles_nbrows_cycle_length_41000_320.log | 107 +++++ ...g_cycles_nbrows_cycle_length_41000_380.log | 107 +++++ ...g_cycles_nbrows_cycle_length_41000_440.log | 107 +++++ ...ng_cycles_nbrows_cycle_length_41000_80.log | 260 ++++++++++++ .../perf_test_ozone_ar_speed_order_0.log | 57 +++ .../perf_test_ozone_ar_speed_order_100.log | 67 +++ .../perf_test_ozone_ar_speed_order_150.log | 67 +++ .../perf_test_ozone_ar_speed_order_200.log | 67 +++ .../perf_test_ozone_ar_speed_order_250.log | 67 +++ .../perf_test_ozone_ar_speed_order_300.log | 67 +++ .../perf_test_ozone_ar_speed_order_350.log | 67 +++ .../perf_test_ozone_ar_speed_order_400.log | 67 +++ .../perf_test_ozone_ar_speed_order_450.log | 67 +++ .../perf_test_ozone_ar_speed_order_50.log | 67 +++ .../perf_test_ozone_ar_speed_order_500.log | 67 +++ .../perf_test_ozone_ar_speed_order_550.log | 67 +++ .../perf_test_ozone_ar_speed_order_600.log | 67 +++ .../perf_test_ozone_ar_speed_order_650.log | 67 +++ .../perf_test_ozone_ar_speed_order_700.log | 67 +++ .../perf_test_ozone_ar_speed_order_750.log | 67 +++ .../perf_test_ozone_ar_speed_order_800.log | 67 +++ .../perf_test_ozone_ar_speed_order_850.log | 67 +++ .../perf_test_ozone_ar_speed_order_900.log | 67 +++ .../perf_test_ozone_ar_speed_order_950.log | 67 +++ 62 files changed, 7955 insertions(+) create mode 100644 tests/references/perf_gen_ar_speed_tests.log create mode 100644 tests/references/perf_gen_long_cycles_tests.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_140.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_20.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_200.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_260.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_320.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_380.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_440.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_80.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_140.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_20.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_200.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_260.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_320.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_380.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_440.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_80.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_140.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_20.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_200.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_260.log create mode 100644 tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_320.log create mode 100644 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tests/references/perf_test_ozone_ar_speed_order_800.log create mode 100644 tests/references/perf_test_ozone_ar_speed_order_850.log create mode 100644 tests/references/perf_test_ozone_ar_speed_order_900.log create mode 100644 tests/references/perf_test_ozone_ar_speed_order_950.log diff --git a/tests/references/perf_gen_ar_speed_tests.log b/tests/references/perf_gen_ar_speed_tests.log new file mode 100644 index 000000000..e69de29bb diff --git a/tests/references/perf_gen_long_cycles_tests.log b/tests/references/perf_gen_long_cycles_tests.log new file mode 100644 index 000000000..e69de29bb diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_140.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_140.log new file mode 100644 index 000000000..e7084150b --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_140.log @@ -0,0 +1,390 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 1000 140 +GENERATING_RANDOM_DATASET Signal_1000_H_0_constant_140_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 20.772822856903076 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-02-11T03:00:00.000000 TimeDelta= Horizon=280 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=988 Min=1.0 Max=11.216527890240371 Mean=6.094239991525178 StdDev=2.91599045086134 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.216527890240371 Mean=6.094239991525178 StdDev=2.91599045086134 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)' [ConstantTrend + Cycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.4415 MAPE_Forecast=0.4415 MAPE_Test=0.4415 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3463 SMAPE_Forecast=0.3463 SMAPE_Test=0.3463 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4882 MASE_Forecast=0.4882 MASE_Test=0.4882 +INFO:pyaf.std:MODEL_L1 L1_Fit=1.7390774615130888 L1_Forecast=1.7390774615130888 L1_Test=1.7390774615130888 +INFO:pyaf.std:MODEL_L2 L2_Fit=2.2996144174678257 L2_Forecast=2.2996144174678257 L2_Test=2.2996144174678257 +INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.094239991525178 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 56 0.2681638776131643 {0: -2.621570118998367, 1: -3.157472713951746, 2: 4.035294710913284, 3: -1.2186339203144456, 4: 2.0040228028902654, 5: 0.06492054976790573, 6: 1.3496210626657086, 7: 0.6941023037165261, 8: 1.137570761105878, 9: -1.4411621181849634, 10: -1.29019104865153, 11: -0.7944491401765816, 12: 1.8205741925071979, 13: 0.31232362090506616, 14: -0.2047113909538929, 15: -0.8750949833534518, 16: -2.2831577684563973, 17: 0.7221634315509293, 18: 0.9993321089642153, 19: 2.2412659643128556, 20: 0.9823631495038585, 21: -1.9247150573952405, 22: 3.023822685826464, 23: 2.191521057190392, 24: -3.5774382356946237, 25: 2.947988585615988, 26: 3.0173773227863903, 27: -0.13274757085036315, 28: -2.381307456298197, 29: -3.239141499822984, 30: 4.142987951894191, 31: -1.1663241619598859, 32: 1.9652418583786337, 33: -0.05611571489733791, 34: 1.364946993040347, 35: 0.6894403931173967, 36: 1.2495908568989487, 37: -1.5179533522502187, 38: -1.4271506978759714, 39: -0.8623305591875452, 40: 1.7715738318735692, 41: 0.004149794325313927, 42: -0.13486853961042033, 43: -0.9395801341815995, 44: -2.3268228862944556, 45: 0.7564232784588754, 46: 0.9210530358804201, 47: 2.1045160740062787, 48: 0.9327622080913098, 49: -1.9885780454563413, 50: 3.1211981027590507, 51: 2.2136432759296056, 52: -3.679395673584035, 53: 2.902469331651437, 54: 3.125263760489961, 55: 0.008230939886325395} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag23 0.1816020647301255 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag8 0.17547340034180575 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag42 -0.1588097040658141 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag49 0.1518006599284732 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag36 0.14322822490715392 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag7 0.14036484656996773 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag59 -0.12882401971544236 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag28 -0.12780525797119718 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag57 -0.12614962982418115 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_Lag45 0.12276677177049053 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 15.74946904182434 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_AR(64)_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 1268 entries, 0 to 1267 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 1268 non-null datetime64[ns] + 1 Signal 988 non-null float64 + 2 Signal_Forecast 1268 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 29.8 KB +None +Forecasts + [[Timestamp('2000-02-11 04:00:00') nan 4.705480880133687] + [Timestamp('2000-02-11 05:00:00') nan 4.414733345341099] + [Timestamp('2000-02-11 06:00:00') nan 3.3687516192082096] + [Timestamp('2000-02-11 07:00:00') nan 5.416099907923083] + [Timestamp('2000-02-11 08:00:00') nan 5.4368483826363185] + [Timestamp('2000-02-11 09:00:00') nan 7.673700786452203] + [Timestamp('2000-02-11 10:00:00') nan 5.701246046514442] + [Timestamp('2000-02-11 11:00:00') nan 3.998015861008371] + [Timestamp('2000-02-11 12:00:00') nan 4.664396345256742] + [Timestamp('2000-02-11 13:00:00') nan 4.846381750162863] + [Timestamp('2000-02-11 14:00:00') nan 7.743318693222477] + [Timestamp('2000-02-11 15:00:00') nan 7.948913756084668] + [Timestamp('2000-02-11 16:00:00') nan 8.100587381217608] + [Timestamp('2000-02-11 17:00:00') nan 2.6853842396930494] + [Timestamp('2000-02-11 18:00:00') nan 7.914337481976579] + [Timestamp('2000-02-11 19:00:00') nan 9.447900594165809] + [Timestamp('2000-02-11 20:00:00') nan 1.2973553651813619] + [Timestamp('2000-02-11 21:00:00') nan 5.5341288259298675] + [Timestamp('2000-02-11 22:00:00') nan 9.93995531050976] + [Timestamp('2000-02-11 23:00:00') nan 5.6547409105624995] + [Timestamp('2000-02-12 00:00:00') nan 4.764269431030901] + [Timestamp('2000-02-12 01:00:00') nan 2.5670399611044674] + [Timestamp('2000-02-12 02:00:00') nan 9.35803032380653] + [Timestamp('2000-02-12 03:00:00') nan 4.024949382787698] + [Timestamp('2000-02-12 04:00:00') nan 10.967291551896288] + [Timestamp('2000-02-12 05:00:00') nan 5.921794447679478] + [Timestamp('2000-02-12 06:00:00') nan 5.779091473199283] + [Timestamp('2000-02-12 07:00:00') nan 6.939466278091732] + [Timestamp('2000-02-12 08:00:00') nan 8.166787959142212] + [Timestamp('2000-02-12 09:00:00') nan 2.592435782268054] + [Timestamp('2000-02-12 10:00:00') nan 6.237215056672147] + [Timestamp('2000-02-12 11:00:00') nan 4.982866835601412] + [Timestamp('2000-02-12 12:00:00') nan 9.881142937258124] + [Timestamp('2000-02-12 13:00:00') nan 3.9029590022859058] + [Timestamp('2000-02-12 14:00:00') nan 7.369369089848817] + 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+INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 0.8199312686920166 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 1028 entries, 0 to 1027 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 1028 non-null datetime64[ns] + 1 Signal 988 non-null float64 + 2 Signal_Forecast 1028 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 24.2 KB +None +Forecasts + [[Timestamp('2000-02-11 04:00:00') nan 1.2572023237547123] + [Timestamp('2000-02-11 05:00:00') nan 3.7695832176441066] + [Timestamp('2000-02-11 06:00:00') nan 4.274049106186207] + [Timestamp('2000-02-11 07:00:00') nan 7.769774770256914] + [Timestamp('2000-02-11 08:00:00') nan 9.316027192438913] + [Timestamp('2000-02-11 09:00:00') nan 3.2999916932560254] + [Timestamp('2000-02-11 10:00:00') nan 7.2397087864089755] + [Timestamp('2000-02-11 11:00:00') nan 4.805000152534031] + [Timestamp('2000-02-11 12:00:00') nan 5.28269039399333] + [Timestamp('2000-02-11 13:00:00') nan 10.295148695287587] + [Timestamp('2000-02-11 14:00:00') nan 8.790687799720708] + [Timestamp('2000-02-11 15:00:00') nan 10.265851408011306] + [Timestamp('2000-02-11 16:00:00') nan 2.2602127922961777] + [Timestamp('2000-02-11 17:00:00') nan 4.271610915370116] + [Timestamp('2000-02-11 18:00:00') nan 5.260444755354369] + [Timestamp('2000-02-11 19:00:00') nan 10.281750637992117] + [Timestamp('2000-02-11 20:00:00') nan 9.81499520459733] + [Timestamp('2000-02-11 21:00:00') nan 2.775833222074845] + [Timestamp('2000-02-11 22:00:00') nan 3.790948515148756] + [Timestamp('2000-02-11 23:00:00') nan 6.804850984048037] + [Timestamp('2000-02-12 00:00:00') nan 1.2572023237547123] + [Timestamp('2000-02-12 01:00:00') nan 3.7695832176441066] + [Timestamp('2000-02-12 02:00:00') nan 4.274049106186207] + [Timestamp('2000-02-12 03:00:00') nan 7.769774770256914] + [Timestamp('2000-02-12 04:00:00') nan 9.316027192438913] + [Timestamp('2000-02-12 05:00:00') nan 3.2999916932560254] + [Timestamp('2000-02-12 06:00:00') nan 7.2397087864089755] + [Timestamp('2000-02-12 07:00:00') nan 4.805000152534031] + [Timestamp('2000-02-12 08:00:00') nan 5.28269039399333] + [Timestamp('2000-02-12 09:00:00') nan 10.295148695287587] + [Timestamp('2000-02-12 10:00:00') nan 8.790687799720708] + [Timestamp('2000-02-12 11:00:00') nan 10.265851408011306] + [Timestamp('2000-02-12 12:00:00') nan 2.2602127922961777] + [Timestamp('2000-02-12 13:00:00') nan 4.271610915370116] + [Timestamp('2000-02-12 14:00:00') nan 5.260444755354369] + [Timestamp('2000-02-12 15:00:00') nan 10.281750637992117] + [Timestamp('2000-02-12 16:00:00') nan 9.81499520459733] + [Timestamp('2000-02-12 17:00:00') nan 2.775833222074845] + [Timestamp('2000-02-12 18:00:00') nan 3.790948515148756] + [Timestamp('2000-02-12 19:00:00') nan 6.804850984048037]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 40, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-02-11 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.07913823183584726", + "MAPE": "0.0174", + "MASE": "0.0255", + "RMSE": "0.09628714474543222" + } + } +} + + + + + + +{"Date":{"988":"2000-02-11T04:00:00.000Z","989":"2000-02-11T05:00:00.000Z","990":"2000-02-11T06:00:00.000Z","991":"2000-02-11T07:00:00.000Z","992":"2000-02-11T08:00:00.000Z","993":"2000-02-11T09:00:00.000Z","994":"2000-02-11T10:00:00.000Z","995":"2000-02-11T11:00:00.000Z","996":"2000-02-11T12:00:00.000Z","997":"2000-02-11T13:00:00.000Z","998":"2000-02-11T14:00:00.000Z","999":"2000-02-11T15:00:00.000Z","1000":"2000-02-11T16:00:00.000Z","1001":"2000-02-11T17:00:00.000Z","1002":"2000-02-11T18:00:00.000Z","1003":"2000-02-11T19:00:00.000Z","1004":"2000-02-11T20:00:00.000Z","1005":"2000-02-11T21:00:00.000Z","1006":"2000-02-11T22:00:00.000Z","1007":"2000-02-11T23:00:00.000Z","1008":"2000-02-12T00:00:00.000Z","1009":"2000-02-12T01:00:00.000Z","1010":"2000-02-12T02:00:00.000Z","1011":"2000-02-12T03:00:00.000Z","1012":"2000-02-12T04:00:00.000Z","1013":"2000-02-12T05:00:00.000Z","1014":"2000-02-12T06:00:00.000Z","1015":"2000-02-12T07:00:00.000Z","1016":"2000-02-12T08:00:00.000Z","1017":"2000-02-12T09:00:00.000Z","1018":"2000-02-12T10:00:00.000Z","1019":"2000-02-12T11:00:00.000Z","1020":"2000-02-12T12:00:00.000Z","1021":"2000-02-12T13:00:00.000Z","1022":"2000-02-12T14:00:00.000Z","1023":"2000-02-12T15:00:00.000Z","1024":"2000-02-12T16:00:00.000Z","1025":"2000-02-12T17:00:00.000Z","1026":"2000-02-12T18:00:00.000Z","1027":"2000-02-12T19:00:00.000Z"},"Signal":{"988":null,"989":null,"990":null,"991":null,"992":null,"993":null,"994":null,"995":null,"996":null,"997":null,"998":null,"999":null,"1000":null,"1001":null,"1002":null,"1003":null,"1004":null,"1005":null,"1006":null,"1007":null,"1008":null,"1009":null,"1010":null,"1011":null,"1012":null,"1013":null,"1014":null,"1015":null,"1016":null,"1017":null,"1018":null,"1019":null,"1020":null,"1021":null,"1022":null,"1023":null,"1024":null,"1025":null,"1026":null,"1027":null},"Signal_Forecast":{"988":1.2572023238,"989":3.7695832176,"990":4.2740491062,"991":7.7697747703,"992":9.3160271924,"993":3.2999916933,"994":7.2397087864,"995":4.8050001525,"996":5.282690394,"997":10.2951486953,"998":8.7906877997,"999":10.265851408,"1000":2.2602127923,"1001":4.2716109154,"1002":5.2604447554,"1003":10.281750638,"1004":9.8149952046,"1005":2.7758332221,"1006":3.7909485151,"1007":6.804850984,"1008":1.2572023238,"1009":3.7695832176,"1010":4.2740491062,"1011":7.7697747703,"1012":9.3160271924,"1013":3.2999916933,"1014":7.2397087864,"1015":4.8050001525,"1016":5.282690394,"1017":10.2951486953,"1018":8.7906877997,"1019":10.265851408,"1020":2.2602127923,"1021":4.2716109154,"1022":5.2604447554,"1023":10.281750638,"1024":9.8149952046,"1025":2.7758332221,"1026":3.7909485151,"1027":6.804850984}} + + + +TEST_CYCLES_END 20 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_200.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_200.log new file mode 100644 index 000000000..7378790c4 --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_200.log @@ -0,0 +1,117 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 1000 200 +GENERATING_RANDOM_DATASET Signal_1000_H_0_constant_200_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 29.90403699874878 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-02-11T03:00:00.000000 TimeDelta= Horizon=400 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=988 Min=1.0 Max=11.127720983406524 Mean=6.061528911202131 StdDev=2.9365483552342058 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.127720983406524 Mean=6.061528911202131 StdDev=2.9365483552342058 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64)' [PolyTrend + Seasonal_Hour + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_PolyTrend' [PolyTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_PolyTrend_residue_Seasonal_Hour' [Seasonal_Hour] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.6351 MAPE_Forecast=0.6351 MAPE_Test=0.6351 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.4224 SMAPE_Forecast=0.4224 SMAPE_Test=0.4224 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6332 MASE_Forecast=0.6332 MASE_Test=0.6332 +INFO:pyaf.std:MODEL_L1 L1_Fit=2.264855049143297 L1_Forecast=2.264855049143297 L1_Test=2.264855049143297 +INFO:pyaf.std:MODEL_L2 L2_Fit=2.686397733480466 L2_Forecast=2.686397733480466 L2_Test=2.686397733480466 +INFO:pyaf.std:MODEL_COMPLEXITY 84 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:POLYNOMIAL_RIDGE_TREND PolyTrend (6.092693936122754, array([-0.05439979, 0.37442829, -0.51482954])) +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_PolyTrend_residue_Seasonal_Hour -0.053587827462947324 {0: 0.5135039986570074, 1: 0.2889937122294923, 2: 0.04181002625096708, 3: -0.2535764638452682, 4: 0.08388819746615717, 5: -0.6494775613001398, 6: 0.6632617816268436, 7: -0.3036857853656061, 8: 0.8424203753289667, 9: 0.3354649381132866, 10: -1.093609659956849, 11: 0.05867867074248867, 12: -0.59401389032947, 13: 0.5640476613874439, 14: -0.80768338084623, 15: -0.12641968824666172, 16: 1.360283640597161, 17: 0.10049373262614658, 18: 0.0007424814125274182, 19: -0.1323633830148605, 20: -0.05637193375615901, 21: 0.8661848877905136, 22: -0.5505352754155863, 23: -0.19781265815949922} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag42 -0.1597696997996459 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag57 0.13637146570437295 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag45 0.11285879658417258 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag16 0.10439087952219743 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag43 0.09781023051158562 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag61 -0.0891808299853972 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag39 -0.08856027245139707 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag52 0.08780568623581977 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag7 0.08036595284421805 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_PolyTrend_residue_Seasonal_Hour_residue_Lag3 0.07663465139373306 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 23.258859157562256 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + 'Date_Normalized_^2', 'Date_Normalized_^3', '_Signal_PolyTrend', + '_Signal_PolyTrend_residue', '_Signal_PolyTrend_residue_Seasonal_Hour', + '_Signal_PolyTrend_residue_Seasonal_Hour_residue', + '_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64)', + '_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64)_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 1388 entries, 0 to 1387 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 1388 non-null datetime64[ns] + 1 Signal 988 non-null float64 + 2 Signal_Forecast 1388 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 32.7 KB +None +Forecasts + [[Timestamp('2000-02-11 04:00:00') nan 5.749801541307053] + [Timestamp('2000-02-11 05:00:00') nan 5.317497448914866] + [Timestamp('2000-02-11 06:00:00') nan 7.250428573636562] + ... + [Timestamp('2000-02-27 17:00:00') nan 5.412874539923819] + [Timestamp('2000-02-27 18:00:00') nan 5.3085454541972] + [Timestamp('2000-02-27 19:00:00') nan 5.164706367194597]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 400, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-02-11 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 988 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_PolyTrend_residue_Seasonal_Hour_residue_AR(64)", + "Cycle": "Seasonal_Hour", + "Signal_Transoformation": "NoTransf", + "Trend": "PolyTrend" + }, + "Model_Performance": { + "COMPLEXITY": "84", + "MAE": "2.264855049143297", + "MAPE": "0.6351", + "MASE": "0.6332", + "RMSE": "2.686397733480466" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 200 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_260.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_260.log new file mode 100644 index 000000000..59b8417cb --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_260.log @@ -0,0 +1,117 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 1000 260 +GENERATING_RANDOM_DATASET Signal_1000_H_0_constant_260_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 35.474315881729126 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-02-11T03:00:00.000000 TimeDelta= Horizon=520 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=988 Min=1.0 Max=11.23086940855957 Mean=6.009670135167347 StdDev=2.8232972675989116 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.23086940855957 Mean=6.009670135167347 StdDev=2.8232972675989116 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)' [ConstantTrend + Seasonal_Hour + AR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour' [Seasonal_Hour] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.5329 MAPE_Forecast=0.5329 MAPE_Test=0.5329 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.3821 SMAPE_Forecast=0.3821 SMAPE_Test=0.3821 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6322 MASE_Forecast=0.6322 MASE_Test=0.6322 +INFO:pyaf.std:MODEL_L1 L1_Fit=2.0692780964287145 L1_Forecast=2.0692780964287145 L1_Test=2.0692780964287145 +INFO:pyaf.std:MODEL_L2 L2_Fit=2.496640489275047 L2_Forecast=2.496640489275047 L2_Test=2.496640489275047 +INFO:pyaf.std:MODEL_COMPLEXITY 68 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.009670135167347 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_Hour -0.07497078014016134 {0: 0.26559437802976893, 1: -0.1358041146306208, 2: 0.29006721834664084, 3: 0.16319541047581199, 4: 0.13111759072848628, 5: -0.030717977751162806, 6: -0.8194762168352376, 7: 0.003944948158731698, 8: 0.8776337965332637, 9: 0.902309130756854, 10: -0.8975141184095268, 11: -0.41894574806111073, 12: 1.0085034878003043, 13: -0.7715233212922517, 14: -0.7392603765903081, 15: -0.3906623166383447, 16: 0.007760918045494947, 17: -1.3395670949952336, 18: 0.8118680819773534, 19: 0.05986715131019604, 20: 0.0004352213169607211, 21: -1.5297531020337924, 22: 0.48383069500574827, 23: -0.33614974373418605} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag35 0.17670244388354978 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag60 -0.1300247717914604 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag17 0.12323899407878872 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag43 0.11285466930234694 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag27 0.10981447799423295 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag46 0.09843196376139042 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag47 0.098009582682531 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag41 0.09735656102722937 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag59 0.09658446820775757 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag2 -0.09495871679954387 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 29.073288679122925 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + '_Signal_ConstantTrend_residue_Seasonal_Hour', + '_Signal_ConstantTrend_residue_Seasonal_Hour_residue', + '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)', + '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 1508 entries, 0 to 1507 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 1508 non-null datetime64[ns] + 1 Signal 988 non-null float64 + 2 Signal_Forecast 1508 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 35.5 KB +None +Forecasts + [[Timestamp('2000-02-11 04:00:00') nan 5.936524924408065] + [Timestamp('2000-02-11 05:00:00') nan 4.30037551584978] + [Timestamp('2000-02-11 06:00:00') nan 4.79784946795424] + ... + [Timestamp('2000-03-03 17:00:00') nan 4.778831333084207] + [Timestamp('2000-03-03 18:00:00') nan 6.93058641324963] + [Timestamp('2000-03-03 19:00:00') nan 6.17804997966546]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 520, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-02-11 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 988 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)", + "Cycle": "Seasonal_Hour", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "68", + "MAE": "2.0692780964287145", + "MAPE": "0.5329", + "MASE": "0.6322", + "RMSE": "2.496640489275047" + } + } +} + + + + + + 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+INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.129434314394495 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Signal_ConstantTrend_residue_Seasonal_Hour -0.05411809100583653 {0: 0.705862274281416, 1: -0.889096644853069, 2: 1.2322741546297906, 3: -0.5356428989518487, 4: -0.3308284199646918, 5: 1.2498720205417344, 6: 0.5984410164457277, 7: -1.7748473744831683, 8: -0.3693828896382758, 9: -0.9542844013390246, 10: 1.1288045968942262, 11: -0.8427048135127082, 12: -0.7995680832547318, 13: 1.1247901567835497, 14: 0.6979054237927036, 15: -1.6705684658210762, 16: -0.34692385983863705, 17: -0.8417527829395075, 18: 1.0566991925521982, 19: -0.8993828582107897, 20: -0.4784615303019324, 21: 1.2051163310095294, 22: 0.6526931778791143, 23: -1.6893468227841018} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag20 -0.11377347264291385 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag60 -0.11339056840921344 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag22 -0.10783047407224254 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag46 -0.10255169000601302 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag8 -0.10002185921693016 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag37 -0.09302617988967929 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag5 0.0921247028231657 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag42 -0.09200622933442558 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag52 0.086401665864884 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag14 -0.08616574826915954 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 35.71287679672241 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + '_Signal_ConstantTrend_residue_Seasonal_Hour', + '_Signal_ConstantTrend_residue_Seasonal_Hour_residue', + '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)', + '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 1628 entries, 0 to 1627 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 1628 non-null datetime64[ns] + 1 Signal 988 non-null float64 + 2 Signal_Forecast 1628 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 38.3 KB +None +Forecasts + [[Timestamp('2000-02-11 04:00:00') nan 5.549760302992541] + [Timestamp('2000-02-11 05:00:00') nan 6.664369901347458] + [Timestamp('2000-02-11 06:00:00') nan 5.937507783696493] + ... + [Timestamp('2000-03-08 17:00:00') nan 5.394616394407617] + [Timestamp('2000-03-08 18:00:00') nan 7.292221708129488] + [Timestamp('2000-03-08 19:00:00') nan 5.335589918917693]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 640, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-02-11 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 988 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)", + "Cycle": "Seasonal_Hour", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "68", + "MAE": "2.1651369743107147", + "MAPE": "0.5395", + "MASE": "0.6631", + "RMSE": "2.6361900296891156" + } + } +} + + + + + + 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+INFO:pyaf.std:AR_MODEL_COEFF 9 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag57 -0.10000476340800166 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Signal_ConstantTrend_residue_Seasonal_Hour_residue_Lag28 -0.09437678617938017 +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 43.88278675079346 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + '_Signal_ConstantTrend_residue_Seasonal_Hour', + '_Signal_ConstantTrend_residue_Seasonal_Hour_residue', + '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)', + '_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 1748 entries, 0 to 1747 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 1748 non-null datetime64[ns] + 1 Signal 988 non-null float64 + 2 Signal_Forecast 1748 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 41.1 KB +None +Forecasts + [[Timestamp('2000-02-11 04:00:00') nan 2.5863299998345823] + [Timestamp('2000-02-11 05:00:00') nan 5.651673860872195] + [Timestamp('2000-02-11 06:00:00') nan 9.685212516606809] + ... + [Timestamp('2000-03-13 17:00:00') nan 6.700353017303312] + [Timestamp('2000-03-13 18:00:00') nan 5.001821265965972] + [Timestamp('2000-03-13 19:00:00') nan 6.206281614594271]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 760, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-02-11 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 988 + }, + "Model": { + "AR_Model": "AR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_Seasonal_Hour_residue_AR(64)", + "Cycle": "Seasonal_Hour", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "68", + "MAE": "2.0684210285443076", + "MAPE": "0.5789", + "MASE": "0.6221", + "RMSE": "2.5432882118773326" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 380 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_440.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_440.log new file mode 100644 index 000000000..82aecb49a --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_1000_440.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 1000 440 +GENERATING_RANDOM_DATASET Signal_1000_H_0_constant_440_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 25.969910860061646 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-02-11T03:00:00.000000 TimeDelta= Horizon=880 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=988 Min=1.0 Max=11.080609431179427 Mean=6.0769653866727085 StdDev=2.8855776145281236 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_Signal' Min=-9.136182927575899 Max=9.66556672639352 Mean=-0.002476615104449511 StdDev=4.097762660329744 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' +INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR' [ConstantTrend + NoCycle + NoAR] +INFO:pyaf.std:TREND_DETAIL 'Diff_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL 'Diff_Signal_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL 'Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.5509 MAPE_Forecast=0.5509 MAPE_Test=0.5509 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.5276 SMAPE_Forecast=0.5276 SMAPE_Test=0.5276 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8521 MASE_Forecast=0.8521 MASE_Test=0.8521 +INFO:pyaf.std:MODEL_L1 L1_Fit=2.8552076759011102 L1_Forecast=2.8552076759011102 L1_Test=2.8552076759011102 +INFO:pyaf.std:MODEL_L2 L2_Fit=3.4456462923674387 L2_Forecast=3.4456462923674387 L2_Test=3.4456462923674387 +INFO:pyaf.std:MODEL_COMPLEXITY 32 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 5.576036269559265 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend -0.002476615104449511 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_Signal_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 15.469907522201538 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', 'Diff_Signal', + 'Diff_Signal_ConstantTrend', 'Diff_Signal_ConstantTrend_residue', + 'Diff_Signal_ConstantTrend_residue_zeroCycle', + 'Diff_Signal_ConstantTrend_residue_zeroCycle_residue', + 'Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR', + 'Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR_residue', + 'Diff_Signal_Trend', 'Diff_Signal_Trend_residue', 'Diff_Signal_Cycle', + 'Diff_Signal_Cycle_residue', 'Diff_Signal_AR', 'Diff_Signal_AR_residue', + 'Diff_Signal_TransformedForecast', 'Signal_Forecast', + 'Diff_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 1868 entries, 0 to 1867 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 1868 non-null datetime64[ns] + 1 Signal 988 non-null float64 + 2 Signal_Forecast 1868 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 43.9 KB +None +Forecasts + [[Timestamp('2000-02-11 04:00:00') nan 3.1266639312587308] + [Timestamp('2000-02-11 05:00:00') nan 3.1241873161542815] + [Timestamp('2000-02-11 06:00:00') nan 3.121710701049832] + ... + [Timestamp('2000-03-18 17:00:00') nan 0.9546724846565855] + [Timestamp('2000-03-18 18:00:00') nan 0.9521958695521358] + [Timestamp('2000-03-18 19:00:00') nan 0.949719254447686]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 880, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2000-02-11 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "Diff_Signal_ConstantTrend_residue_zeroCycle_residue_NoAR", + "Cycle": "NoCycle", + "Signal_Transoformation": "Difference", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "32", + "MAE": "2.8552076759011102", + "MAPE": "0.5509", + "MASE": "0.8521", + "RMSE": "3.4456462923674387" + } + } +} + + + + + + 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-2.5981062230778074, 24: -3.165718250202101, 25: -0.011617013401436171, 26: -0.9947822028205486, 27: 3.2351209301667927, 28: -3.7953877835970444, 29: 2.1394695515617057, 30: -0.9362162934355682, 31: -1.1337115599622662, 32: 4.349255972586262, 33: -2.0462626542698747, 34: -0.6357745363574758, 35: 4.430982035236874, 36: 1.9079442422552724, 37: -1.4925411781047324, 38: -0.6993240640156713, 39: -4.976877208008457, 40: -5.008098188298199, 41: -0.4538886842709955, 42: 1.62699051283013, 43: -4.344591242331989, 44: -0.2021673362321299, 45: -2.7995285798232326, 46: 4.883918979601747, 47: -4.485557683585879, 48: 0.32479547194510383, 49: 2.2927038563680586, 50: -1.0643404468727384, 51: -4.8490546754646, 52: 3.214054166997135, 53: 0.22939436250418632, 54: 2.1354498909104134, 55: -0.5319992567393395, 56: -3.594650182561656, 57: 0.7663246085638695, 58: -4.886437769271042, 59: -3.261216464882107, 60: 1.634229148579902, 61: -3.4944980918769075, 62: 0.29360234438481214, 63: 4.414556190555829, 64: 3.5346683318038608, 65: -4.296149364009689, 66: 3.5909557431053987, 67: 0.9788714571703694, 68: -4.642059422863957, 69: 4.505455613475142, 70: 1.4822928988448067, 71: 4.84744371364188, 72: -3.094900678233266, 73: -2.468837216862819, 74: 2.345922384458194, 75: -2.103999332018932, 76: 4.941917645112604, 77: -3.299373240613394, 78: 1.0208585623458015, 79: 1.1849860765066857} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 3.0173914432525635 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 1148 entries, 0 to 1147 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 1148 non-null datetime64[ns] + 1 Signal 988 non-null float64 + 2 Signal_Forecast 1148 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 27.0 KB +None +Forecasts + [[Timestamp('2000-02-11 04:00:00') nan 2.465720930190749] + [Timestamp('2000-02-11 05:00:00') nan 8.400578265349498] + [Timestamp('2000-02-11 06:00:00') nan 5.324892420352225] + [Timestamp('2000-02-11 07:00:00') nan 5.127397153825527] + [Timestamp('2000-02-11 08:00:00') nan 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01:00:00') nan 8.553812570155852] + [Timestamp('2000-02-12 02:00:00') nan 5.196768266915055] + [Timestamp('2000-02-12 03:00:00') nan 1.4120540383231939] + [Timestamp('2000-02-12 04:00:00') nan 9.475162880784929] + [Timestamp('2000-02-12 05:00:00') nan 6.490503076291979] + [Timestamp('2000-02-12 06:00:00') nan 8.396558604698207] + [Timestamp('2000-02-12 07:00:00') nan 5.7291094570484535] + [Timestamp('2000-02-12 08:00:00') nan 2.6664585312261373] + [Timestamp('2000-02-12 09:00:00') nan 7.027433322351663] + [Timestamp('2000-02-12 10:00:00') nan 1.374670944516751] + [Timestamp('2000-02-12 11:00:00') nan 2.9998922489056863] + [Timestamp('2000-02-12 12:00:00') nan 7.895337862367695] + [Timestamp('2000-02-12 13:00:00') nan 2.766610621910886] + [Timestamp('2000-02-12 14:00:00') nan 6.554711058172606] + [Timestamp('2000-02-12 15:00:00') nan 10.675664904343623] + [Timestamp('2000-02-12 16:00:00') nan 9.795777045591654] + [Timestamp('2000-02-12 17:00:00') nan 1.9649593497781046] + 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1.1282673999484407, 65: -1.5280988828109, 66: -1.4519222419087816, 67: -0.01904419034675975, 68: 1.8314462472148891, 69: 1.764878397192307, 70: -4.951400201192475, 71: 3.1285354329579578, 72: -2.359129080088051, 73: -1.5247017383504788, 74: 1.66828456061367, 75: 4.417080847568664, 76: 2.0268086646445527, 77: -1.930140855872506, 78: 3.0274540663644425, 79: 4.133546154609751, 80: -4.9678136590239665, 81: 4.911353528427029, 82: 3.1897040852750367, 83: -1.889038511939141, 84: 4.098623452008547, 85: -3.327415174488649, 86: 4.319271346271841, 87: 3.7089144479991143, 88: 2.061038400924594, 89: -0.5472182007493549, 90: 3.8422690793135965, 91: 0.9139312428357274, 92: -1.3841471180421232, 93: -3.960513916029557, 94: -2.0105523698280834, 95: -0.8205672670728683, 96: -2.3483164635575218, 97: -4.237685200377189, 98: 1.1745658903126808, 99: -1.8850783443461667, 100: 2.486676973031133, 101: -4.165586965870198, 102: -4.772701523870274, 103: -1.30485043816351, 104: 0.77775199810978, 105: -2.6481275301415574, 106: 4.187593433890588, 107: -2.2168483950430624, 108: -3.597993385688392, 109: -1.9483014931061868, 110: 3.2707146121648742, 111: 0.5582756865276242, 112: -2.810684664880283, 113: -3.2331950868707953, 114: 4.000394009829496, 115: -4.794896047625911, 116: -4.733461737416402, 117: 1.7947725223036857, 118: 2.6984978937360475, 119: -4.0026963644867095, 120: 3.057637587638575, 121: -2.1113792378364824, 122: 0.18460229125929573, 123: -3.586086779406564, 124: 1.8402506441527615, 125: 0.22426906556686, 126: -0.16262242261450766, 127: -0.5940834660561203, 128: -3.972536948554516, 129: 1.9134003628858816, 130: -4.644743901226818, 131: 4.994222861997646, 132: 1.2136819805583814, 133: 3.684028741834262, 134: 2.8636011422122367, 135: 0.39475202622918104, 136: -3.827861143563613, 137: 2.9028961496491563, 138: -3.1527646004979926, 139: 3.7052757819501503} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 9.394283771514893 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 11268 entries, 0 to 11267 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 11268 non-null datetime64[ns] + 1 Signal 10988 non-null float64 + 2 Signal_Forecast 11268 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 264.2 KB +None +Forecasts + [[Timestamp('2001-04-02 20:00:00') nan 8.039833240285077] + [Timestamp('2001-04-02 21:00:00') nan 7.973265390262495] + [Timestamp('2001-04-02 22:00:00') nan 1.2569867918777131] + [Timestamp('2001-04-02 23:00:00') nan 9.336922426028146] + [Timestamp('2001-04-03 00:00:00') nan 3.8492579129821367] + [Timestamp('2001-04-03 01:00:00') nan 4.683685254719709] + [Timestamp('2001-04-03 02:00:00') nan 7.876671553683858] + [Timestamp('2001-04-03 03:00:00') nan 10.625467840638851] + [Timestamp('2001-04-03 04:00:00') nan 8.23519565771474] + [Timestamp('2001-04-03 05:00:00') nan 4.278246137197682] + [Timestamp('2001-04-03 06:00:00') nan 9.23584105943463] + [Timestamp('2001-04-03 07:00:00') nan 10.341933147679939] + [Timestamp('2001-04-03 08:00:00') nan 1.2405733340462213] + [Timestamp('2001-04-03 09:00:00') nan 11.119740521497217] + [Timestamp('2001-04-03 10:00:00') nan 9.398091078345225] + [Timestamp('2001-04-03 11:00:00') nan 4.319348481131047] + [Timestamp('2001-04-03 12:00:00') nan 10.307010445078735] + [Timestamp('2001-04-03 13:00:00') nan 2.8809718185815387] + [Timestamp('2001-04-03 14:00:00') nan 10.527658339342029] + [Timestamp('2001-04-03 15:00:00') nan 9.917301441069302] + [Timestamp('2001-04-03 16:00:00') nan 8.269425393994782] + [Timestamp('2001-04-03 17:00:00') nan 5.661168792320833] + [Timestamp('2001-04-03 18:00:00') nan 10.050656072383784] + [Timestamp('2001-04-03 19:00:00') nan 7.122318235905915] + [Timestamp('2001-04-03 20:00:00') nan 4.824239875028065] + [Timestamp('2001-04-03 21:00:00') nan 2.247873077040631] + [Timestamp('2001-04-03 22:00:00') nan 4.197834623242104] + [Timestamp('2001-04-03 23:00:00') nan 5.3878197259973195] + [Timestamp('2001-04-04 00:00:00') nan 3.860070529512666] + [Timestamp('2001-04-04 01:00:00') nan 1.9707017926929984] + [Timestamp('2001-04-04 02:00:00') nan 7.382952883382869] + [Timestamp('2001-04-04 03:00:00') nan 4.323308648724021] + [Timestamp('2001-04-04 04:00:00') nan 8.695063966101321] + [Timestamp('2001-04-04 05:00:00') nan 2.0428000271999895] + [Timestamp('2001-04-04 06:00:00') nan 1.435685469199914] + [Timestamp('2001-04-04 07:00:00') nan 4.903536554906678] + [Timestamp('2001-04-04 08:00:00') nan 6.986138991179968] + [Timestamp('2001-04-04 09:00:00') nan 3.5602594629286304] + [Timestamp('2001-04-04 10:00:00') nan 10.395980426960776] + [Timestamp('2001-04-04 11:00:00') nan 3.9915385980271254] + [Timestamp('2001-04-04 12:00:00') nan 2.6103936073817957] + [Timestamp('2001-04-04 13:00:00') nan 4.260085499964001] + [Timestamp('2001-04-04 14:00:00') nan 9.479101605235062] + [Timestamp('2001-04-04 15:00:00') nan 6.766662679597812] + [Timestamp('2001-04-04 16:00:00') nan 3.397702328189905] + [Timestamp('2001-04-04 17:00:00') nan 2.9751919061993926] + [Timestamp('2001-04-04 18:00:00') nan 10.208781002899684] + 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11.202609855067834] + [Timestamp('2001-04-05 12:00:00') nan 7.422068973628569] + [Timestamp('2001-04-05 13:00:00') nan 9.89241573490445] + [Timestamp('2001-04-05 14:00:00') nan 9.071988135282425] + [Timestamp('2001-04-05 15:00:00') nan 6.603139019299369] + [Timestamp('2001-04-05 16:00:00') nan 2.380525849506575] + [Timestamp('2001-04-05 17:00:00') nan 9.111283142719344] + [Timestamp('2001-04-05 18:00:00') nan 3.0556223925721953] + [Timestamp('2001-04-05 19:00:00') nan 9.913662775020338] + [Timestamp('2001-04-05 20:00:00') nan 8.652133525496001] + [Timestamp('2001-04-05 21:00:00') nan 1.873410653287884] + [Timestamp('2001-04-05 22:00:00') nan 2.7385289939227455] + [Timestamp('2001-04-05 23:00:00') nan 3.8373715804745343] + [Timestamp('2001-04-06 00:00:00') nan 7.481244589003368] + [Timestamp('2001-04-06 01:00:00') nan 6.24630826454033] + [Timestamp('2001-04-06 02:00:00') nan 7.5601756965522515] + [Timestamp('2001-04-06 03:00:00') nan 5.389381155724298] + [Timestamp('2001-04-06 04:00:00') nan 4.048399365919289] + [Timestamp('2001-04-06 05:00:00') nan 7.462369817524268] + [Timestamp('2001-04-06 06:00:00') nan 7.541020012890935] + [Timestamp('2001-04-06 07:00:00') nan 7.020282458826859] + [Timestamp('2001-04-06 08:00:00') nan 3.0465910603468322] + [Timestamp('2001-04-06 09:00:00') nan 6.745989313975083] + [Timestamp('2001-04-06 10:00:00') nan 6.407857730281428] + [Timestamp('2001-04-06 11:00:00') nan 1.893794213495684] + [Timestamp('2001-04-06 12:00:00') nan 9.489130385175597] + [Timestamp('2001-04-06 13:00:00') nan 6.909413918641235] + [Timestamp('2001-04-06 14:00:00') nan 7.137492661343967] + [Timestamp('2001-04-06 15:00:00') nan 8.323838141533345] + [Timestamp('2001-04-06 16:00:00') nan 3.318999087897143] + [Timestamp('2001-04-06 17:00:00') nan 3.5429115312167814] + [Timestamp('2001-04-06 18:00:00') nan 1.9060608853956547] + [Timestamp('2001-04-06 19:00:00') nan 10.322996444001554] + [Timestamp('2001-04-06 20:00:00') nan 3.5425219727687294] + 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1.298749264300354] + [Timestamp('2001-04-07 14:00:00') nan 5.908582176210142] + [Timestamp('2001-04-07 15:00:00') nan 5.334030245808891] + [Timestamp('2001-04-07 16:00:00') nan 3.770391932232634] + [Timestamp('2001-04-07 17:00:00') nan 8.534756329536606] + [Timestamp('2001-04-07 18:00:00') nan 9.753357195295706] + [Timestamp('2001-04-07 19:00:00') nan 2.047315788891116] + [Timestamp('2001-04-07 20:00:00') nan 7.136156984840228] + [Timestamp('2001-04-07 21:00:00') nan 7.754181115301823] + [Timestamp('2001-04-07 22:00:00') nan 10.409338516722489] + [Timestamp('2001-04-07 23:00:00') nan 8.330701590006264] + [Timestamp('2001-04-08 00:00:00') nan 5.040008657972875] + [Timestamp('2001-04-08 01:00:00') nan 9.892989816874053] + [Timestamp('2001-04-08 02:00:00') nan 7.248841407769629] + [Timestamp('2001-04-08 03:00:00') nan 6.109058288849283] + [Timestamp('2001-04-08 04:00:00') nan 1.7059797437721738] + [Timestamp('2001-04-08 05:00:00') nan 4.620531085871242] + [Timestamp('2001-04-08 06:00:00') nan 10.33565937960801] + [Timestamp('2001-04-08 07:00:00') nan 10.623791648227183] + [Timestamp('2001-04-08 08:00:00') nan 8.390927605064437] + [Timestamp('2001-04-08 09:00:00') nan 6.795140375987695] + [Timestamp('2001-04-08 10:00:00') nan 2.896945309171784] + [Timestamp('2001-04-08 11:00:00') nan 9.616818940787965] + [Timestamp('2001-04-08 12:00:00') nan 7.3366543930186285] + [Timestamp('2001-04-08 13:00:00') nan 4.680288110259288] + [Timestamp('2001-04-08 14:00:00') nan 4.756464751161406] + [Timestamp('2001-04-08 15:00:00') nan 6.189342802723428] + [Timestamp('2001-04-08 16:00:00') nan 8.039833240285077] + [Timestamp('2001-04-08 17:00:00') nan 7.973265390262495] + [Timestamp('2001-04-08 18:00:00') nan 1.2569867918777131] + [Timestamp('2001-04-08 19:00:00') nan 9.336922426028146] + [Timestamp('2001-04-08 20:00:00') nan 3.8492579129821367] + [Timestamp('2001-04-08 21:00:00') nan 4.683685254719709] + [Timestamp('2001-04-08 22:00:00') nan 7.876671553683858] + [Timestamp('2001-04-08 23:00:00') nan 10.625467840638851] + [Timestamp('2001-04-09 00:00:00') nan 8.23519565771474] + [Timestamp('2001-04-09 01:00:00') nan 4.278246137197682] + [Timestamp('2001-04-09 02:00:00') nan 9.23584105943463] + [Timestamp('2001-04-09 03:00:00') nan 10.341933147679939] + [Timestamp('2001-04-09 04:00:00') nan 1.2405733340462213] + [Timestamp('2001-04-09 05:00:00') nan 11.119740521497217] + [Timestamp('2001-04-09 06:00:00') nan 9.398091078345225] + [Timestamp('2001-04-09 07:00:00') nan 4.319348481131047] + [Timestamp('2001-04-09 08:00:00') nan 10.307010445078735] + [Timestamp('2001-04-09 09:00:00') nan 2.8809718185815387] + [Timestamp('2001-04-09 10:00:00') nan 10.527658339342029] + [Timestamp('2001-04-09 11:00:00') nan 9.917301441069302] + [Timestamp('2001-04-09 12:00:00') nan 8.269425393994782] + [Timestamp('2001-04-09 13:00:00') nan 5.661168792320833] + [Timestamp('2001-04-09 14:00:00') nan 10.050656072383784] + [Timestamp('2001-04-09 15:00:00') nan 7.122318235905915] + [Timestamp('2001-04-09 16:00:00') nan 4.824239875028065] + [Timestamp('2001-04-09 17:00:00') nan 2.247873077040631] + [Timestamp('2001-04-09 18:00:00') nan 4.197834623242104] + [Timestamp('2001-04-09 19:00:00') nan 5.3878197259973195] + [Timestamp('2001-04-09 20:00:00') nan 3.860070529512666] + [Timestamp('2001-04-09 21:00:00') nan 1.9707017926929984] + [Timestamp('2001-04-09 22:00:00') nan 7.382952883382869] + [Timestamp('2001-04-09 23:00:00') nan 4.323308648724021] + [Timestamp('2001-04-10 00:00:00') nan 8.695063966101321] + [Timestamp('2001-04-10 01:00:00') nan 2.0428000271999895] + [Timestamp('2001-04-10 02:00:00') nan 1.435685469199914] + [Timestamp('2001-04-10 03:00:00') nan 4.903536554906678] + [Timestamp('2001-04-10 04:00:00') nan 6.986138991179968] + [Timestamp('2001-04-10 05:00:00') nan 3.5602594629286304] + [Timestamp('2001-04-10 06:00:00') nan 10.395980426960776] + [Timestamp('2001-04-10 07:00:00') nan 3.9915385980271254] + [Timestamp('2001-04-10 08:00:00') nan 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+INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 2.2755300998687744 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 11028 entries, 0 to 11027 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 11028 non-null datetime64[ns] + 1 Signal 10988 non-null float64 + 2 Signal_Forecast 11028 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 258.6 KB +None +Forecasts + [[Timestamp('2001-04-02 20:00:00') nan 1.2801818642434295] + [Timestamp('2001-04-02 21:00:00') nan 3.7758145023765137] + [Timestamp('2001-04-02 22:00:00') nan 4.27830186042392] + [Timestamp('2001-04-02 23:00:00') nan 7.772878365421665] + [Timestamp('2001-04-03 00:00:00') nan 9.28097881648262] + [Timestamp('2001-04-03 01:00:00') nan 3.2864089964761307] + [Timestamp('2001-04-03 02:00:00') nan 7.268236396414212] + [Timestamp('2001-04-03 03:00:00') nan 4.786197987371677] + [Timestamp('2001-04-03 04:00:00') nan 5.281828295009651] + [Timestamp('2001-04-03 05:00:00') nan 10.286490984940725] + [Timestamp('2001-04-03 06:00:00') nan 8.778119176593812] + [Timestamp('2001-04-03 07:00:00') nan 10.279555287398527] + [Timestamp('2001-04-03 08:00:00') nan 2.2773575977726903] + [Timestamp('2001-04-03 09:00:00') nan 4.271748190366056] + [Timestamp('2001-04-03 10:00:00') nan 5.271972647011233] + [Timestamp('2001-04-03 11:00:00') nan 10.279192398594912] + [Timestamp('2001-04-03 12:00:00') nan 9.780452021063056] + [Timestamp('2001-04-03 13:00:00') nan 2.7775942980165187] + [Timestamp('2001-04-03 14:00:00') nan 3.774213707182981] + [Timestamp('2001-04-03 15:00:00') nan 6.785856057911959] + [Timestamp('2001-04-03 16:00:00') nan 1.2801818642434295] + [Timestamp('2001-04-03 17:00:00') nan 3.7758145023765137] + [Timestamp('2001-04-03 18:00:00') nan 4.27830186042392] + [Timestamp('2001-04-03 19:00:00') nan 7.772878365421665] + [Timestamp('2001-04-03 20:00:00') nan 9.28097881648262] + [Timestamp('2001-04-03 21:00:00') nan 3.2864089964761307] + [Timestamp('2001-04-03 22:00:00') nan 7.268236396414212] + [Timestamp('2001-04-03 23:00:00') nan 4.786197987371677] + [Timestamp('2001-04-04 00:00:00') nan 5.281828295009651] + [Timestamp('2001-04-04 01:00:00') nan 10.286490984940725] + [Timestamp('2001-04-04 02:00:00') nan 8.778119176593812] + [Timestamp('2001-04-04 03:00:00') nan 10.279555287398527] + [Timestamp('2001-04-04 04:00:00') nan 2.2773575977726903] + [Timestamp('2001-04-04 05:00:00') nan 4.271748190366056] + [Timestamp('2001-04-04 06:00:00') nan 5.271972647011233] + [Timestamp('2001-04-04 07:00:00') nan 10.279192398594912] + [Timestamp('2001-04-04 08:00:00') nan 9.780452021063056] + [Timestamp('2001-04-04 09:00:00') nan 2.7775942980165187] + [Timestamp('2001-04-04 10:00:00') nan 3.774213707182981] + [Timestamp('2001-04-04 11:00:00') nan 6.785856057911959]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 40, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-04-02 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 10988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08063108059648652", + "MAPE": "0.0181", + "MASE": "0.0261", + "RMSE": "0.10060570344360589" + } + } +} + + + + + + +{"Date":{"10988":"2001-04-02T20:00:00.000Z","10989":"2001-04-02T21:00:00.000Z","10990":"2001-04-02T22:00:00.000Z","10991":"2001-04-02T23:00:00.000Z","10992":"2001-04-03T00:00:00.000Z","10993":"2001-04-03T01:00:00.000Z","10994":"2001-04-03T02:00:00.000Z","10995":"2001-04-03T03:00:00.000Z","10996":"2001-04-03T04:00:00.000Z","10997":"2001-04-03T05:00:00.000Z","10998":"2001-04-03T06:00:00.000Z","10999":"2001-04-03T07:00:00.000Z","11000":"2001-04-03T08:00:00.000Z","11001":"2001-04-03T09:00:00.000Z","11002":"2001-04-03T10:00:00.000Z","11003":"2001-04-03T11:00:00.000Z","11004":"2001-04-03T12:00:00.000Z","11005":"2001-04-03T13:00:00.000Z","11006":"2001-04-03T14:00:00.000Z","11007":"2001-04-03T15:00:00.000Z","11008":"2001-04-03T16:00:00.000Z","11009":"2001-04-03T17:00:00.000Z","11010":"2001-04-03T18:00:00.000Z","11011":"2001-04-03T19:00:00.000Z","11012":"2001-04-03T20:00:00.000Z","11013":"2001-04-03T21:00:00.000Z","11014":"2001-04-03T22:00:00.000Z","11015":"2001-04-03T23:00:00.000Z","11016":"2001-04-04T00:00:00.000Z","11017":"2001-04-04T01:00:00.000Z","11018":"2001-04-04T02:00:00.000Z","11019":"2001-04-04T03:00:00.000Z","11020":"2001-04-04T04:00:00.000Z","11021":"2001-04-04T05:00:00.000Z","11022":"2001-04-04T06:00:00.000Z","11023":"2001-04-04T07:00:00.000Z","11024":"2001-04-04T08:00:00.000Z","11025":"2001-04-04T09:00:00.000Z","11026":"2001-04-04T10:00:00.000Z","11027":"2001-04-04T11:00:00.000Z"},"Signal":{"10988":null,"10989":null,"10990":null,"10991":null,"10992":null,"10993":null,"10994":null,"10995":null,"10996":null,"10997":null,"10998":null,"10999":null,"11000":null,"11001":null,"11002":null,"11003":null,"11004":null,"11005":null,"11006":null,"11007":null,"11008":null,"11009":null,"11010":null,"11011":null,"11012":null,"11013":null,"11014":null,"11015":null,"11016":null,"11017":null,"11018":null,"11019":null,"11020":null,"11021":null,"11022":null,"11023":null,"11024":null,"11025":null,"11026":null,"11027":null},"Signal_Forecast":{"10988":1.2801818642,"10989":3.7758145024,"10990":4.2783018604,"10991":7.7728783654,"10992":9.2809788165,"10993":3.2864089965,"10994":7.2682363964,"10995":4.7861979874,"10996":5.281828295,"10997":10.2864909849,"10998":8.7781191766,"10999":10.2795552874,"11000":2.2773575978,"11001":4.2717481904,"11002":5.271972647,"11003":10.2791923986,"11004":9.7804520211,"11005":2.777594298,"11006":3.7742137072,"11007":6.7858560579,"11008":1.2801818642,"11009":3.7758145024,"11010":4.2783018604,"11011":7.7728783654,"11012":9.2809788165,"11013":3.2864089965,"11014":7.2682363964,"11015":4.7861979874,"11016":5.281828295,"11017":10.2864909849,"11018":8.7781191766,"11019":10.2795552874,"11020":2.2773575978,"11021":4.2717481904,"11022":5.271972647,"11023":10.2791923986,"11024":9.7804520211,"11025":2.777594298,"11026":3.7742137072,"11027":6.7858560579}} + + + +TEST_CYCLES_END 20 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_200.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_200.log new file mode 100644 index 000000000..5fadfb333 --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_200.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 11000 200 +GENERATING_RANDOM_DATASET Signal_11000_H_0_constant_200_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 79.62325406074524 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-12-18T21:00:00.000000 TimeDelta= Horizon=400 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10988 Min=1.0 Max=11.359446481082374 Mean=6.224511430583995 StdDev=2.9385148604176465 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.359446481082374 Mean=6.224511430583995 StdDev=2.9385148604176465 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0175 MAPE_Forecast=0.0182 MAPE_Test=0.0175 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0175 SMAPE_Forecast=0.0182 SMAPE_Test=0.0175 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0219 MASE_Forecast=0.0225 MASE_Test=0.0219 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07829714131906344 L1_Forecast=0.08034399661640988 L1_Test=0.07871612075934405 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.0995204726463601 L2_Forecast=0.10105696821359945 L2_Test=0.09983912128439594 +INFO:pyaf.std:MODEL_COMPLEXITY 8 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185: 4.776945730414362, 186: 1.9534821628942227, 187: -0.6604322538279699, 188: 1.049136897280698, 189: 0.4564372857519752, 190: -1.2462577966536577, 191: 4.193005500489038, 192: -4.196073788795208, 193: 2.5761603705894114, 194: 2.8636779656548272, 195: 2.4859164600099115, 196: 0.5141810025073399, 197: -3.7324407696213173, 198: 4.395463994140308, 199: 1.0717414773499812} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 12.830541610717773 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 11388 entries, 0 to 11387 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 11388 non-null datetime64[ns] + 1 Signal 10988 non-null float64 + 2 Signal_Forecast 11388 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 267.0 KB +None +Forecasts + [[Timestamp('2001-04-02 20:00:00') nan 7.273521557358627] + [Timestamp('2001-04-02 21:00:00') nan 6.680821945829904] + [Timestamp('2001-04-02 22:00:00') nan 4.978126863424271] + ... + [Timestamp('2001-04-19 09:00:00') nan 11.001330390492292] + [Timestamp('2001-04-19 10:00:00') nan 8.177866822972153] + [Timestamp('2001-04-19 11:00:00') nan 5.563952406249959]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 400, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-04-02 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 10988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08034399661640988", + "MAPE": "0.0182", + "MASE": "0.0225", + "RMSE": "0.10105696821359945" + } + } +} + + + + + + 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+INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 17.27975821495056 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 11508 entries, 0 to 11507 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 11508 non-null datetime64[ns] + 1 Signal 10988 non-null float64 + 2 Signal_Forecast 11508 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 269.8 KB +None +Forecasts + [[Timestamp('2001-04-02 20:00:00') nan 5.00662134954621] + [Timestamp('2001-04-02 21:00:00') nan 2.810680864912885] + [Timestamp('2001-04-02 22:00:00') nan 9.000270722630386] + ... + [Timestamp('2001-04-24 09:00:00') nan 4.895025038951182] + [Timestamp('2001-04-24 10:00:00') nan 11.120585591635923] + [Timestamp('2001-04-24 11:00:00') nan 8.10280596636142]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 520, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-04-02 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 10988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08065064130237609", + "MAPE": "0.0177", + "MASE": "0.0247", + "RMSE": "0.10116598859472481" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 260 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_320.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_320.log new file mode 100644 index 000000000..1d71a1811 --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_320.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 11000 320 +GENERATING_RANDOM_DATASET Signal_11000_H_0_constant_320_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 104.25743341445923 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-12-10T21:00:00.000000 TimeDelta= Horizon=640 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10988 Min=1.0 Max=11.28405367003274 Mean=6.228408437329798 StdDev=2.885267200841129 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 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-4.87572030339104, 304: 3.808525824792734, 305: -2.7388330920320185, 306: 4.593524919957162, 307: 1.8050348662957854, 308: 4.573453259913237, 309: 2.0551460567419264, 310: 3.4888830036266247, 311: 4.037348836184692, 312: -3.4014792912592577, 313: -0.02880980973969116, 314: 4.8079919977941055, 315: 4.7244298565266005, 316: 3.5286481394155826, 317: -3.362983188267938, 318: 4.535016928619998, 319: -2.0543084662126354} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 19.90350914001465 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 11628 entries, 0 to 11627 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 11628 non-null datetime64[ns] + 1 Signal 10988 non-null float64 + 2 Signal_Forecast 11628 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 272.7 KB +None +Forecasts + [[Timestamp('2001-04-02 20:00:00') nan 2.5305322432342328] + [Timestamp('2001-04-02 21:00:00') nan 1.5573441832345276] + [Timestamp('2001-04-02 22:00:00') nan 9.287345112642905] + ... + [Timestamp('2001-04-29 09:00:00') nan 2.3453733850516247] + [Timestamp('2001-04-29 10:00:00') nan 9.514914430343255] + [Timestamp('2001-04-29 11:00:00') nan 3.9254125137916205]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 640, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-04-02 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 10988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08140303463563925", + "MAPE": "0.0177", + "MASE": "0.0249", + "RMSE": "0.1017377551623725" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 320 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_380.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_380.log new file mode 100644 index 000000000..649d4a0b1 --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_380.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 11000 380 +GENERATING_RANDOM_DATASET Signal_11000_H_0_constant_380_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 109.9379243850708 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-12-06T21:00:00.000000 TimeDelta= Horizon=760 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10988 Min=1.0 Max=11.36278775328616 Mean=6.251486804151526 StdDev=2.906781613229828 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 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+INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.250898726374005 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 380 0.016205633945490128 {0: 0.10398990685805165, 1: 4.393953126734281, 2: -4.820135205009587, 3: 0.5158859247604095, 4: 2.20093346789716, 5: 1.2479195221826886, 6: 2.6195958072763803, 7: -2.75586224955759, 8: -3.2386284107652026, 9: -2.748441027698477, 10: 3.180354526487653, 11: 0.020929782792514562, 12: -3.994240349409938, 13: -2.8070468552013903, 14: -0.47157100773189997, 15: -2.7483857068446795, 16: 3.7720366125760183, 17: -0.7222848178784029, 18: -4.432101849857077, 19: 3.694616273771045, 20: -3.157582808605958, 21: 1.94802258419912, 22: -2.063649770198125, 23: 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344: 0.7739230017870229, 345: 2.373042839672599, 346: 2.528416134671576, 347: 2.0143408852823734, 348: 2.4063074123367043, 349: 2.2238876534404453, 350: 3.47822137048862, 351: -4.463413272453382, 352: 0.9265500063084007, 353: 4.896262688504673, 354: -2.861908678029522, 355: -1.500593425138499, 356: -3.474608714959433, 357: -1.3421272684954584, 358: -4.919918813886511, 359: 1.6522227311243194, 360: -1.4836807673515011, 361: 0.3782505184181888, 362: -1.4182011058783042, 363: -4.9659057118903025, 364: 4.090873432544993, 365: 2.376800122612953, 366: -3.0946854732718383, 367: 3.0897554920979884, 368: 0.7038850144315854, 369: 3.013173170688808, 370: 0.8554663889264935, 371: 4.416604849852853, 372: 2.1389282757163732, 373: 4.750407156692254, 374: 2.5371364682437934, 375: -3.721240536811211, 376: -0.9060465857137725, 377: 3.16864871084272, 378: 3.1254810221426093, 379: 2.11647328298391} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 23.836652517318726 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 11748 entries, 0 to 11747 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 11748 non-null datetime64[ns] + 1 Signal 10988 non-null float64 + 2 Signal_Forecast 11748 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 275.5 KB +None +Forecasts + [[Timestamp('2001-04-02 20:00:00') nan 8.657206138710709] + [Timestamp('2001-04-02 21:00:00') nan 8.47478637981445] + [Timestamp('2001-04-02 22:00:00') nan 9.729120096862625] + ... + [Timestamp('2001-05-04 09:00:00') nan 8.623941566046604] + [Timestamp('2001-05-04 10:00:00') nan 8.779314861045581] + [Timestamp('2001-05-04 11:00:00') nan 8.265239611656378]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 760, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-04-02 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 10988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08242224573339389", + "MAPE": "0.0177", + "MASE": "0.0249", + "RMSE": "0.10330463807727093" + } + } +} + + + + + + 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'_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 11868 entries, 0 to 11867 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 11868 non-null datetime64[ns] + 1 Signal 10988 non-null float64 + 2 Signal_Forecast 11868 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 278.3 KB +None +Forecasts + [[Timestamp('2001-04-02 20:00:00') nan 7.869302545023303] + [Timestamp('2001-04-02 21:00:00') nan 2.448685828792426] + [Timestamp('2001-04-02 22:00:00') nan 4.840517814952181] + ... + [Timestamp('2001-05-09 09:00:00') nan 9.418777480840692] + [Timestamp('2001-05-09 10:00:00') nan 7.480274276632001] + [Timestamp('2001-05-09 11:00:00') nan 9.72250276881579]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 880, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2001-04-02 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 10988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08157508834385004", + "MAPE": "0.0178", + "MASE": "0.0245", + "RMSE": "0.102477928971905" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 440 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_80.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_80.log new file mode 100644 index 000000000..87960868c --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_11000_80.log @@ -0,0 +1,260 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 11000 80 +GENERATING_RANDOM_DATASET Signal_11000_H_0_constant_80_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 48.19736862182617 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2000-12-26T21:00:00.000000 TimeDelta= Horizon=160 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=10988 Min=1.0 Max=11.638628714203735 Mean=6.371969191028226 StdDev=2.99534008887571 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.638628714203735 Mean=6.371969191028226 StdDev=2.99534008887571 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0175 MAPE_Forecast=0.0178 MAPE_Test=0.0176 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0174 SMAPE_Forecast=0.0178 SMAPE_Test=0.0174 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.021 MASE_Forecast=0.0213 MASE_Test=0.0207 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07967659900759516 L1_Forecast=0.08094287299794205 L1_Test=0.07797888615622477 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10042545830867874 L2_Forecast=0.10099488312597175 L2_Test=0.0963438619612881 +INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.372931362913403 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 80 -0.1422748069419848 {0: 3.432803987620221, 1: -3.819788050125222, 2: -2.312070522752964, 3: -0.44521944732818053, 4: 3.8140623604144936, 5: -3.441596917439899, 6: 2.307165033094278, 7: 3.1882678488528464, 8: -0.053969174979351386, 9: 0.802403074472033, 10: -0.310298279670838, 11: -1.8164444908154591, 12: 4.689477398673767, 13: 4.088093489977726, 14: -3.827786311945331, 15: -2.4094859580393186, 16: 3.6973089845201583, 17: 4.969557342570103, 18: 0.9428124008398493, 19: 3.06878529221603, 20: 1.1815365411538066, 21: -1.3159920408206824, 22: -2.553703811144513, 23: -2.5593979618241143, 24: -3.165546990962704, 25: -0.0594087301546562, 26: -0.9273200795391459, 27: 3.2192553943626168, 28: -3.8079184716119894, 29: 2.192633218615901, 30: -0.9343485035807211, 31: -1.0824385497105116, 32: 4.302469457754391, 33: -2.0528564858067053, 34: -0.5850464721398279, 35: 4.4410494558581295, 36: 1.9321436322210035, 37: -1.4401241839807648, 38: -0.6870311831197551, 39: -4.930020430920207, 40: -4.927744938895699, 41: -0.4425607069262343, 42: 1.6630771205667294, 43: -4.32505789118029, 44: -0.18673817237691281, 45: -2.8145287548048827, 46: 4.928093096698125, 47: -4.43097099973912, 48: 0.32336863831990303, 49: 2.3086931965415225, 50: -1.0554182625735358, 51: -4.819408080325213, 52: 3.1935904536451982, 53: 0.18779684129424723, 54: 2.1773934423236883, 55: -0.546984350822096, 56: -3.5447238908248977, 57: 0.8146527524898586, 58: -4.948543294002908, 59: -3.214635631624582, 60: 1.6859118720509052, 61: -3.4529208495747516, 62: 0.302202690419457, 63: 4.450222310343123, 64: 3.552692120372395, 65: -4.190989799823278, 66: 3.5570662070265318, 67: 0.9347176223655325, 68: -4.5654049639012335, 69: 4.572199689192433, 70: 1.5544447207579237, 71: 4.824145390330326, 72: -3.055883454751539, 73: -2.447677540135487, 74: 2.3314276981632984, 75: -2.0722250877629835, 76: 4.934509304029215, 77: -3.30088637062218, 78: 1.0623380723290143, 79: 1.1887728321781843} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 6.768343687057495 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 11148 entries, 0 to 11147 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 11148 non-null datetime64[ns] + 1 Signal 10988 non-null float64 + 2 Signal_Forecast 11148 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 261.4 KB +None +Forecasts + [[Timestamp('2001-04-02 20:00:00') nan 2.5650128913014134] + [Timestamp('2001-04-02 21:00:00') nan 8.565564581529305] + [Timestamp('2001-04-02 22:00:00') nan 5.438582859332682] + [Timestamp('2001-04-02 23:00:00') nan 5.290492813202891] + [Timestamp('2001-04-03 00:00:00') nan 10.675400820667793] + [Timestamp('2001-04-03 01:00:00') nan 4.3200748771066975] + [Timestamp('2001-04-03 02:00:00') nan 5.787884890773575] + [Timestamp('2001-04-03 03:00:00') nan 10.813980818771533] + [Timestamp('2001-04-03 04:00:00') nan 8.305074995134406] + [Timestamp('2001-04-03 05:00:00') nan 4.932807178932638] + [Timestamp('2001-04-03 06:00:00') nan 5.685900179793648] + [Timestamp('2001-04-03 07:00:00') nan 1.4429109319931959] + [Timestamp('2001-04-03 08:00:00') nan 1.4451864240177041] + [Timestamp('2001-04-03 09:00:00') nan 5.9303706559871685] + [Timestamp('2001-04-03 10:00:00') nan 8.036008483480131] + [Timestamp('2001-04-03 11:00:00') nan 2.047873471733113] + [Timestamp('2001-04-03 12:00:00') nan 6.18619319053649] + [Timestamp('2001-04-03 13:00:00') nan 3.55840260810852] + [Timestamp('2001-04-03 14:00:00') nan 11.301024459611527] + [Timestamp('2001-04-03 15:00:00') nan 1.9419603631742826] + [Timestamp('2001-04-03 16:00:00') nan 6.696300001233306] + [Timestamp('2001-04-03 17:00:00') nan 8.681624559454924] + [Timestamp('2001-04-03 18:00:00') nan 5.317513100339867] + [Timestamp('2001-04-03 19:00:00') nan 1.5535232825881895] + [Timestamp('2001-04-03 20:00:00') nan 9.566521816558602] + [Timestamp('2001-04-03 21:00:00') nan 6.56072820420765] + [Timestamp('2001-04-03 22:00:00') nan 8.550324805237091] + [Timestamp('2001-04-03 23:00:00') nan 5.825947012091307] + [Timestamp('2001-04-04 00:00:00') nan 2.828207472088505] + [Timestamp('2001-04-04 01:00:00') nan 7.187584115403261] + [Timestamp('2001-04-04 02:00:00') nan 1.4243880689104946] + [Timestamp('2001-04-04 03:00:00') nan 3.158295731288821] + [Timestamp('2001-04-04 04:00:00') nan 8.058843234964307] + [Timestamp('2001-04-04 05:00:00') nan 2.920010513338651] + [Timestamp('2001-04-04 06:00:00') nan 6.67513405333286] + [Timestamp('2001-04-04 07:00:00') nan 10.823153673256526] + [Timestamp('2001-04-04 08:00:00') nan 9.925623483285797] + [Timestamp('2001-04-04 09:00:00') nan 2.181941563090125] + [Timestamp('2001-04-04 10:00:00') nan 9.929997569939935] + [Timestamp('2001-04-04 11:00:00') nan 7.307648985278935] + [Timestamp('2001-04-04 12:00:00') nan 1.8075263990121693] + [Timestamp('2001-04-04 13:00:00') nan 10.945131052105836] + [Timestamp('2001-04-04 14:00:00') nan 7.9273760836713265] + [Timestamp('2001-04-04 15:00:00') nan 11.197076753243728] + [Timestamp('2001-04-04 16:00:00') nan 3.3170479081618636] + [Timestamp('2001-04-04 17:00:00') nan 3.9252538227779157] + [Timestamp('2001-04-04 18:00:00') nan 8.704359061076701] + [Timestamp('2001-04-04 19:00:00') nan 4.30070627515042] + [Timestamp('2001-04-04 20:00:00') nan 11.307440666942618] + [Timestamp('2001-04-04 21:00:00') nan 3.072044992291223] + [Timestamp('2001-04-04 22:00:00') nan 7.435269435242417] + [Timestamp('2001-04-04 23:00:00') nan 7.561704195091587] + [Timestamp('2001-04-05 00:00:00') nan 9.805735350533624] + [Timestamp('2001-04-05 01:00:00') nan 2.5531433127881806] + [Timestamp('2001-04-05 02:00:00') nan 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19:00:00') nan 9.441716655129433] + [Timestamp('2001-04-05 20:00:00') nan 7.554467904067209] + [Timestamp('2001-04-05 21:00:00') nan 5.05693932209272] + [Timestamp('2001-04-05 22:00:00') nan 3.81922755176889] + [Timestamp('2001-04-05 23:00:00') nan 3.8135334010892885] + [Timestamp('2001-04-06 00:00:00') nan 3.207384371950699] + [Timestamp('2001-04-06 01:00:00') nan 6.313522632758747] + [Timestamp('2001-04-06 02:00:00') nan 5.445611283374257] + [Timestamp('2001-04-06 03:00:00') nan 9.59218675727602] + [Timestamp('2001-04-06 04:00:00') nan 2.5650128913014134] + [Timestamp('2001-04-06 05:00:00') nan 8.565564581529305] + [Timestamp('2001-04-06 06:00:00') nan 5.438582859332682] + [Timestamp('2001-04-06 07:00:00') nan 5.290492813202891] + [Timestamp('2001-04-06 08:00:00') nan 10.675400820667793] + [Timestamp('2001-04-06 09:00:00') nan 4.3200748771066975] + [Timestamp('2001-04-06 10:00:00') nan 5.787884890773575] + [Timestamp('2001-04-06 11:00:00') nan 10.813980818771533] + [Timestamp('2001-04-06 12:00:00') nan 8.305074995134406] + [Timestamp('2001-04-06 13:00:00') nan 4.932807178932638] + [Timestamp('2001-04-06 14:00:00') nan 5.685900179793648] + [Timestamp('2001-04-06 15:00:00') nan 1.4429109319931959] + [Timestamp('2001-04-06 16:00:00') nan 1.4451864240177041] + [Timestamp('2001-04-06 17:00:00') nan 5.9303706559871685] + [Timestamp('2001-04-06 18:00:00') nan 8.036008483480131] + [Timestamp('2001-04-06 19:00:00') nan 2.047873471733113] + [Timestamp('2001-04-06 20:00:00') nan 6.18619319053649] + [Timestamp('2001-04-06 21:00:00') nan 3.55840260810852] + [Timestamp('2001-04-06 22:00:00') nan 11.301024459611527] + [Timestamp('2001-04-06 23:00:00') nan 1.9419603631742826] + [Timestamp('2001-04-07 00:00:00') nan 6.696300001233306] + [Timestamp('2001-04-07 01:00:00') nan 8.681624559454924] + [Timestamp('2001-04-07 02:00:00') nan 5.317513100339867] + [Timestamp('2001-04-07 03:00:00') nan 1.5535232825881895] + [Timestamp('2001-04-07 04:00:00') nan 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nan 10.945131052105836] + [Timestamp('2001-04-07 22:00:00') nan 7.9273760836713265] + [Timestamp('2001-04-07 23:00:00') nan 11.197076753243728] + [Timestamp('2001-04-08 00:00:00') nan 3.3170479081618636] + [Timestamp('2001-04-08 01:00:00') nan 3.9252538227779157] + [Timestamp('2001-04-08 02:00:00') nan 8.704359061076701] + [Timestamp('2001-04-08 03:00:00') nan 4.30070627515042] + [Timestamp('2001-04-08 04:00:00') nan 11.307440666942618] + [Timestamp('2001-04-08 05:00:00') nan 3.072044992291223] + [Timestamp('2001-04-08 06:00:00') nan 7.435269435242417] + [Timestamp('2001-04-08 07:00:00') nan 7.561704195091587] + [Timestamp('2001-04-08 08:00:00') nan 9.805735350533624] + [Timestamp('2001-04-08 09:00:00') nan 2.5531433127881806] + [Timestamp('2001-04-08 10:00:00') nan 4.060860840160439] + [Timestamp('2001-04-08 11:00:00') nan 5.927711915585222] + [Timestamp('2001-04-08 12:00:00') nan 10.186993723327896] + [Timestamp('2001-04-08 13:00:00') nan 2.931334445473504] + [Timestamp('2001-04-08 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1.1322890180589238, 65: -1.5301049204326111, 66: -1.4573542849905423, 67: -0.016306091761943975, 68: 1.8345675385378568, 69: 1.7741160138016188, 70: -4.949526905016016, 71: 3.126489907534604, 72: -2.364228264785484, 73: -1.5256181629220213, 74: 1.7035735271441883, 75: 4.405176527767968, 76: 2.033757651192669, 77: -1.9419650710360137, 78: 3.0505989783931096, 79: 4.1322598593346465, 80: -4.956800835604811, 81: 4.910172598749361, 82: 3.20169626748016, 83: -1.870599958117428, 84: 4.107036263167568, 85: -3.304726942189773, 86: 4.3303746797169635, 87: 3.7033880642292187, 88: 2.046907519641871, 89: -0.5262318423600858, 90: 3.835824761658796, 91: 0.9241342758226443, 92: -1.3713610361998207, 93: -3.9581034315746493, 94: -2.0259850381214597, 95: -0.786436971554485, 96: -2.3551919321450203, 97: -4.238401131732692, 98: 1.1822848217093567, 99: -1.8732957258597254, 100: 2.4929524470103415, 101: -4.174077976401809, 102: -4.787466114378607, 103: -1.306130993906784, 104: 0.7649752232881522, 105: -2.649809873268363, 106: 4.19485136366533, 107: -2.219639234625186, 108: -3.5855577244900103, 109: -1.950112147201418, 110: 3.271396247117461, 111: 0.5543370950135857, 112: -2.80733820783447, 113: -3.2294342067609714, 114: 3.9909674460638156, 115: -4.799504246727276, 116: -4.749753253018003, 117: 1.7754416365523245, 118: 2.6940933873477952, 119: -4.011488264224488, 120: 3.0441215991977524, 121: -2.108131432315352, 122: 0.17942315123943287, 123: -3.5889114591628406, 124: 1.8390909630813015, 125: 0.21141868647933348, 126: -0.16230356942316249, 127: -0.5753105566357863, 128: -3.966645631535722, 129: 1.9077547670404762, 130: -4.655579578690276, 131: 4.985788319736909, 132: 1.2118720941261656, 133: 3.6832778362684797, 134: 2.857885652987897, 135: 0.40919821375877063, 136: -3.824747471129493, 137: 2.914465028478589, 138: -3.1626718358513752, 139: 3.7007705312001056} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 14.428113460540771 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 21268 entries, 0 to 21267 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 21268 non-null datetime64[ns] + 1 Signal 20988 non-null float64 + 2 Signal_Forecast 21268 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 498.6 KB +None +Forecasts + [[Timestamp('2002-05-24 12:00:00') nan 2.243050499825605] + [Timestamp('2002-05-24 13:00:00') nan 8.117450898401803] + [Timestamp('2002-05-24 14:00:00') nan 1.5541165526710516] + [Timestamp('2002-05-24 15:00:00') nan 11.195484451098237] + [Timestamp('2002-05-24 16:00:00') nan 7.421568225487492] + [Timestamp('2002-05-24 17:00:00') nan 9.892973967629807] + [Timestamp('2002-05-24 18:00:00') nan 9.067581784349224] + [Timestamp('2002-05-24 19:00:00') nan 6.618894345120098] + [Timestamp('2002-05-24 20:00:00') nan 2.384948660231834] + [Timestamp('2002-05-24 21:00:00') nan 9.124161159839916] + [Timestamp('2002-05-24 22:00:00') nan 3.047024295509952] + [Timestamp('2002-05-24 23:00:00') nan 9.910466662561433] + [Timestamp('2002-05-25 00:00:00') nan 8.620348113087326] + [Timestamp('2002-05-25 01:00:00') nan 1.8954177611666205] + [Timestamp('2002-05-25 02:00:00') nan 2.752370913270214] + [Timestamp('2002-05-25 03:00:00') nan 3.820403185403656] + [Timestamp('2002-05-25 04:00:00') nan 7.475340187193779] + [Timestamp('2002-05-25 05:00:00') nan 6.268823037800553] + [Timestamp('2002-05-25 06:00:00') nan 7.5501806652881465] + [Timestamp('2002-05-25 07:00:00') nan 5.3953973462583695] + [Timestamp('2002-05-25 08:00:00') nan 4.0267737917241995] + [Timestamp('2002-05-25 09:00:00') nan 7.471537297600404] + [Timestamp('2002-05-25 10:00:00') nan 7.541052892960101] + [Timestamp('2002-05-25 11:00:00') nan 7.026261109644243] + [Timestamp('2002-05-25 12:00:00') nan 3.040232011198796] + [Timestamp('2002-05-25 13:00:00') nan 6.7524084629653505] + [Timestamp('2002-05-25 14:00:00') nan 6.399421716749185] + [Timestamp('2002-05-25 15:00:00') nan 1.890940859355415] + [Timestamp('2002-05-25 16:00:00') nan 9.490140558984699] + [Timestamp('2002-05-25 17:00:00') nan 6.908376116792414] + [Timestamp('2002-05-25 18:00:00') nan 7.1268516044667765] + [Timestamp('2002-05-25 19:00:00') nan 8.330271290746044] + [Timestamp('2002-05-25 20:00:00') nan 3.319646051562379] + [Timestamp('2002-05-25 21:00:00') nan 3.5353515972874128] + [Timestamp('2002-05-25 22:00:00') nan 1.9041609636737649] + [Timestamp('2002-05-25 23:00:00') nan 10.333882996929859] + [Timestamp('2002-05-26 00:00:00') nan 3.542068682412136] + [Timestamp('2002-05-26 01:00:00') nan 3.462099800376613] + [Timestamp('2002-05-26 02:00:00') nan 9.422014544243375] + [Timestamp('2002-05-26 03:00:00') nan 3.2622580339993514] + [Timestamp('2002-05-26 04:00:00') nan 3.6747201401968046] + [Timestamp('2002-05-26 05:00:00') nan 10.391496599773589] + [Timestamp('2002-05-26 06:00:00') nan 10.388513212212708] + [Timestamp('2002-05-26 07:00:00') nan 5.043406560563627] + [Timestamp('2002-05-26 08:00:00') nan 10.749283020079758] + [Timestamp('2002-05-26 09:00:00') nan 3.9968943278957623] + [Timestamp('2002-05-26 10:00:00') nan 2.488029127689982] + [Timestamp('2002-05-26 11:00:00') nan 6.897928848728306] + [Timestamp('2002-05-26 12:00:00') nan 10.706002550608279] + [Timestamp('2002-05-26 13:00:00') nan 8.770879366077386] + [Timestamp('2002-05-26 14:00:00') nan 4.253888778845212] + [Timestamp('2002-05-26 15:00:00') nan 3.488434497644277] + [Timestamp('2002-05-26 16:00:00') nan 9.8275960448012] + [Timestamp('2002-05-26 17:00:00') nan 1.3330317486959977] + [Timestamp('2002-05-26 18:00:00') nan 5.899564183273116] + [Timestamp('2002-05-26 19:00:00') nan 5.321673559409334] + [Timestamp('2002-05-26 20:00:00') nan 3.770391932232634] + [Timestamp('2002-05-26 21:00:00') nan 8.542945077975258] + [Timestamp('2002-05-26 22:00:00') nan 9.74850865016418] + [Timestamp('2002-05-26 23:00:00') nan 2.0482888732256734] + [Timestamp('2002-05-27 00:00:00') nan 7.133595164182408] + [Timestamp('2002-05-27 01:00:00') nan 7.768843648758233] + [Timestamp('2002-05-27 02:00:00') nan 10.40217761378345] + [Timestamp('2002-05-27 03:00:00') nan 8.32714038355225] + [Timestamp('2002-05-27 04:00:00') nan 5.046266474295799] + [Timestamp('2002-05-27 05:00:00') nan 9.890272304660618] + [Timestamp('2002-05-27 06:00:00') nan 7.250554066096216] + [Timestamp('2002-05-27 07:00:00') nan 6.087565978517482] + [Timestamp('2002-05-27 08:00:00') nan 1.687952543484065] + [Timestamp('2002-05-27 09:00:00') nan 4.6092405415092275] + [Timestamp('2002-05-27 10:00:00') nan 10.33657169413718] + [Timestamp('2002-05-27 11:00:00') nan 10.622812506774281] + [Timestamp('2002-05-27 12:00:00') nan 8.394762579715918] + [Timestamp('2002-05-27 13:00:00') nan 6.817382814333871] + [Timestamp('2002-05-27 14:00:00') nan 2.907757810568368] + [Timestamp('2002-05-27 15:00:00') nan 9.619903319959715] + [Timestamp('2002-05-27 16:00:00') nan 7.3419851494202515] + [Timestamp('2002-05-27 17:00:00') nan 4.6795912109287165] + [Timestamp('2002-05-27 18:00:00') nan 4.7523418463707845] + [Timestamp('2002-05-27 19:00:00') nan 6.193390039599383] + [Timestamp('2002-05-27 20:00:00') nan 8.044263669899184] + [Timestamp('2002-05-27 21:00:00') nan 7.983812145162946] + [Timestamp('2002-05-27 22:00:00') nan 1.2601692263453108] + [Timestamp('2002-05-27 23:00:00') nan 9.336186038895931] + [Timestamp('2002-05-28 00:00:00') nan 3.8454678665758433] + [Timestamp('2002-05-28 01:00:00') nan 4.684077968439306] + [Timestamp('2002-05-28 02:00:00') nan 7.913269658505516] + [Timestamp('2002-05-28 03:00:00') nan 10.614872659129295] + [Timestamp('2002-05-28 04:00:00') nan 8.243453782553996] + [Timestamp('2002-05-28 05:00:00') nan 4.267731060325314] + [Timestamp('2002-05-28 06:00:00') nan 9.260295109754438] + [Timestamp('2002-05-28 07:00:00') nan 10.341955990695974] + [Timestamp('2002-05-28 08:00:00') nan 1.2528952957565158] + [Timestamp('2002-05-28 09:00:00') nan 11.119868730110689] + [Timestamp('2002-05-28 10:00:00') nan 9.411392398841487] + [Timestamp('2002-05-28 11:00:00') nan 4.339096173243899] + [Timestamp('2002-05-28 12:00:00') nan 10.316732394528895] + [Timestamp('2002-05-28 13:00:00') nan 2.9049691891715543] + [Timestamp('2002-05-28 14:00:00') nan 10.54007081107829] + [Timestamp('2002-05-28 15:00:00') nan 9.913084195590546] + [Timestamp('2002-05-28 16:00:00') nan 8.256603651003198] + [Timestamp('2002-05-28 17:00:00') nan 5.6834642890012415] + [Timestamp('2002-05-28 18:00:00') nan 10.045520893020123] + [Timestamp('2002-05-28 19:00:00') nan 7.1338304071839715] + [Timestamp('2002-05-28 20:00:00') nan 4.838335095161506] + [Timestamp('2002-05-28 21:00:00') nan 2.251592699786678] + [Timestamp('2002-05-28 22:00:00') nan 4.183711093239868] + [Timestamp('2002-05-28 23:00:00') nan 5.423259159806842] + [Timestamp('2002-05-29 00:00:00') nan 3.854504199216307] + [Timestamp('2002-05-29 01:00:00') nan 1.9712949996286353] + [Timestamp('2002-05-29 02:00:00') nan 7.391980953070684] + [Timestamp('2002-05-29 03:00:00') nan 4.336400405501601] + [Timestamp('2002-05-29 04:00:00') nan 8.702648578371669] + [Timestamp('2002-05-29 05:00:00') nan 2.0356181549595185] + [Timestamp('2002-05-29 06:00:00') nan 1.42223001698272] + [Timestamp('2002-05-29 07:00:00') nan 4.903565137454543] + [Timestamp('2002-05-29 08:00:00') nan 6.974671354649479] + [Timestamp('2002-05-29 09:00:00') nan 3.5598862580929644] + [Timestamp('2002-05-29 10:00:00') nan 10.404547495026657] + [Timestamp('2002-05-29 11:00:00') nan 3.9900568967361414] + [Timestamp('2002-05-29 12:00:00') nan 2.624138406871317] + [Timestamp('2002-05-29 13:00:00') nan 4.259583984159909] + [Timestamp('2002-05-29 14:00:00') nan 9.481092378478788] + [Timestamp('2002-05-29 15:00:00') nan 6.764033226374913] + [Timestamp('2002-05-29 16:00:00') nan 3.402357923526857] + [Timestamp('2002-05-29 17:00:00') nan 2.980261924600356] + [Timestamp('2002-05-29 18:00:00') nan 10.200663577425143] + [Timestamp('2002-05-29 19:00:00') nan 1.4101918846340515] + [Timestamp('2002-05-29 20:00:00') nan 1.4599428783433241] + [Timestamp('2002-05-29 21:00:00') nan 7.985137767913652] + [Timestamp('2002-05-29 22:00:00') nan 8.903789518709122] + [Timestamp('2002-05-29 23:00:00') nan 2.1982078671368397] + [Timestamp('2002-05-30 00:00:00') nan 9.25381773055908] + [Timestamp('2002-05-30 01:00:00') nan 4.101564699045975] + [Timestamp('2002-05-30 02:00:00') nan 6.38911928260076] + [Timestamp('2002-05-30 03:00:00') nan 2.6207846721984867] + [Timestamp('2002-05-30 04:00:00') nan 8.048787094442629] + [Timestamp('2002-05-30 05:00:00') nan 6.42111481784066] + [Timestamp('2002-05-30 06:00:00') nan 6.047392561938165] + [Timestamp('2002-05-30 07:00:00') nan 5.634385574725541] + [Timestamp('2002-05-30 08:00:00') nan 2.243050499825605] + [Timestamp('2002-05-30 09:00:00') nan 8.117450898401803] + [Timestamp('2002-05-30 10:00:00') nan 1.5541165526710516] + [Timestamp('2002-05-30 11:00:00') nan 11.195484451098237] + [Timestamp('2002-05-30 12:00:00') nan 7.421568225487492] + [Timestamp('2002-05-30 13:00:00') nan 9.892973967629807] + [Timestamp('2002-05-30 14:00:00') nan 9.067581784349224] + [Timestamp('2002-05-30 15:00:00') nan 6.618894345120098] + [Timestamp('2002-05-30 16:00:00') nan 2.384948660231834] + [Timestamp('2002-05-30 17:00:00') nan 9.124161159839916] + [Timestamp('2002-05-30 18:00:00') nan 3.047024295509952] + [Timestamp('2002-05-30 19:00:00') nan 9.910466662561433] + [Timestamp('2002-05-30 20:00:00') nan 8.620348113087326] + [Timestamp('2002-05-30 21:00:00') nan 1.8954177611666205] + [Timestamp('2002-05-30 22:00:00') nan 2.752370913270214] + [Timestamp('2002-05-30 23:00:00') nan 3.820403185403656] + [Timestamp('2002-05-31 00:00:00') nan 7.475340187193779] + [Timestamp('2002-05-31 01:00:00') nan 6.268823037800553] + [Timestamp('2002-05-31 02:00:00') nan 7.5501806652881465] + [Timestamp('2002-05-31 03:00:00') nan 5.3953973462583695] + [Timestamp('2002-05-31 04:00:00') nan 4.0267737917241995] + [Timestamp('2002-05-31 05:00:00') nan 7.471537297600404] + [Timestamp('2002-05-31 06:00:00') nan 7.541052892960101] + [Timestamp('2002-05-31 07:00:00') nan 7.026261109644243] + [Timestamp('2002-05-31 08:00:00') nan 3.040232011198796] + [Timestamp('2002-05-31 09:00:00') nan 6.7524084629653505] + [Timestamp('2002-05-31 10:00:00') nan 6.399421716749185] + [Timestamp('2002-05-31 11:00:00') nan 1.890940859355415] + [Timestamp('2002-05-31 12:00:00') nan 9.490140558984699] + [Timestamp('2002-05-31 13:00:00') nan 6.908376116792414] + [Timestamp('2002-05-31 14:00:00') nan 7.1268516044667765] + [Timestamp('2002-05-31 15:00:00') nan 8.330271290746044] + [Timestamp('2002-05-31 16:00:00') nan 3.319646051562379] + [Timestamp('2002-05-31 17:00:00') nan 3.5353515972874128] + [Timestamp('2002-05-31 18:00:00') nan 1.9041609636737649] + [Timestamp('2002-05-31 19:00:00') nan 10.333882996929859] + [Timestamp('2002-05-31 20:00:00') nan 3.542068682412136] + [Timestamp('2002-05-31 21:00:00') nan 3.462099800376613] + [Timestamp('2002-05-31 22:00:00') nan 9.422014544243375] + [Timestamp('2002-05-31 23:00:00') nan 3.2622580339993514] + [Timestamp('2002-06-01 00:00:00') nan 3.6747201401968046] + 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"TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-05-24 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 20988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08077488078219398", + "MAPE": "0.018", + "MASE": "0.0226", + "RMSE": "0.10104590272302597" + } + } +} + + + + + + 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+INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 6.4507856369018555 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 21028 entries, 0 to 21027 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 21028 non-null datetime64[ns] + 1 Signal 20988 non-null float64 + 2 Signal_Forecast 21028 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 493.0 KB +None +Forecasts + [[Timestamp('2002-05-24 12:00:00') nan 1.370260994854772] + [Timestamp('2002-05-24 13:00:00') nan 3.865716262837024] + [Timestamp('2002-05-24 14:00:00') nan 4.370821598808521] + [Timestamp('2002-05-24 15:00:00') nan 7.868275436302881] + [Timestamp('2002-05-24 16:00:00') nan 9.365130871739098] + [Timestamp('2002-05-24 17:00:00') nan 3.375101234109116] + [Timestamp('2002-05-24 18:00:00') nan 7.3668472539960135] + [Timestamp('2002-05-24 19:00:00') nan 4.873658693136499] + [Timestamp('2002-05-24 20:00:00') nan 5.375411170209381] + [Timestamp('2002-05-24 21:00:00') nan 10.372394035698822] + [Timestamp('2002-05-24 22:00:00') nan 8.869657735674497] + [Timestamp('2002-05-24 23:00:00') nan 10.368060067689711] + [Timestamp('2002-05-25 00:00:00') nan 2.373364572697599] + [Timestamp('2002-05-25 01:00:00') nan 4.3661047711711625] + [Timestamp('2002-05-25 02:00:00') nan 5.365588261208359] + [Timestamp('2002-05-25 03:00:00') nan 10.366372876854633] + [Timestamp('2002-05-25 04:00:00') nan 9.870624861623295] + [Timestamp('2002-05-25 05:00:00') nan 2.8681617902990624] + [Timestamp('2002-05-25 06:00:00') nan 3.8719098350898147] + [Timestamp('2002-05-25 07:00:00') nan 6.871016102388197] + [Timestamp('2002-05-25 08:00:00') nan 1.370260994854772] + [Timestamp('2002-05-25 09:00:00') nan 3.865716262837024] + [Timestamp('2002-05-25 10:00:00') nan 4.370821598808521] + [Timestamp('2002-05-25 11:00:00') nan 7.868275436302881] + [Timestamp('2002-05-25 12:00:00') nan 9.365130871739098] + [Timestamp('2002-05-25 13:00:00') nan 3.375101234109116] + [Timestamp('2002-05-25 14:00:00') nan 7.3668472539960135] + [Timestamp('2002-05-25 15:00:00') nan 4.873658693136499] + [Timestamp('2002-05-25 16:00:00') nan 5.375411170209381] + [Timestamp('2002-05-25 17:00:00') nan 10.372394035698822] + [Timestamp('2002-05-25 18:00:00') nan 8.869657735674497] + [Timestamp('2002-05-25 19:00:00') nan 10.368060067689711] + [Timestamp('2002-05-25 20:00:00') nan 2.373364572697599] + [Timestamp('2002-05-25 21:00:00') nan 4.3661047711711625] + [Timestamp('2002-05-25 22:00:00') nan 5.365588261208359] + [Timestamp('2002-05-25 23:00:00') nan 10.366372876854633] + [Timestamp('2002-05-26 00:00:00') nan 9.870624861623295] + [Timestamp('2002-05-26 01:00:00') nan 2.8681617902990624] + [Timestamp('2002-05-26 02:00:00') nan 3.8719098350898147] + [Timestamp('2002-05-26 03:00:00') nan 6.871016102388197]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 40, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-05-24 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 20988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08073286796912685", + "MAPE": "0.0176", + "MASE": "0.0261", + "RMSE": "0.10090799197051308" + } + } +} + + + + + + +{"Date":{"20988":"2002-05-24T12:00:00.000Z","20989":"2002-05-24T13:00:00.000Z","20990":"2002-05-24T14:00:00.000Z","20991":"2002-05-24T15:00:00.000Z","20992":"2002-05-24T16:00:00.000Z","20993":"2002-05-24T17:00:00.000Z","20994":"2002-05-24T18:00:00.000Z","20995":"2002-05-24T19:00:00.000Z","20996":"2002-05-24T20:00:00.000Z","20997":"2002-05-24T21:00:00.000Z","20998":"2002-05-24T22:00:00.000Z","20999":"2002-05-24T23:00:00.000Z","21000":"2002-05-25T00:00:00.000Z","21001":"2002-05-25T01:00:00.000Z","21002":"2002-05-25T02:00:00.000Z","21003":"2002-05-25T03:00:00.000Z","21004":"2002-05-25T04:00:00.000Z","21005":"2002-05-25T05:00:00.000Z","21006":"2002-05-25T06:00:00.000Z","21007":"2002-05-25T07:00:00.000Z","21008":"2002-05-25T08:00:00.000Z","21009":"2002-05-25T09:00:00.000Z","21010":"2002-05-25T10:00:00.000Z","21011":"2002-05-25T11:00:00.000Z","21012":"2002-05-25T12:00:00.000Z","21013":"2002-05-25T13:00:00.000Z","21014":"2002-05-25T14:00:00.000Z","21015":"2002-05-25T15:00:00.000Z","21016":"2002-05-25T16:00:00.000Z","21017":"2002-05-25T17:00:00.000Z","21018":"2002-05-25T18:00:00.000Z","21019":"2002-05-25T19:00:00.000Z","21020":"2002-05-25T20:00:00.000Z","21021":"2002-05-25T21:00:00.000Z","21022":"2002-05-25T22:00:00.000Z","21023":"2002-05-25T23:00:00.000Z","21024":"2002-05-26T00:00:00.000Z","21025":"2002-05-26T01:00:00.000Z","21026":"2002-05-26T02:00:00.000Z","21027":"2002-05-26T03:00:00.000Z"},"Signal":{"20988":null,"20989":null,"20990":null,"20991":null,"20992":null,"20993":null,"20994":null,"20995":null,"20996":null,"20997":null,"20998":null,"20999":null,"21000":null,"21001":null,"21002":null,"21003":null,"21004":null,"21005":null,"21006":null,"21007":null,"21008":null,"21009":null,"21010":null,"21011":null,"21012":null,"21013":null,"21014":null,"21015":null,"21016":null,"21017":null,"21018":null,"21019":null,"21020":null,"21021":null,"21022":null,"21023":null,"21024":null,"21025":null,"21026":null,"21027":null},"Signal_Forecast":{"20988":1.3702609949,"20989":3.8657162628,"20990":4.3708215988,"20991":7.8682754363,"20992":9.3651308717,"20993":3.3751012341,"20994":7.366847254,"20995":4.8736586931,"20996":5.3754111702,"20997":10.3723940357,"20998":8.8696577357,"20999":10.3680600677,"21000":2.3733645727,"21001":4.3661047712,"21002":5.3655882612,"21003":10.3663728769,"21004":9.8706248616,"21005":2.8681617903,"21006":3.8719098351,"21007":6.8710161024,"21008":1.3702609949,"21009":3.8657162628,"21010":4.3708215988,"21011":7.8682754363,"21012":9.3651308717,"21013":3.3751012341,"21014":7.366847254,"21015":4.8736586931,"21016":5.3754111702,"21017":10.3723940357,"21018":8.8696577357,"21019":10.3680600677,"21020":2.3733645727,"21021":4.3661047712,"21022":5.3655882612,"21023":10.3663728769,"21024":9.8706248616,"21025":2.8681617903,"21026":3.8719098351,"21027":6.8710161024}} + + + +TEST_CYCLES_END 20 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_200.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_200.log new file mode 100644 index 000000000..b99a906ef --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_200.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 21000 200 +GENERATING_RANDOM_DATASET Signal_21000_H_0_constant_200_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 130.70779013633728 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-11-17T05:00:00.000000 TimeDelta= Horizon=400 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=20988 Min=1.0 Max=11.388989018940086 Mean=6.254177197330205 StdDev=2.938338292373913 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.388989018940086 Mean=6.254177197330205 StdDev=2.938338292373913 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0178 MAPE_Test=0.018 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0175 SMAPE_Forecast=0.0177 SMAPE_Test=0.0178 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.022 MASE_Forecast=0.0226 MASE_Test=0.0214 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07881800691157978 L1_Forecast=0.08082333642614617 L1_Test=0.07688758732164615 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09953904269881558 L2_Forecast=0.1014000013877175 L2_Test=0.09357925233283451 +INFO:pyaf.std:MODEL_COMPLEXITY 8 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-4.780197412771107, 185: 4.770764581992205, 186: 1.9632595437544964, 187: -0.6786717959628401, 188: 1.0463557718388308, 189: 0.46770577544397174, 190: -1.2424346009044678, 191: 4.186821204759006, 192: -4.214120488078273, 193: 2.5981590668545156, 194: 2.8477793153310644, 195: 2.4780304207044095, 196: 0.537342724144263, 197: -3.748072175870264, 198: 4.424348387222393, 199: 1.0683522541155206} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 17.185582637786865 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 21388 entries, 0 to 21387 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 21388 non-null datetime64[ns] + 1 Signal 20988 non-null float64 + 2 Signal_Forecast 21388 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 501.4 KB +None +Forecasts + [[Timestamp('2002-05-24 12:00:00') nan 7.300605044944957] + [Timestamp('2002-05-24 13:00:00') nan 6.7219550485500985] + [Timestamp('2002-05-24 14:00:00') nan 5.011814672201659] + ... + [Timestamp('2002-06-10 01:00:00') nan 11.025013855098333] + [Timestamp('2002-06-10 02:00:00') nan 8.217508816860622] + [Timestamp('2002-06-10 03:00:00') nan 5.575577477143287]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 400, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-05-24 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 20988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08082333642614617", + "MAPE": "0.0178", + "MASE": "0.0226", + "RMSE": "0.1014000013877175" + } + } +} + + + + + + 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+INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 21.521478176116943 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 21508 entries, 0 to 21507 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 21508 non-null datetime64[ns] + 1 Signal 20988 non-null float64 + 2 Signal_Forecast 21508 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 504.2 KB +None +Forecasts + [[Timestamp('2002-05-24 12:00:00') nan 8.032017799609577] + [Timestamp('2002-05-24 13:00:00') nan 2.2495849968437236] + [Timestamp('2002-05-24 14:00:00') nan 5.658777624092609] + ... + [Timestamp('2002-06-15 01:00:00') nan 6.240970518445422] + [Timestamp('2002-06-15 02:00:00') nan 9.319467586751685] + [Timestamp('2002-06-15 03:00:00') nan 7.73411761878763]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 520, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-05-24 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 20988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08160852958856477", + "MAPE": "0.0177", + "MASE": "0.025", + "RMSE": "0.10157191003138824" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 260 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_320.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_320.log new file mode 100644 index 000000000..bd6931cd2 --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_320.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 21000 320 +GENERATING_RANDOM_DATASET Signal_21000_H_0_constant_320_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 195.078631401062 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-11-09T05:00:00.000000 TimeDelta= Horizon=640 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=20988 Min=1.0 Max=11.37885166200663 Mean=6.22973436874409 StdDev=2.885738899816027 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 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+INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.228914472288353 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 320 -0.10964346215433363 {0: 1.0589605433826668, 1: -4.738983080311806, 2: 1.5619302022377193, 3: 3.643273322757472, 4: 2.56409794228768, 5: 4.109014466431451, 6: -2.279076664200376, 7: -2.843148175712545, 8: -2.282756680368313, 9: 4.796124133861423, 10: 1.0037367060348261, 11: -3.816702357111489, 12: -2.2607495357222276, 13: 0.4226213123543028, 14: -2.2855325616387634, 15: 0.13919369804533765, 16: -4.249139758922265, 17: -2.7608993829464716, 18: 3.2651087575920767, 19: -1.4485265724193317, 20: 2.582159213990919, 21: 1.1672862322559725, 22: -1.9387979318296162, 23: 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-4.526260715333163, 265: -1.716225288220957, 266: 0.12760729228456125, 267: -2.0962046305809148, 268: -4.24016252908411, 269: -1.3705828026094062, 270: 0.9629982924056248, 271: 1.0845138207761913, 272: 4.575574057234184, 273: -0.7401186491950131, 274: -2.0969913620226484, 275: 2.4268669870250283, 276: -2.292057351244699, 277: -0.006874463985365509, 278: -0.4158559404139677, 279: -2.782798467124011, 280: -2.2996228890036363, 281: -4.635995051769283, 282: 4.767457809176338, 283: -2.494925627916646, 284: -1.2665405242414147, 285: 1.3109585079046733, 286: 1.8649307111310263, 287: 3.7831912050345657, 288: 3.964437327863088, 289: 3.4030409046337597, 290: 3.8315710557691265, 291: 3.618292631408205, 292: -4.3484481907358585, 293: 2.057532630001851, 294: -2.4515588797587613, 295: -0.7983128811310389, 296: -3.1120949913678957, 297: -0.6139984947162196, 298: -4.873376110694539, 299: 2.9865882286907874, 300: -0.7638469368131995, 301: 1.4260088071199135, 302: -0.6754969236794008, 303: -4.86275917568968, 304: 3.796552790100548, 305: -2.704623400533743, 306: 4.593204005774625, 307: 1.8097198207114094, 308: 4.568812900988732, 309: 2.0766451690660075, 310: 3.5327730213996205, 311: 4.033932753460839, 312: -3.3980659020614086, 313: -0.06111255623827283, 314: 4.804842747072754, 315: 4.709059347719196, 316: 3.524342966335899, 317: -3.357840449585181, 318: 4.49224997085802, 319: -2.0753474157561427} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 27.534795999526978 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 21628 entries, 0 to 21627 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 21628 non-null datetime64[ns] + 1 Signal 20988 non-null float64 + 2 Signal_Forecast 21628 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 507.0 KB +None +Forecasts + [[Timestamp('2002-05-24 12:00:00') nan 6.098630459096321] + [Timestamp('2002-05-24 13:00:00') nan 4.696693527257269] + [Timestamp('2002-05-24 14:00:00') nan 9.273680878444024] + ... + [Timestamp('2002-06-20 01:00:00') nan 5.523393858417273] + [Timestamp('2002-06-20 02:00:00') nan 6.900677152447968] + [Timestamp('2002-06-20 03:00:00') nan 5.1090485082998685]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 640, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-05-24 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 20988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08176737837525865", + "MAPE": "0.018", + "MASE": "0.0251", + "RMSE": "0.10161495713387622" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 320 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_380.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_380.log new file mode 100644 index 000000000..965c713ab --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_380.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 21000 380 +GENERATING_RANDOM_DATASET Signal_21000_H_0_constant_380_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 214.80890321731567 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-11-05T05:00:00.000000 TimeDelta= Horizon=760 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=20988 Min=1.0 Max=11.38658543695496 Mean=6.276224570847281 StdDev=2.9079531028959695 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.38658543695496 Mean=6.276224570847281 StdDev=2.9079531028959695 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0172 MAPE_Forecast=0.0179 MAPE_Test=0.0182 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0171 SMAPE_Forecast=0.0178 SMAPE_Test=0.0182 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0236 MASE_Forecast=0.0245 MASE_Test=0.0244 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.0784273773709982 L1_Forecast=0.08131799418675782 L1_Test=0.08096364542627017 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09959529459211529 L2_Forecast=0.10168677795510155 L2_Test=0.10129301931369575 +INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.274667865428558 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 380 0.01481084465242688 {0: 0.07762368127403274, 1: 4.395321872320347, 2: -4.802753623457417, 3: 0.4909783692240559, 4: 2.201873222850555, 5: 1.31294814202713, 6: 2.6126635557463054, 7: -2.7552182776704455, 8: -3.242418393066062, 9: -2.7458955956145408, 10: 3.1754142491295703, 11: -0.013798256203605419, 12: -4.03507931919759, 13: -2.76387234983488, 14: -0.45716580099901627, 15: -2.739930141047663, 16: 3.806004775083636, 17: -0.7089806258398914, 18: -4.406908081831842, 19: 3.7028625410197016, 20: -3.171763453922226, 21: 1.8932069491224297, 22: -2.0206010736371383, 23: 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3.3535847083125274, 343: 0.2767964639745073, 344: 0.7594797509005162, 345: 2.3573484271433323, 346: 2.5259233068199514, 347: 2.0153668049956597, 348: 2.4054012046584967, 349: 2.217649477004284, 350: 3.4810696271075834, 351: -4.483669951071297, 352: 0.9187166166122909, 353: 4.90154894941138, 354: -2.891208317641108, 355: -1.5258453270265777, 356: -3.4771270164403267, 357: -1.3456429746530878, 358: -4.916188033905316, 359: 1.6546826334364093, 360: -1.4800477259395421, 361: 0.41021743819948986, 362: -1.3886958551974153, 363: -4.963736536186875, 364: 4.091513508424351, 365: 2.379269004362081, 366: -3.0975296453065218, 367: 3.0957282188060917, 368: 0.6969595887347442, 369: 3.0014184752321427, 370: 0.8717400893241121, 371: 4.424464889716621, 372: 2.1269500559204166, 373: 4.764130115213645, 374: 2.5747364006535154, 375: -3.717887075546358, 376: -0.9165696190427206, 377: 3.198336864044247, 378: 3.1142969701674685, 379: 2.1165197182652236} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 43.681068897247314 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 21748 entries, 0 to 21747 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 21748 non-null datetime64[ns] + 1 Signal 20988 non-null float64 + 2 Signal_Forecast 21748 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 509.8 KB +None +Forecasts + [[Timestamp('2002-05-24 12:00:00') nan 9.244277171124736] + [Timestamp('2002-05-24 13:00:00') nan 3.061481350215381] + [Timestamp('2002-05-24 14:00:00') nan 5.665901652938632] + ... + [Timestamp('2002-06-25 01:00:00') nan 11.064996283051517] + [Timestamp('2002-06-25 02:00:00') nan 10.183057906830221] + [Timestamp('2002-06-25 03:00:00') nan 2.493409604355505]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 760, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-05-24 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 20988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08131799418675782", + "MAPE": "0.0179", + "MASE": "0.0245", + "RMSE": "0.10168677795510155" + } + } +} + + + + + + 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'_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 21868 entries, 0 to 21867 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 21868 non-null datetime64[ns] + 1 Signal 20988 non-null float64 + 2 Signal_Forecast 21868 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 512.7 KB +None +Forecasts + [[Timestamp('2002-05-24 12:00:00') nan 4.178405645806276] + [Timestamp('2002-05-24 13:00:00') nan 5.961980715963765] + [Timestamp('2002-05-24 14:00:00') nan 9.5962206844448] + ... + [Timestamp('2002-06-30 01:00:00') nan 3.0498835946124228] + [Timestamp('2002-06-30 02:00:00') nan 4.288378407224063] + [Timestamp('2002-06-30 03:00:00') nan 5.55464120595861]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 880, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2002-05-24 11:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 20988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08202310594399796", + "MAPE": "0.0176", + "MASE": "0.0245", + "RMSE": "0.102770253793865" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 440 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_80.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_80.log new file mode 100644 index 000000000..186d61eda --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_21000_80.log @@ -0,0 +1,260 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 21000 80 +GENERATING_RANDOM_DATASET Signal_21000_H_0_constant_80_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 71.50394320487976 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2001-11-25T05:00:00.000000 TimeDelta= Horizon=160 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=20988 Min=1.0 Max=11.638628714203735 Mean=6.372020897061164 StdDev=2.99501594254902 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.638628714203735 Mean=6.372020897061164 StdDev=2.99501594254902 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0177 MAPE_Test=0.0174 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0175 SMAPE_Forecast=0.0176 SMAPE_Test=0.0174 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.021 MASE_Forecast=0.0212 MASE_Test=0.0207 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07994362617025408 L1_Forecast=0.08064950238250072 L1_Test=0.07816909327178907 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10057684964926282 L2_Forecast=0.10102341971666792 L2_Test=0.09668241848127522 +INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.371841092019485 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 80 -0.13935776587814264 {0: 3.432266557863711, 1: -3.8297925744188643, 2: -2.312061375608499, 3: -0.44361975013491595, 4: 3.8137480376290434, 5: -3.440618769030695, 6: 2.317358564724188, 7: 3.1884819501377386, 8: -0.045618605445495675, 9: 0.8018259211735383, 10: -0.3072561161997278, 11: -1.8209865741381268, 12: 4.682333545816425, 13: 4.083613342963648, 14: -3.818895718264323, 15: -2.413693840882105, 16: 3.6972620656688466, 17: 4.96206986241685, 18: 0.9448463087599244, 19: 3.0619751515573466, 20: 1.1909686981280263, 21: -1.300558431091833, 22: -2.5668502166645464, 23: -2.5628268290199845, 24: -3.17376341849774, 25: -0.05831845926073864, 26: -0.926381671284445, 27: 3.2112526998741773, 28: -3.810827034036325, 29: 2.185220319565687, 30: -0.9345035480046819, 31: -1.071411993710314, 32: 4.310732885917249, 33: -2.046620347036788, 34: -0.5734634677501464, 35: 4.442139726752047, 36: 1.9354816502736334, 37: -1.454962443345941, 38: -0.6838883394206734, 39: -4.931172657578081, 40: -4.919703384345303, 41: -0.4414867320787135, 42: 1.673614965425804, 43: -4.327425635222832, 44: -0.1865860743314487, 45: -2.808558517288752, 46: 4.9333695564964435, 47: -4.4411097885625175, 48: 0.31133594752243354, 49: 2.306686006922055, 50: -1.0595126467689027, 51: -4.811841404794434, 52: 3.1902855852166327, 53: 0.18238391906587204, 54: 2.182979014402176, 55: -0.5596455879538826, 56: -3.5473015109506076, 57: 0.8140872333369318, 58: -4.943038392388266, 59: -3.2002555279102687, 60: 1.6870021429448228, 61: -3.4420416513157304, 62: 0.31082672599514627, 63: 4.439991796243197, 64: 3.557947371595688, 65: -4.193708030228482, 66: 3.5661875359612463, 67: 0.9223999957647457, 68: -4.566092746729023, 69: 4.571316922766539, 70: 1.557449014644133, 71: 4.821838379369269, 72: -3.072239599521995, 73: -2.441844329859912, 74: 2.3223079952013057, 75: -2.072140260395623, 76: 4.933493680858712, 77: -3.298554031947401, 78: 1.0606021986824468, 79: 1.1903653353371055} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 8.27073073387146 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 21148 entries, 0 to 21147 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 21148 non-null datetime64[ns] + 1 Signal 20988 non-null float64 + 2 Signal_Forecast 21148 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 495.8 KB +None +Forecasts + [[Timestamp('2002-05-24 12:00:00') nan 2.56101405798316] + [Timestamp('2002-05-24 13:00:00') nan 8.557061411585172] + [Timestamp('2002-05-24 14:00:00') nan 5.437337544014803] + [Timestamp('2002-05-24 15:00:00') nan 5.300429098309172] + [Timestamp('2002-05-24 16:00:00') nan 10.682573977936734] + [Timestamp('2002-05-24 17:00:00') nan 4.325220744982698] + [Timestamp('2002-05-24 18:00:00') nan 5.798377624269339] + [Timestamp('2002-05-24 19:00:00') nan 10.813980818771533] + [Timestamp('2002-05-24 20:00:00') nan 8.307322742293119] + [Timestamp('2002-05-24 21:00:00') nan 4.916878648673544] + [Timestamp('2002-05-24 22:00:00') nan 5.687952752598812] + [Timestamp('2002-05-24 23:00:00') nan 1.4406684344414042] + [Timestamp('2002-05-25 00:00:00') nan 1.452137707674182] + [Timestamp('2002-05-25 01:00:00') nan 5.930354359940772] + [Timestamp('2002-05-25 02:00:00') nan 8.04545605744529] + [Timestamp('2002-05-25 03:00:00') nan 2.044415456796653] + [Timestamp('2002-05-25 04:00:00') nan 6.1852550176880365] + [Timestamp('2002-05-25 05:00:00') nan 3.5632825747307333] + [Timestamp('2002-05-25 06:00:00') nan 11.305210648515928] + [Timestamp('2002-05-25 07:00:00') nan 1.9307313034569678] + [Timestamp('2002-05-25 08:00:00') nan 6.683177039541919] + [Timestamp('2002-05-25 09:00:00') nan 8.67852709894154] + [Timestamp('2002-05-25 10:00:00') nan 5.312328445250582] + [Timestamp('2002-05-25 11:00:00') nan 1.5599996872250514] + [Timestamp('2002-05-25 12:00:00') nan 9.562126677236119] + [Timestamp('2002-05-25 13:00:00') nan 6.554225011085357] + [Timestamp('2002-05-25 14:00:00') nan 8.554820106421662] + [Timestamp('2002-05-25 15:00:00') nan 5.812195504065603] + [Timestamp('2002-05-25 16:00:00') nan 2.8245395810688776] + [Timestamp('2002-05-25 17:00:00') nan 7.185928325356417] + [Timestamp('2002-05-25 18:00:00') nan 1.4288026996312189] + [Timestamp('2002-05-25 19:00:00') nan 3.1715855641092165] + [Timestamp('2002-05-25 20:00:00') nan 8.058843234964307] + [Timestamp('2002-05-25 21:00:00') nan 2.929799440703755] + [Timestamp('2002-05-25 22:00:00') nan 6.6826678180146315] + [Timestamp('2002-05-25 23:00:00') nan 10.811832888262682] + [Timestamp('2002-05-26 00:00:00') nan 9.929788463615173] + [Timestamp('2002-05-26 01:00:00') nan 2.1781330617910033] + [Timestamp('2002-05-26 02:00:00') nan 9.938028627980731] + [Timestamp('2002-05-26 03:00:00') nan 7.294241087784231] + [Timestamp('2002-05-26 04:00:00') nan 1.8057483452904624] + [Timestamp('2002-05-26 05:00:00') nan 10.943158014786025] + [Timestamp('2002-05-26 06:00:00') nan 7.929290106663618] + [Timestamp('2002-05-26 07:00:00') nan 11.193679471388755] + [Timestamp('2002-05-26 08:00:00') nan 3.2996014924974904] + [Timestamp('2002-05-26 09:00:00') nan 3.9299967621595733] + [Timestamp('2002-05-26 10:00:00') nan 8.69414908722079] + [Timestamp('2002-05-26 11:00:00') nan 4.299700831623863] + [Timestamp('2002-05-26 12:00:00') nan 11.305334772878197] + [Timestamp('2002-05-26 13:00:00') nan 3.0732870600720843] + [Timestamp('2002-05-26 14:00:00') nan 7.432443290701932] + [Timestamp('2002-05-26 15:00:00') nan 7.562206427356591] + [Timestamp('2002-05-26 16:00:00') nan 9.804107649883196] + [Timestamp('2002-05-26 17:00:00') nan 2.542048517600621] + [Timestamp('2002-05-26 18:00:00') nan 4.059779716410986] + [Timestamp('2002-05-26 19:00:00') nan 5.928221341884569] + [Timestamp('2002-05-26 20:00:00') nan 10.185589129648529] + [Timestamp('2002-05-26 21:00:00') nan 2.9312223229887904] + [Timestamp('2002-05-26 22:00:00') nan 8.689199656743673] + [Timestamp('2002-05-26 23:00:00') nan 9.560323042157224] + [Timestamp('2002-05-27 00:00:00') nan 6.32622248657399] + [Timestamp('2002-05-27 01:00:00') nan 7.173667013193024] + [Timestamp('2002-05-27 02:00:00') nan 6.064584975819757] + [Timestamp('2002-05-27 03:00:00') nan 4.550854517881358] + [Timestamp('2002-05-27 04:00:00') nan 11.05417463783591] + [Timestamp('2002-05-27 05:00:00') nan 10.455454434983134] + [Timestamp('2002-05-27 06:00:00') nan 2.552945373755162] + [Timestamp('2002-05-27 07:00:00') nan 3.95814725113738] + [Timestamp('2002-05-27 08:00:00') nan 10.069103157688332] + [Timestamp('2002-05-27 09:00:00') nan 11.333910954436336] + [Timestamp('2002-05-27 10:00:00') nan 7.31668740077941] + [Timestamp('2002-05-27 11:00:00') nan 9.433816243576832] + [Timestamp('2002-05-27 12:00:00') nan 7.5628097901475115] + [Timestamp('2002-05-27 13:00:00') nan 5.071282660927652] + [Timestamp('2002-05-27 14:00:00') nan 3.804990875354939] + [Timestamp('2002-05-27 15:00:00') nan 3.8090142629995007] + [Timestamp('2002-05-27 16:00:00') nan 3.198077673521745] + [Timestamp('2002-05-27 17:00:00') nan 6.313522632758747] + [Timestamp('2002-05-27 18:00:00') nan 5.44545942073504] + [Timestamp('2002-05-27 19:00:00') nan 9.583093791893663] + [Timestamp('2002-05-27 20:00:00') nan 2.56101405798316] + [Timestamp('2002-05-27 21:00:00') nan 8.557061411585172] + [Timestamp('2002-05-27 22:00:00') nan 5.437337544014803] + [Timestamp('2002-05-27 23:00:00') nan 5.300429098309172] + [Timestamp('2002-05-28 00:00:00') nan 10.682573977936734] + [Timestamp('2002-05-28 01:00:00') nan 4.325220744982698] + [Timestamp('2002-05-28 02:00:00') nan 5.798377624269339] + [Timestamp('2002-05-28 03:00:00') nan 10.813980818771533] + [Timestamp('2002-05-28 04:00:00') nan 8.307322742293119] + [Timestamp('2002-05-28 05:00:00') nan 4.916878648673544] + [Timestamp('2002-05-28 06:00:00') nan 5.687952752598812] + [Timestamp('2002-05-28 07:00:00') nan 1.4406684344414042] + [Timestamp('2002-05-28 08:00:00') nan 1.452137707674182] + [Timestamp('2002-05-28 09:00:00') nan 5.930354359940772] + [Timestamp('2002-05-28 10:00:00') nan 8.04545605744529] + [Timestamp('2002-05-28 11:00:00') nan 2.044415456796653] + [Timestamp('2002-05-28 12:00:00') nan 6.1852550176880365] + [Timestamp('2002-05-28 13:00:00') nan 3.5632825747307333] + [Timestamp('2002-05-28 14:00:00') nan 11.305210648515928] + [Timestamp('2002-05-28 15:00:00') nan 1.9307313034569678] + [Timestamp('2002-05-28 16:00:00') nan 6.683177039541919] + [Timestamp('2002-05-28 17:00:00') nan 8.67852709894154] + [Timestamp('2002-05-28 18:00:00') nan 5.312328445250582] + [Timestamp('2002-05-28 19:00:00') nan 1.5599996872250514] + [Timestamp('2002-05-28 20:00:00') nan 9.562126677236119] + [Timestamp('2002-05-28 21:00:00') nan 6.554225011085357] + [Timestamp('2002-05-28 22:00:00') nan 8.554820106421662] + [Timestamp('2002-05-28 23:00:00') nan 5.812195504065603] + [Timestamp('2002-05-29 00:00:00') nan 2.8245395810688776] + [Timestamp('2002-05-29 01:00:00') nan 7.185928325356417] + [Timestamp('2002-05-29 02:00:00') nan 1.4288026996312189] + [Timestamp('2002-05-29 03:00:00') nan 3.1715855641092165] + [Timestamp('2002-05-29 04:00:00') nan 8.058843234964307] + [Timestamp('2002-05-29 05:00:00') nan 2.929799440703755] + [Timestamp('2002-05-29 06:00:00') nan 6.6826678180146315] + [Timestamp('2002-05-29 07:00:00') nan 10.811832888262682] + [Timestamp('2002-05-29 08:00:00') nan 9.929788463615173] + [Timestamp('2002-05-29 09:00:00') nan 2.1781330617910033] + [Timestamp('2002-05-29 10:00:00') nan 9.938028627980731] + [Timestamp('2002-05-29 11:00:00') nan 7.294241087784231] + [Timestamp('2002-05-29 12:00:00') nan 1.8057483452904624] + [Timestamp('2002-05-29 13:00:00') nan 10.943158014786025] + [Timestamp('2002-05-29 14:00:00') nan 7.929290106663618] + [Timestamp('2002-05-29 15:00:00') nan 11.193679471388755] + [Timestamp('2002-05-29 16:00:00') nan 3.2996014924974904] + [Timestamp('2002-05-29 17:00:00') nan 3.9299967621595733] + [Timestamp('2002-05-29 18:00:00') nan 8.69414908722079] + [Timestamp('2002-05-29 19:00:00') nan 4.299700831623863] + [Timestamp('2002-05-29 20:00:00') nan 11.305334772878197] + [Timestamp('2002-05-29 21:00:00') nan 3.0732870600720843] + [Timestamp('2002-05-29 22:00:00') nan 7.432443290701932] + [Timestamp('2002-05-29 23:00:00') nan 7.562206427356591] + [Timestamp('2002-05-30 00:00:00') nan 9.804107649883196] + [Timestamp('2002-05-30 01:00:00') nan 2.542048517600621] + [Timestamp('2002-05-30 02:00:00') nan 4.059779716410986] + [Timestamp('2002-05-30 03:00:00') nan 5.928221341884569] + [Timestamp('2002-05-30 04:00:00') nan 10.185589129648529] + [Timestamp('2002-05-30 05:00:00') nan 2.9312223229887904] + [Timestamp('2002-05-30 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1.1258417876433255, 65: -1.5276662989547862, 66: -1.44777715134237, 67: -0.02163997207696866, 68: 1.8377113247128944, 69: 1.770838144377695, 70: -4.94515481192744, 71: 3.125939651227749, 72: -2.367124030527451, 73: -1.5235446059112636, 74: 1.693777231574444, 75: 4.398868098473684, 76: 2.0369618209429, 77: -1.9327366376027149, 78: 3.0358549076979626, 79: 4.133901714920472, 80: -4.955843199114406, 81: 4.895758156265109, 82: 3.197908529267422, 83: -1.875730269851247, 84: 4.112531793256788, 85: -3.3013045817280204, 86: 4.331400051689076, 87: 3.6988150215900637, 88: 2.0430576915352914, 89: -0.5308083983067595, 90: 3.8362315288802575, 91: 0.9160361094006415, 92: -1.371891287724317, 93: -3.9577441261984267, 94: -2.0183111951752926, 95: -0.7887053299235367, 96: -2.3556271439707817, 97: -4.230480565691158, 98: 1.1857894095249115, 99: -1.8781312485900257, 100: 2.484081191300924, 101: -4.169964933204154, 102: -4.795188202406598, 103: -1.318560410746806, 104: 0.7692598978594187, 105: -2.650947645499289, 106: 4.188541717052028, 107: -2.221908651420598, 108: -3.5859051348040447, 109: -1.958067020809012, 110: 3.2693323191132384, 111: 0.5461266540202674, 112: -2.8070347744350306, 113: -3.23153667300451, 114: 3.9908848164226773, 115: -4.800209041882843, 116: -4.751476742642405, 117: 1.7652807959590033, 118: 2.696487285742557, 119: -4.0293124866930246, 120: 3.0533947816269373, 121: -2.0990767439027396, 122: 0.17332315895228767, 123: -3.589046002472294, 124: 1.8393413057298842, 125: 0.20588279621683814, 126: -0.16797317987391835, 127: -0.5843224853620637, 128: -3.956941626713052, 129: 1.9075377475481696, 130: -4.647376904064047, 131: 4.980300248884915, 132: 1.215316515286828, 133: 3.6804746251581753, 134: 2.8517782972180905, 135: 0.41117965629749254, 136: -3.8188925737138724, 137: 2.9031679623280757, 138: -3.163794880803043, 139: 3.6975268502578684} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 27.20607089996338 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 31268 entries, 0 to 31267 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 31268 non-null datetime64[ns] + 1 Signal 30988 non-null float64 + 2 Signal_Forecast 31268 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 733.0 KB +None +Forecasts + [[Timestamp('2003-07-15 04:00:00') nan 7.129175321026894] + [Timestamp('2003-07-15 05:00:00') nan 7.766220621967307] + [Timestamp('2003-07-15 06:00:00') nan 10.40217761378345] + [Timestamp('2003-07-15 07:00:00') nan 8.32714038355225] + [Timestamp('2003-07-15 08:00:00') nan 5.048401851310198] + [Timestamp('2003-07-15 09:00:00') nan 9.89957730739377] + [Timestamp('2003-07-15 10:00:00') nan 7.246076643665289] + [Timestamp('2003-07-15 11:00:00') nan 6.1028562762398435] + [Timestamp('2003-07-15 12:00:00') nan 1.699506297588762] + [Timestamp('2003-07-15 13:00:00') nan 4.60825208739269] + [Timestamp('2003-07-15 14:00:00') nan 10.341028193212248] + [Timestamp('2003-07-15 15:00:00') nan 10.618952270200351] + [Timestamp('2003-07-15 16:00:00') nan 8.398210529063658] + [Timestamp('2003-07-15 17:00:00') nan 6.8231364248301] + [Timestamp('2003-07-15 18:00:00') nan 2.9060195461399974] + [Timestamp('2003-07-15 19:00:00') nan 9.618766870557609] + [Timestamp('2003-07-15 20:00:00') nan 7.336824562443722] + [Timestamp('2003-07-15 21:00:00') nan 4.6833164758456105] + [Timestamp('2003-07-15 22:00:00') nan 4.763205623458027] + [Timestamp('2003-07-15 23:00:00') nan 6.189342802723428] + [Timestamp('2003-07-16 00:00:00') nan 8.048694099513291] + [Timestamp('2003-07-16 01:00:00') nan 7.981820919178092] + [Timestamp('2003-07-16 02:00:00') nan 1.2658279628729563] + [Timestamp('2003-07-16 03:00:00') nan 9.336922426028146] + [Timestamp('2003-07-16 04:00:00') nan 3.843858744272946] + [Timestamp('2003-07-16 05:00:00') nan 4.687438168889133] + [Timestamp('2003-07-16 06:00:00') nan 7.904760006374841] + [Timestamp('2003-07-16 07:00:00') nan 10.60985087327408] + [Timestamp('2003-07-16 08:00:00') nan 8.247944595743297] + [Timestamp('2003-07-16 09:00:00') nan 4.278246137197682] + [Timestamp('2003-07-16 10:00:00') nan 9.24683768249836] + [Timestamp('2003-07-16 11:00:00') nan 10.344884489720869] + [Timestamp('2003-07-16 12:00:00') nan 1.255139575685991] + [Timestamp('2003-07-16 13:00:00') nan 11.106740931065506] + [Timestamp('2003-07-16 14:00:00') nan 9.408891304067819] + [Timestamp('2003-07-16 15:00:00') nan 4.33525250494915] + [Timestamp('2003-07-16 16:00:00') nan 10.323514568057185] + [Timestamp('2003-07-16 17:00:00') nan 2.9096781930723763] + [Timestamp('2003-07-16 18:00:00') nan 10.542382826489472] + [Timestamp('2003-07-16 19:00:00') nan 9.90979779639046] + [Timestamp('2003-07-16 20:00:00') nan 8.254040466335688] + [Timestamp('2003-07-16 21:00:00') nan 5.680174376493637] + [Timestamp('2003-07-16 22:00:00') nan 10.047214303680654] + [Timestamp('2003-07-16 23:00:00') nan 7.127018884201038] + [Timestamp('2003-07-17 00:00:00') nan 4.83909148707608] + [Timestamp('2003-07-17 01:00:00') nan 2.25323864860197] + [Timestamp('2003-07-17 02:00:00') nan 4.192671579625104] + [Timestamp('2003-07-17 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+INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 3.6424620151519775 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 31028 entries, 0 to 31027 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 31028 non-null datetime64[ns] + 1 Signal 30988 non-null float64 + 2 Signal_Forecast 31028 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 727.3 KB +None +Forecasts + [[Timestamp('2003-07-15 04:00:00') nan 1.3717179200208642] + [Timestamp('2003-07-15 05:00:00') nan 3.8664973524055055] + [Timestamp('2003-07-15 06:00:00') nan 4.372913492729101] + [Timestamp('2003-07-15 07:00:00') nan 7.870818418820688] + [Timestamp('2003-07-15 08:00:00') nan 9.362970613929594] + [Timestamp('2003-07-15 09:00:00') nan 3.3739997419024705] + [Timestamp('2003-07-15 10:00:00') nan 7.366972783925554] + [Timestamp('2003-07-15 11:00:00') nan 4.872920752714473] + [Timestamp('2003-07-15 12:00:00') nan 5.3738346520559555] + [Timestamp('2003-07-15 13:00:00') nan 10.376568797913327] + [Timestamp('2003-07-15 14:00:00') nan 8.870436343946784] + [Timestamp('2003-07-15 15:00:00') nan 10.367264271103313] + [Timestamp('2003-07-15 16:00:00') nan 2.3720053856723102] + [Timestamp('2003-07-15 17:00:00') nan 4.367037996693735] + [Timestamp('2003-07-15 18:00:00') nan 5.368235187970484] + [Timestamp('2003-07-15 19:00:00') nan 10.36692080725473] + [Timestamp('2003-07-15 20:00:00') nan 9.873566658551736] + [Timestamp('2003-07-15 21:00:00') nan 2.872877607961491] + [Timestamp('2003-07-15 22:00:00') nan 3.8748940333634323] + [Timestamp('2003-07-15 23:00:00') nan 6.8689062908194005] + [Timestamp('2003-07-16 00:00:00') nan 1.3717179200208642] + [Timestamp('2003-07-16 01:00:00') nan 3.8664973524055055] + [Timestamp('2003-07-16 02:00:00') nan 4.372913492729101] + [Timestamp('2003-07-16 03:00:00') nan 7.870818418820688] + [Timestamp('2003-07-16 04:00:00') nan 9.362970613929594] + [Timestamp('2003-07-16 05:00:00') nan 3.3739997419024705] + [Timestamp('2003-07-16 06:00:00') nan 7.366972783925554] + [Timestamp('2003-07-16 07:00:00') nan 4.872920752714473] + [Timestamp('2003-07-16 08:00:00') nan 5.3738346520559555] + [Timestamp('2003-07-16 09:00:00') nan 10.376568797913327] + [Timestamp('2003-07-16 10:00:00') nan 8.870436343946784] + [Timestamp('2003-07-16 11:00:00') nan 10.367264271103313] + [Timestamp('2003-07-16 12:00:00') nan 2.3720053856723102] + [Timestamp('2003-07-16 13:00:00') nan 4.367037996693735] + [Timestamp('2003-07-16 14:00:00') nan 5.368235187970484] + [Timestamp('2003-07-16 15:00:00') nan 10.36692080725473] + [Timestamp('2003-07-16 16:00:00') nan 9.873566658551736] + [Timestamp('2003-07-16 17:00:00') nan 2.872877607961491] + [Timestamp('2003-07-16 18:00:00') nan 3.8748940333634323] + [Timestamp('2003-07-16 19:00:00') nan 6.8689062908194005]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 40, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-07-15 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 30988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08066263584421943", + "MAPE": "0.0176", + "MASE": "0.026", + "RMSE": "0.1007581745552993" + } + } +} + + + + + + +{"Date":{"30988":"2003-07-15T04:00:00.000Z","30989":"2003-07-15T05:00:00.000Z","30990":"2003-07-15T06:00:00.000Z","30991":"2003-07-15T07:00:00.000Z","30992":"2003-07-15T08:00:00.000Z","30993":"2003-07-15T09:00:00.000Z","30994":"2003-07-15T10:00:00.000Z","30995":"2003-07-15T11:00:00.000Z","30996":"2003-07-15T12:00:00.000Z","30997":"2003-07-15T13:00:00.000Z","30998":"2003-07-15T14:00:00.000Z","30999":"2003-07-15T15:00:00.000Z","31000":"2003-07-15T16:00:00.000Z","31001":"2003-07-15T17:00:00.000Z","31002":"2003-07-15T18:00:00.000Z","31003":"2003-07-15T19:00:00.000Z","31004":"2003-07-15T20:00:00.000Z","31005":"2003-07-15T21:00:00.000Z","31006":"2003-07-15T22:00:00.000Z","31007":"2003-07-15T23:00:00.000Z","31008":"2003-07-16T00:00:00.000Z","31009":"2003-07-16T01:00:00.000Z","31010":"2003-07-16T02:00:00.000Z","31011":"2003-07-16T03:00:00.000Z","31012":"2003-07-16T04:00:00.000Z","31013":"2003-07-16T05:00:00.000Z","31014":"2003-07-16T06:00:00.000Z","31015":"2003-07-16T07:00:00.000Z","31016":"2003-07-16T08:00:00.000Z","31017":"2003-07-16T09:00:00.000Z","31018":"2003-07-16T10:00:00.000Z","31019":"2003-07-16T11:00:00.000Z","31020":"2003-07-16T12:00:00.000Z","31021":"2003-07-16T13:00:00.000Z","31022":"2003-07-16T14:00:00.000Z","31023":"2003-07-16T15:00:00.000Z","31024":"2003-07-16T16:00:00.000Z","31025":"2003-07-16T17:00:00.000Z","31026":"2003-07-16T18:00:00.000Z","31027":"2003-07-16T19:00:00.000Z"},"Signal":{"30988":null,"30989":null,"30990":null,"30991":null,"30992":null,"30993":null,"30994":null,"30995":null,"30996":null,"30997":null,"30998":null,"30999":null,"31000":null,"31001":null,"31002":null,"31003":null,"31004":null,"31005":null,"31006":null,"31007":null,"31008":null,"31009":null,"31010":null,"31011":null,"31012":null,"31013":null,"31014":null,"31015":null,"31016":null,"31017":null,"31018":null,"31019":null,"31020":null,"31021":null,"31022":null,"31023":null,"31024":null,"31025":null,"31026":null,"31027":null},"Signal_Forecast":{"30988":1.37171792,"30989":3.8664973524,"30990":4.3729134927,"30991":7.8708184188,"30992":9.3629706139,"30993":3.3739997419,"30994":7.3669727839,"30995":4.8729207527,"30996":5.3738346521,"30997":10.3765687979,"30998":8.8704363439,"30999":10.3672642711,"31000":2.3720053857,"31001":4.3670379967,"31002":5.368235188,"31003":10.3669208073,"31004":9.8735666586,"31005":2.872877608,"31006":3.8748940334,"31007":6.8689062908,"31008":1.37171792,"31009":3.8664973524,"31010":4.3729134927,"31011":7.8708184188,"31012":9.3629706139,"31013":3.3739997419,"31014":7.3669727839,"31015":4.8729207527,"31016":5.3738346521,"31017":10.3765687979,"31018":8.8704363439,"31019":10.3672642711,"31020":2.3720053857,"31021":4.3670379967,"31022":5.368235188,"31023":10.3669208073,"31024":9.8735666586,"31025":2.872877608,"31026":3.8748940334,"31027":6.8689062908}} + + + +TEST_CYCLES_END 20 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_200.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_200.log new file mode 100644 index 000000000..7a71c3f44 --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_200.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 31000 200 +GENERATING_RANDOM_DATASET Signal_31000_H_0_constant_200_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 145.12213134765625 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-10-16T13:00:00.000000 TimeDelta= Horizon=400 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=30988 Min=1.0 Max=11.417388536167453 Mean=6.254449304069785 StdDev=2.937631769520396 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.417388536167453 Mean=6.254449304069785 StdDev=2.937631769520396 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0174 MAPE_Forecast=0.0179 MAPE_Test=0.0179 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0173 SMAPE_Forecast=0.0179 SMAPE_Test=0.0179 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0218 MASE_Forecast=0.0226 MASE_Test=0.0209 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07811050319131498 L1_Forecast=0.0805869564665574 L1_Test=0.07484159811055752 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09956544997200749 L2_Forecast=0.10109899777038611 L2_Test=0.09507457361950544 +INFO:pyaf.std:MODEL_COMPLEXITY 8 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'_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 31388 entries, 0 to 31387 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 31388 non-null datetime64[ns] + 1 Signal 30988 non-null float64 + 2 Signal_Forecast 31388 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 735.8 KB +None +Forecasts + [[Timestamp('2003-07-15 04:00:00') nan 7.302401127811647] + [Timestamp('2003-07-15 05:00:00') nan 6.719265056965963] + [Timestamp('2003-07-15 06:00:00') nan 5.0582693420483515] + ... + [Timestamp('2003-07-31 17:00:00') nan 10.998741584601] + [Timestamp('2003-07-31 18:00:00') nan 8.208539866499425] + [Timestamp('2003-07-31 19:00:00') nan 5.576633069837807]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 400, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-07-15 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 30988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.0805869564665574", + "MAPE": "0.0179", + "MASE": "0.0226", + "RMSE": "0.10109899777038611" + } + } +} + + + + + + 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+INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 27.29106092453003 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 31508 entries, 0 to 31507 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 31508 non-null datetime64[ns] + 1 Signal 30988 non-null float64 + 2 Signal_Forecast 31508 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 738.6 KB +None +Forecasts + [[Timestamp('2003-07-15 04:00:00') nan 5.946317038741675] + [Timestamp('2003-07-15 05:00:00') nan 4.511338587239574] + [Timestamp('2003-07-15 06:00:00') nan 9.077167768502466] + ... + [Timestamp('2003-08-05 17:00:00') nan 4.780036394352374] + [Timestamp('2003-08-05 18:00:00') nan 5.0858691691689275] + [Timestamp('2003-08-05 19:00:00') nan 3.3499750945946234]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 520, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-07-15 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 30988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08123706196829242", + "MAPE": "0.0176", + "MASE": "0.0248", + "RMSE": "0.10137502116603808" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 260 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_320.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_320.log new file mode 100644 index 000000000..ab06eff92 --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_320.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 31000 320 +GENERATING_RANDOM_DATASET Signal_31000_H_0_constant_320_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 223.58812952041626 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-10-08T13:00:00.000000 TimeDelta= Horizon=640 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=30988 Min=1.0 Max=11.37885166200663 Mean=6.230149936993148 StdDev=2.886231461222299 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 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-4.877500569605093, 304: 3.7923629855738428, 305: -2.7045451422109017, 306: 4.593027181885234, 307: 1.810355259437979, 308: 4.5669882876060095, 309: 2.0881964951145298, 310: 3.5509763749358916, 311: 4.035568569970638, 312: -3.4240625867733825, 313: -0.055850122403317215, 314: 4.806211731580052, 315: 4.710997501307005, 316: 3.5068359177936044, 317: -3.3387733473337673, 318: 4.4808370858945965, 319: -2.100921349184145} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 35.915183782577515 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 31628 entries, 0 to 31627 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 31628 non-null datetime64[ns] + 1 Signal 30988 non-null float64 + 2 Signal_Forecast 31628 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 741.4 KB +None +Forecasts + [[Timestamp('2003-07-15 04:00:00') nan 1.9806748194641202] + [Timestamp('2003-07-15 05:00:00') nan 4.846805947980242] + [Timestamp('2003-07-15 06:00:00') nan 7.191133901033961] + ... + [Timestamp('2003-08-10 17:00:00') nan 4.513587373924956] + [Timestamp('2003-08-10 18:00:00') nan 6.355106028168004] + [Timestamp('2003-08-10 19:00:00') nan 4.162426093054827]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 640, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-07-15 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 30988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08105988831768676", + "MAPE": "0.0178", + "MASE": "0.0248", + "RMSE": "0.10129774838070872" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 320 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_380.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_380.log new file mode 100644 index 000000000..b888c16e7 --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_380.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 31000 380 +GENERATING_RANDOM_DATASET Signal_31000_H_0_constant_380_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 283.9214606285095 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-10-04T13:00:00.000000 TimeDelta= Horizon=760 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=30988 Min=1.0 Max=11.463803261556725 Mean=6.276113205270509 StdDev=2.9075448218131434 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.463803261556725 Mean=6.276113205270509 StdDev=2.9075448218131434 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0173 MAPE_Forecast=0.018 MAPE_Test=0.0172 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0173 SMAPE_Forecast=0.018 SMAPE_Test=0.0172 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0238 MASE_Forecast=0.0245 MASE_Test=0.0239 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.0790209067394528 L1_Forecast=0.0815542286352815 L1_Test=0.07924353362113146 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09989546013404906 L2_Forecast=0.10169527314632494 L2_Test=0.09978026546805867 +INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.274968787978239 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 380 0.014156272460888797 {0: 0.06842482778609105, 1: 4.393680748798846, 2: -4.81272687326318, 3: 0.49058544056375064, 4: 2.19672921843872, 5: 1.3164411478503313, 6: 2.61452028593064, 7: -2.7544533404575597, 8: -3.235516582201699, 9: -2.745551110240969, 10: 3.1896339613005376, 11: -0.00968518981092803, 12: -4.034985038019614, 13: -2.755144614039728, 14: -0.45415651211482766, 15: -2.7517946664221125, 16: 3.8129096209570754, 17: -0.7158506601326309, 18: -4.408121436071644, 19: 3.7131495502697387, 20: -3.173856825618434, 21: 1.9020471065637237, 22: -2.014806207472892, 23: 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3.3515413130813227, 343: 0.2936774913002438, 344: 0.7591060026244563, 345: 2.354827861463595, 346: 2.5256563202304454, 347: 2.0133800077367843, 348: 2.433639854999053, 349: 2.2110818334041946, 350: 3.477948992553185, 351: -4.488184893845785, 352: 0.9215333707538056, 353: 4.900200561712103, 354: -2.8862083720587925, 355: -1.5232780232859557, 356: -3.453271958404656, 357: -1.3423996464308923, 358: -4.900033655141416, 359: 1.6568130685845706, 360: -1.4647161050148059, 361: 0.4081082564582248, 362: -1.3834984131005568, 363: -4.961896827647377, 364: 4.093149868639574, 365: 2.3755929249000802, 366: -3.089842753431489, 367: 3.08353472330841, 368: 0.7080447979128719, 369: 3.0211286903266314, 370: 0.882295383460983, 371: 4.433364766771736, 372: 2.137221486257686, 373: 4.737888212183917, 374: 2.559418937332043, 375: -3.7155608056833476, 376: -0.9044608220277199, 377: 3.1973905315218305, 378: 3.114187266307998, 379: 2.1300623869810336} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 40.068180322647095 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 31748 entries, 0 to 31747 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 31748 non-null datetime64[ns] + 1 Signal 30988 non-null float64 + 2 Signal_Forecast 31748 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 744.2 KB +None +Forecasts + [[Timestamp('2003-07-15 04:00:00') nan 2.9883306534007383] + [Timestamp('2003-07-15 05:00:00') nan 8.00601304952755] + [Timestamp('2003-07-15 06:00:00') nan 7.358293118077492] + ... + [Timestamp('2003-08-15 17:00:00') nan 9.102208203856067] + [Timestamp('2003-08-15 18:00:00') nan 6.8829672727638425] + [Timestamp('2003-08-15 19:00:00') nan 1.838393271678659]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 760, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-07-15 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 30988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.0815542286352815", + "MAPE": "0.018", + "MASE": "0.0245", + "RMSE": "0.10169527314632494" + } + } +} + + + + + + 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'_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 31868 entries, 0 to 31867 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 31868 non-null datetime64[ns] + 1 Signal 30988 non-null float64 + 2 Signal_Forecast 31868 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 747.0 KB +None +Forecasts + [[Timestamp('2003-07-15 04:00:00') nan 7.356720626425816] + [Timestamp('2003-07-15 05:00:00') nan 9.218630248282393] + [Timestamp('2003-07-15 06:00:00') nan 7.130309005308099] + ... + [Timestamp('2003-08-20 17:00:00') nan 5.311217577778692] + [Timestamp('2003-08-20 18:00:00') nan 8.541938305117526] + [Timestamp('2003-08-20 19:00:00') nan 8.41562468501282]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 880, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2003-07-15 03:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 30988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08184126893058219", + "MAPE": "0.0178", + "MASE": "0.0244", + "RMSE": "0.10218345406872793" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 440 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_80.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_80.log new file mode 100644 index 000000000..062071c2f --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_31000_80.log @@ -0,0 +1,260 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 31000 80 +GENERATING_RANDOM_DATASET Signal_31000_H_0_constant_80_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 107.57464694976807 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2002-10-24T13:00:00.000000 TimeDelta= Horizon=160 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=30988 Min=1.0 Max=11.638628714203735 Mean=6.37173112490205 StdDev=2.9948919625279 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.638628714203735 Mean=6.37173112490205 StdDev=2.9948919625279 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0175 MAPE_Test=0.0155 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.0175 SMAPE_Test=0.0155 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0211 MASE_Forecast=0.0213 MASE_Test=0.0193 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.08021258347148905 L1_Forecast=0.08080610315669234 L1_Test=0.0725799955530335 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10078925502027619 L2_Forecast=0.10101108276705287 L2_Test=0.09171688798437955 +INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.372004766898557 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 80 -0.13879355154450312 {0: 3.432405433739153, 1: -3.823267186131777, 2: -2.3059002160490047, 3: -0.4413699359011387, 4: 3.8127622637087617, 5: -3.4396399613374333, 6: 2.320706395408318, 7: 3.183646135755917, 8: -0.04322658281055691, 9: 0.8020930586948625, 10: -0.30368374448822966, 11: -1.8172844807333526, 12: 4.674085756621136, 13: 4.071396477496783, 14: -3.817204137727175, 15: -2.419477702308386, 16: 3.6967860424787693, 17: 4.969047505630258, 18: 0.9446826338808529, 19: 3.057175321934145, 20: 1.1897479756152043, 21: -1.3065907408889155, 22: -2.5644139447686882, 23: -2.564970036744581, 24: -3.1748549283933514, 25: -0.05908020185573637, 26: -0.9265453461635165, 27: 3.203525174639587, 28: -3.8144316122369113, 29: 2.187537132922004, 30: -0.9302010707576027, 31: -1.0670227815800177, 32: 4.302554303461045, 33: -2.0447051542155132, 34: -0.5601062451713883, 35: 4.440794088293351, 36: 1.9357255003975435, 37: -1.446161746231132, 38: -0.6872273722584703, 39: -4.934261106467457, 40: -4.926579348602235, 41: -0.4403174637242966, 42: 1.6790065048348808, 43: -4.323357418815861, 44: -0.17917476401043775, 45: -2.8056138095287393, 46: 4.936785594675395, 47: -4.4416371511241435, 48: 0.3132277986428802, 49: 2.3097811077337616, 50: -1.0586436460298176, 51: -4.810116533182184, 52: 3.188915563383129, 53: 0.17814882914960162, 54: 2.177423362647735, 55: -0.5634550884288823, 56: -3.554655268240758, 57: 0.8132467854104615, 58: -4.945043440675812, 59: -3.197734230172383, 60: 1.6863973810136752, 61: -3.4387363615543904, 62: 0.308204885287493, 63: 4.4431966471394215, 64: 3.5565263550063726, 65: -4.180597401629927, 66: 3.5675825625714, 67: 0.9262540693840782, 68: -4.568403724257093, 69: 4.567954321731829, 70: 1.55737964615128, 71: 4.821721899237808, 72: -3.0630593497138703, 73: -2.4405582239388286, 74: 2.3211321922068375, 75: -2.0618972322151623, 76: 4.936451485595317, 77: -3.30126359040986, 78: 1.0733795159336275, 79: 1.1862537453120234} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 17.77501630783081 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 31148 entries, 0 to 31147 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 31148 non-null datetime64[ns] + 1 Signal 30988 non-null float64 + 2 Signal_Forecast 31148 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 730.2 KB +None +Forecasts + [[Timestamp('2003-07-15 04:00:00') nan 2.5575731546616454] + [Timestamp('2003-07-15 05:00:00') nan 8.55954189982056] + [Timestamp('2003-07-15 06:00:00') nan 5.441803696140954] + [Timestamp('2003-07-15 07:00:00') nan 5.304981985318539] + [Timestamp('2003-07-15 08:00:00') nan 10.674559070359603] + [Timestamp('2003-07-15 09:00:00') nan 4.327299612683044] + [Timestamp('2003-07-15 10:00:00') nan 5.811898521727168] + [Timestamp('2003-07-15 11:00:00') nan 10.812798855191907] + [Timestamp('2003-07-15 12:00:00') nan 8.307730267296101] + [Timestamp('2003-07-15 13:00:00') nan 4.925843020667425] + [Timestamp('2003-07-15 14:00:00') nan 5.684777394640086] + [Timestamp('2003-07-15 15:00:00') nan 1.4377436604310994] + [Timestamp('2003-07-15 16:00:00') nan 1.4454254182963213] + [Timestamp('2003-07-15 17:00:00') nan 5.93168730317426] + [Timestamp('2003-07-15 18:00:00') nan 8.051011271733437] + [Timestamp('2003-07-15 19:00:00') nan 2.048647348082696] + [Timestamp('2003-07-15 20:00:00') nan 6.192830002888119] + [Timestamp('2003-07-15 21:00:00') nan 3.5663909573698174] + [Timestamp('2003-07-15 22:00:00') nan 11.308790361573951] + [Timestamp('2003-07-15 23:00:00') nan 1.9303676157744132] + [Timestamp('2003-07-16 00:00:00') nan 6.685232565541437] + [Timestamp('2003-07-16 01:00:00') nan 8.681785874632318] + [Timestamp('2003-07-16 02:00:00') nan 5.3133611208687395] + [Timestamp('2003-07-16 03:00:00') nan 1.561888233716373] + [Timestamp('2003-07-16 04:00:00') nan 9.560920330281686] + [Timestamp('2003-07-16 05:00:00') nan 6.550153596048158] + [Timestamp('2003-07-16 06:00:00') nan 8.549428129546293] + [Timestamp('2003-07-16 07:00:00') nan 5.808549678469674] + [Timestamp('2003-07-16 08:00:00') nan 2.8173494986577987] + [Timestamp('2003-07-16 09:00:00') nan 7.185251552309018] + [Timestamp('2003-07-16 10:00:00') nan 1.4269613262227443] + [Timestamp('2003-07-16 11:00:00') nan 3.1742705367261737] + [Timestamp('2003-07-16 12:00:00') nan 8.058402147912233] + [Timestamp('2003-07-16 13:00:00') nan 2.933268405344166] + [Timestamp('2003-07-16 14:00:00') nan 6.68020965218605] + [Timestamp('2003-07-16 15:00:00') nan 10.815201414037979] + [Timestamp('2003-07-16 16:00:00') nan 9.928531121904928] + [Timestamp('2003-07-16 17:00:00') nan 2.1914073652686294] + 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nan 9.429180088832702] + [Timestamp('2003-07-18 04:00:00') nan 7.561752742513761] + [Timestamp('2003-07-18 05:00:00') nan 5.065414026009641] + [Timestamp('2003-07-18 06:00:00') nan 3.8075908221298684] + [Timestamp('2003-07-18 07:00:00') nan 3.8070347301539758] + [Timestamp('2003-07-18 08:00:00') nan 3.1971498385052053] + [Timestamp('2003-07-18 09:00:00') nan 6.31292456504282] + [Timestamp('2003-07-18 10:00:00') nan 5.44545942073504] + [Timestamp('2003-07-18 11:00:00') nan 9.575529941538143] + [Timestamp('2003-07-18 12:00:00') nan 2.5575731546616454] + [Timestamp('2003-07-18 13:00:00') nan 8.55954189982056] + [Timestamp('2003-07-18 14:00:00') nan 5.441803696140954] + [Timestamp('2003-07-18 15:00:00') nan 5.304981985318539] + [Timestamp('2003-07-18 16:00:00') nan 10.674559070359603] + [Timestamp('2003-07-18 17:00:00') nan 4.327299612683044] + [Timestamp('2003-07-18 18:00:00') nan 5.811898521727168] + [Timestamp('2003-07-18 19:00:00') nan 10.812798855191907] + [Timestamp('2003-07-18 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1.1284449888577406, 65: -1.5180229570091317, 66: -1.4463151658513063, 67: -0.01981385725466467, 68: 1.837505019177657, 69: 1.7710489526984006, 70: -4.942964006678867, 71: 3.125145923763137, 72: -2.363161843915256, 73: -1.5179977496359953, 74: 1.6954953252228666, 75: 4.404038849012096, 76: 2.0390501951261033, 77: -1.931273339991332, 78: 3.047450707760124, 79: 4.130410784616972, 80: -4.956165523219616, 81: 4.901958199798882, 82: 3.2001680859219004, 83: -1.8716593351961635, 84: 4.109867718597842, 85: -3.3016440251331844, 86: 4.335140106459059, 87: 3.7015036907119683, 88: 2.042168882917375, 89: -0.5325715482384097, 90: 3.833705979026175, 91: 0.9231548628879955, 92: -1.3741845339851464, 93: -3.957335349882346, 94: -2.0224764491184364, 95: -0.7889184660469528, 96: -2.3618899994139486, 97: -4.230125143945942, 98: 1.1893830431566426, 99: -1.875245606749838, 100: 2.4789171944015616, 101: -4.161842517404995, 102: -4.795504481075401, 103: -1.315550436818707, 104: 0.7651916314282636, 105: -2.657219697679542, 106: 4.189270446556655, 107: -2.2248131578140447, 108: -3.593575406385038, 109: -1.9606785992517617, 110: 3.2678197335544494, 111: 0.546495890328949, 112: -2.808733175413211, 113: -3.2401234728981683, 114: 3.9920267239374523, 115: -4.807971399395374, 116: -4.751722466896611, 117: 1.7621834697970744, 118: 2.6944471273269137, 119: -4.021499960470232, 120: 3.0520199353015487, 121: -2.099126851944214, 122: 0.17764341791904625, 123: -3.5917398801700537, 124: 1.837481995537022, 125: 0.20541088513137584, 126: -0.1671720301387567, 127: -0.5843290961273708, 128: -3.9572776794779756, 129: 1.9108982603778415, 130: -4.661770983944038, 131: 4.984894991648621, 132: 1.205429374960377, 133: 3.680963975920819, 134: 2.85125005918725, 135: 0.4114769375927594, 136: -3.8250632117372896, 137: 2.899025143549011, 138: -3.1665503066246217, 139: 3.694938266345308} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 20.95918321609497 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 41268 entries, 0 to 41267 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 41268 non-null datetime64[ns] + 1 Signal 40988 non-null float64 + 2 Signal_Forecast 41268 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 967.3 KB +None +Forecasts + [[Timestamp('2004-09-03 20:00:00') nan 2.6969873820039423] + [Timestamp('2004-09-03 21:00:00') nan 4.329884189137219] + [Timestamp('2004-09-03 22:00:00') nan 9.558382521943429] + [Timestamp('2004-09-03 23:00:00') nan 6.837058678717929] + [Timestamp('2004-09-04 00:00:00') nan 3.481829612975769] + [Timestamp('2004-09-04 01:00:00') nan 3.050439315490812] + [Timestamp('2004-09-04 02:00:00') nan 10.282589512326432] + [Timestamp('2004-09-04 03:00:00') nan 1.4825913889936064] + [Timestamp('2004-09-04 04:00:00') nan 1.5388403214923692] + [Timestamp('2004-09-04 05:00:00') nan 8.052746258186055] + [Timestamp('2004-09-04 06:00:00') nan 8.985009915715894] + [Timestamp('2004-09-04 07:00:00') nan 2.2690628279187486] + [Timestamp('2004-09-04 08:00:00') nan 9.34258272369053] + [Timestamp('2004-09-04 09:00:00') nan 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+INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 5.383009672164917 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 41028 entries, 0 to 41027 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 41028 non-null datetime64[ns] + 1 Signal 40988 non-null float64 + 2 Signal_Forecast 41028 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 961.7 KB +None +Forecasts + [[Timestamp('2004-09-03 20:00:00') nan 1.3697617994015108] + [Timestamp('2004-09-03 21:00:00') nan 3.866142830047128] + [Timestamp('2004-09-03 22:00:00') nan 4.372405900409069] + [Timestamp('2004-09-03 23:00:00') nan 7.870232447915551] + [Timestamp('2004-09-04 00:00:00') nan 9.362679800360283] + [Timestamp('2004-09-04 01:00:00') nan 3.3709928538582696] + [Timestamp('2004-09-04 02:00:00') nan 7.367908249019161] + [Timestamp('2004-09-04 03:00:00') nan 4.872416504209212] + [Timestamp('2004-09-04 04:00:00') nan 5.3711712446513875] + [Timestamp('2004-09-04 05:00:00') nan 10.375616195575372] + [Timestamp('2004-09-04 06:00:00') nan 8.869991450765426] + [Timestamp('2004-09-04 07:00:00') nan 10.366291312156536] + [Timestamp('2004-09-04 08:00:00') nan 2.3716330548380595] + [Timestamp('2004-09-04 09:00:00') nan 4.368056476153526] + [Timestamp('2004-09-04 10:00:00') nan 5.367695439309716] + [Timestamp('2004-09-04 11:00:00') nan 10.370044763223639] + [Timestamp('2004-09-04 12:00:00') nan 9.871744500958561] + [Timestamp('2004-09-04 13:00:00') nan 2.872735213888032] + [Timestamp('2004-09-04 14:00:00') nan 3.8748940333634323] + [Timestamp('2004-09-04 15:00:00') nan 6.872218192870298] + [Timestamp('2004-09-04 16:00:00') nan 1.3697617994015108] + [Timestamp('2004-09-04 17:00:00') nan 3.866142830047128] + [Timestamp('2004-09-04 18:00:00') nan 4.372405900409069] + [Timestamp('2004-09-04 19:00:00') nan 7.870232447915551] + [Timestamp('2004-09-04 20:00:00') nan 9.362679800360283] + [Timestamp('2004-09-04 21:00:00') nan 3.3709928538582696] + [Timestamp('2004-09-04 22:00:00') nan 7.367908249019161] + [Timestamp('2004-09-04 23:00:00') nan 4.872416504209212] + [Timestamp('2004-09-05 00:00:00') nan 5.3711712446513875] + [Timestamp('2004-09-05 01:00:00') nan 10.375616195575372] + [Timestamp('2004-09-05 02:00:00') nan 8.869991450765426] + [Timestamp('2004-09-05 03:00:00') nan 10.366291312156536] + [Timestamp('2004-09-05 04:00:00') nan 2.3716330548380595] + [Timestamp('2004-09-05 05:00:00') nan 4.368056476153526] + [Timestamp('2004-09-05 06:00:00') nan 5.367695439309716] + [Timestamp('2004-09-05 07:00:00') nan 10.370044763223639] + [Timestamp('2004-09-05 08:00:00') nan 9.871744500958561] + [Timestamp('2004-09-05 09:00:00') nan 2.872735213888032] + [Timestamp('2004-09-05 10:00:00') nan 3.8748940333634323] + [Timestamp('2004-09-05 11:00:00') nan 6.872218192870298]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 40, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2004-09-03 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 40988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.07921539043673723", + "MAPE": "0.0174", + "MASE": "0.0256", + "RMSE": "0.09948495443297016" + } + } +} + + + + + + +{"Date":{"40988":"2004-09-03T20:00:00.000Z","40989":"2004-09-03T21:00:00.000Z","40990":"2004-09-03T22:00:00.000Z","40991":"2004-09-03T23:00:00.000Z","40992":"2004-09-04T00:00:00.000Z","40993":"2004-09-04T01:00:00.000Z","40994":"2004-09-04T02:00:00.000Z","40995":"2004-09-04T03:00:00.000Z","40996":"2004-09-04T04:00:00.000Z","40997":"2004-09-04T05:00:00.000Z","40998":"2004-09-04T06:00:00.000Z","40999":"2004-09-04T07:00:00.000Z","41000":"2004-09-04T08:00:00.000Z","41001":"2004-09-04T09:00:00.000Z","41002":"2004-09-04T10:00:00.000Z","41003":"2004-09-04T11:00:00.000Z","41004":"2004-09-04T12:00:00.000Z","41005":"2004-09-04T13:00:00.000Z","41006":"2004-09-04T14:00:00.000Z","41007":"2004-09-04T15:00:00.000Z","41008":"2004-09-04T16:00:00.000Z","41009":"2004-09-04T17:00:00.000Z","41010":"2004-09-04T18:00:00.000Z","41011":"2004-09-04T19:00:00.000Z","41012":"2004-09-04T20:00:00.000Z","41013":"2004-09-04T21:00:00.000Z","41014":"2004-09-04T22:00:00.000Z","41015":"2004-09-04T23:00:00.000Z","41016":"2004-09-05T00:00:00.000Z","41017":"2004-09-05T01:00:00.000Z","41018":"2004-09-05T02:00:00.000Z","41019":"2004-09-05T03:00:00.000Z","41020":"2004-09-05T04:00:00.000Z","41021":"2004-09-05T05:00:00.000Z","41022":"2004-09-05T06:00:00.000Z","41023":"2004-09-05T07:00:00.000Z","41024":"2004-09-05T08:00:00.000Z","41025":"2004-09-05T09:00:00.000Z","41026":"2004-09-05T10:00:00.000Z","41027":"2004-09-05T11:00:00.000Z"},"Signal":{"40988":null,"40989":null,"40990":null,"40991":null,"40992":null,"40993":null,"40994":null,"40995":null,"40996":null,"40997":null,"40998":null,"40999":null,"41000":null,"41001":null,"41002":null,"41003":null,"41004":null,"41005":null,"41006":null,"41007":null,"41008":null,"41009":null,"41010":null,"41011":null,"41012":null,"41013":null,"41014":null,"41015":null,"41016":null,"41017":null,"41018":null,"41019":null,"41020":null,"41021":null,"41022":null,"41023":null,"41024":null,"41025":null,"41026":null,"41027":null},"Signal_Forecast":{"40988":1.3697617994,"40989":3.86614283,"40990":4.3724059004,"40991":7.8702324479,"40992":9.3626798004,"40993":3.3709928539,"40994":7.367908249,"40995":4.8724165042,"40996":5.3711712447,"40997":10.3756161956,"40998":8.8699914508,"40999":10.3662913122,"41000":2.3716330548,"41001":4.3680564762,"41002":5.3676954393,"41003":10.3700447632,"41004":9.871744501,"41005":2.8727352139,"41006":3.8748940334,"41007":6.8722181929,"41008":1.3697617994,"41009":3.86614283,"41010":4.3724059004,"41011":7.8702324479,"41012":9.3626798004,"41013":3.3709928539,"41014":7.367908249,"41015":4.8724165042,"41016":5.3711712447,"41017":10.3756161956,"41018":8.8699914508,"41019":10.3662913122,"41020":2.3716330548,"41021":4.3680564762,"41022":5.3676954393,"41023":10.3700447632,"41024":9.871744501,"41025":2.8727352139,"41026":3.8748940334,"41027":6.8722181929}} + + + +TEST_CYCLES_END 20 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_200.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_200.log new file mode 100644 index 000000000..24361cdc7 --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_200.log @@ -0,0 +1,107 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 41000 200 +GENERATING_RANDOM_DATASET Signal_41000_H_0_constant_200_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 237.99244594573975 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2003-09-14T21:00:00.000000 TimeDelta= Horizon=400 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=40988 Min=1.0 Max=11.419359695396068 Mean=6.2569139836431225 StdDev=2.937564113656013 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.419359695396068 Mean=6.2569139836431225 StdDev=2.937564113656013 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.0178 MAPE_Test=0.017 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0175 SMAPE_Forecast=0.0178 SMAPE_Test=0.017 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0221 MASE_Forecast=0.0222 MASE_Test=0.0212 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.07905847933324466 L1_Forecast=0.07951206746194601 L1_Test=0.07583069602410518 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.09967988432333363 L2_Forecast=0.1001841257723618 L2_Test=0.0943982225163407 +INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.25676964787203 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 200 -0.03234903922900978 {0: 4.766049852212221, 1: 0.1355451515369932, 2: -4.545723841901707, 3: -3.9421382463039647, 4: -3.183555772240907, 5: -0.6340927243447458, 6: -1.4922518171308425, 7: -0.5915535704927546, 8: 1.9893174766468746, 9: -2.095031496639276, 10: 4.647353440375603, 11: -3.0501770488531914, 12: -0.643840273845699, 13: 3.7064147437876187, 14: -0.592116073614763, 15: -0.9435468535221583, 16: 3.24851890093319, 17: -3.746870670116093, 18: -1.1473374203820912, 19: -1.3816463643668868, 20: -4.552779308240323, 21: 2.3998423111931757, 22: 0.7520694244041266, 23: 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-4.784867006880733, 185: 4.760716495247273, 186: 1.9615508393940173, 187: -0.6872185955683343, 188: 1.043785444163413, 189: 0.45985252022533496, 190: -1.2394793611960422, 191: 4.197364391909981, 192: -4.197770536744226, 193: 2.6005343658754985, 194: 2.858648127856295, 195: 2.460054118769957, 196: 0.5122751289713583, 197: -3.7347650214576946, 198: 4.4124998692890545, 199: 1.0689973012242655} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 33.07526421546936 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 41388 entries, 0 to 41387 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 41388 non-null datetime64[ns] + 1 Signal 40988 non-null float64 + 2 Signal_Forecast 41388 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 970.2 KB +None +Forecasts + [[Timestamp('2004-09-03 20:00:00') nan 7.300555092035443] + [Timestamp('2004-09-03 21:00:00') nan 6.716622168097365] + [Timestamp('2004-09-03 22:00:00') nan 5.017290286675988] + ... + [Timestamp('2004-09-20 09:00:00') nan 11.017486143119303] + [Timestamp('2004-09-20 10:00:00') nan 8.218320487266048] + [Timestamp('2004-09-20 11:00:00') nan 5.569551052303696]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 400, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2004-09-03 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 40988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.07951206746194601", + "MAPE": "0.0178", + "MASE": "0.0222", + "RMSE": "0.1001841257723618" + } + } +} + + + + + + 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+INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 39.68151903152466 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 41508 entries, 0 to 41507 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 41508 non-null datetime64[ns] + 1 Signal 40988 non-null float64 + 2 Signal_Forecast 41508 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 973.0 KB +None +Forecasts + [[Timestamp('2004-09-03 20:00:00') nan 11.280357060141721] + [Timestamp('2004-09-03 21:00:00') nan 2.360455826353618] + [Timestamp('2004-09-03 22:00:00') nan 8.958237593749187] + ... + [Timestamp('2004-09-25 09:00:00') nan 2.6519999478489793] + [Timestamp('2004-09-25 10:00:00') nan 4.129467576080689] + [Timestamp('2004-09-25 11:00:00') nan 9.69289236213211]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 520, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2004-09-03 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 40988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + 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-4.885347380565145, 304: 3.7936724500169063, 305: -2.707566177879377, 306: 4.598683925359054, 307: 1.8202621531298266, 308: 4.569185518154574, 309: 2.088533620873579, 310: 3.5497623018830877, 311: 4.0268491652951495, 312: -3.428530365097544, 313: -0.06950929253302718, 314: 4.805875774600761, 315: 4.710716710414503, 316: 3.507995569551732, 317: -3.348867786109121, 318: 4.483022357447484, 319: -2.0858243715557387} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 50.8564031124115 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 41628 entries, 0 to 41627 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 41628 non-null datetime64[ns] + 1 Signal 40988 non-null float64 + 2 Signal_Forecast 41628 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 975.8 KB +None +Forecasts + [[Timestamp('2004-09-03 20:00:00') nan 10.296620766945267] + [Timestamp('2004-09-03 21:00:00') nan 9.575532075671285] + [Timestamp('2004-09-03 22:00:00') nan 7.064604852251904] + ... + [Timestamp('2004-09-30 09:00:00') nan 10.198424176145995] + [Timestamp('2004-09-30 10:00:00') nan 5.888854800974208] + [Timestamp('2004-09-30 11:00:00') nan 5.889926653103677]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 640, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2004-09-03 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 40988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08052075233158258", + "MAPE": "0.0174", + "MASE": "0.0246", + "RMSE": "0.10090199897798338" + } + } +} + + + + + + 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'_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 41748 entries, 0 to 41747 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 41748 non-null datetime64[ns] + 1 Signal 40988 non-null float64 + 2 Signal_Forecast 41748 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 978.6 KB +None +Forecasts + [[Timestamp('2004-09-03 20:00:00') nan 10.34161778617565] + [Timestamp('2004-09-03 21:00:00') nan 3.668538603693376] + [Timestamp('2004-09-03 22:00:00') nan 11.178371684606415] + ... + [Timestamp('2004-10-05 09:00:00') nan 6.336814685338754] + [Timestamp('2004-10-05 10:00:00') nan 9.264079893940508] + [Timestamp('2004-10-05 11:00:00') nan 4.754209155127051]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 760, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2004-09-03 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 40988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08203351258860131", + "MAPE": "0.0179", + "MASE": "0.0247", + "RMSE": "0.10261856151366805" + } + } +} + + + + + + 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+INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 106.62318897247314 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 41868 entries, 0 to 41867 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 41868 non-null datetime64[ns] + 1 Signal 40988 non-null float64 + 2 Signal_Forecast 41868 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 981.4 KB +None +Forecasts + [[Timestamp('2004-09-03 20:00:00') nan 10.887441304473493] + [Timestamp('2004-09-03 21:00:00') nan 2.5240006891844597] + [Timestamp('2004-09-03 22:00:00') nan 7.8959018886414425] + ... + [Timestamp('2004-10-10 09:00:00') nan 9.802925115940546] + [Timestamp('2004-10-10 10:00:00') nan 7.149792193835002] + [Timestamp('2004-10-10 11:00:00') nan 8.547417749206753]] + + + +{ + "Signal": { + "Dataset": { + "Signal": "Signal", + "Time": { + "Horizon": 880, + "TimeMinMax": [ + "2000-01-01 00:00:00", + "2004-09-03 19:00:00" + ], + "TimeVariable": "Date" + }, + "Training_Signal_Length": 40988 + }, + "Model": { + "AR_Model": "NoAR", + "Best_Decomposition": "_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR", + "Cycle": "Cycle", + "Signal_Transoformation": "NoTransf", + "Trend": "ConstantTrend" + }, + "Model_Performance": { + "COMPLEXITY": "8", + "MAE": "0.08150723255975946", + "MAPE": "0.0179", + "MASE": "0.0243", + "RMSE": "0.10212839463763229" + } + } +} + + + + + + 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+ + + +TEST_CYCLES_END 440 diff --git a/tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_80.log b/tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_80.log new file mode 100644 index 000000000..9e9d86aef --- /dev/null +++ b/tests/references/perf_test_long_cycles_nbrows_cycle_length_41000_80.log @@ -0,0 +1,260 @@ +INFO:pyaf.std:START_TRAINING 'Signal' +TEST_CYCLES_START 41000 80 +GENERATING_RANDOM_DATASET Signal_41000_H_0_constant_80_None_0.1_0 +TREND 0.0976270078546495 0.43037873274483895 0.20552675214328775 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Signal']' 139.1737322807312 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Date' TimeMin=2000-01-01T00:00:00.000000 TimeMax=2003-09-22T21:00:00.000000 TimeDelta= Horizon=160 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Signal' Length=40988 Min=1.0 Max=11.638628714203735 Mean=6.371410585994418 StdDev=2.995259801621353 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Signal' Min=1.0 Max=11.638628714203735 Mean=6.371410585994418 StdDev=2.995259801621353 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [ConstantTrend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL '_Signal_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0176 MAPE_Forecast=0.017 MAPE_Test=0.0155 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0176 SMAPE_Forecast=0.017 SMAPE_Test=0.0155 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0212 MASE_Forecast=0.0209 MASE_Test=0.0189 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.08033238084906295 L1_Forecast=0.07942443453116882 L1_Test=0.07149087565855855 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.10080271456929532 L2_Forecast=0.099800195740442 L2_Test=0.0906610398647148 +INFO:pyaf.std:MODEL_COMPLEXITY 8 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 6.371538169960517 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES _Signal_ConstantTrend_residue_bestCycle_byMAPE 80 -0.13996223368563854 {0: 3.4334565240869397, 1: -3.822061756646124, 2: -2.312335792237226, 3: -0.4374425612099122, 4: 3.8114267747257697, 5: -3.4370736306470926, 6: 2.320552135535382, 7: 3.192339831909867, 8: -0.04666472496705776, 9: 0.803051854841649, 10: -0.30257799326425516, 11: -1.8228927459486473, 12: 4.680752116025176, 13: 4.06255326072558, 14: -3.8185927962053547, 15: -2.427777421586137, 16: 3.6906651213973776, 17: 4.960390022695154, 18: 0.9413325487952848, 19: 3.0621832443130463, 20: 1.1876234414145737, 21: -1.3048138945891479, 22: -2.561308076065009, 23: -2.558501700657134, 24: -3.1780861139538508, 25: -0.057957698059452056, 26: -0.9313820931260981, 27: 3.2041128815806017, 28: -3.8139650152988716, 29: 2.1849838574328446, 30: -0.9263224582413381, 31: -1.0644766080987598, 32: 4.3028567149982715, 33: -2.0464776342195017, 34: -0.5606617302130159, 35: 4.4412606852313905, 36: 1.9389104706249531, 37: -1.4454400865861654, 38: -0.6862385502200707, 39: -4.937269028011645, 40: -4.918971030377768, 41: -0.4389810120491813, 42: 1.6796610948769795, 43: -4.3196586482842, 44: -0.18420447635645676, 45: -2.8069236553211185, 46: 4.940394283015009, 47: -4.436830544737309, 48: 0.316858392247676, 49: 2.3102477046718013, 50: -1.0644358118429622, 51: -4.80942743890793, 52: 3.184887768587437, 53: 0.180114926604241, 54: 2.1802302747704223, 55: -0.5601970383457182, 56: -3.550180869919699, 57: 0.8148295176096445, 58: -4.947400560116694, 59: -3.195953067421124, 60: 1.684929480875872, 61: -3.437782190492228, 62: 0.3100411660976592, 63: 4.442001483925588, 64: 3.557278326495311, 65: -4.184822073026886, 66: 3.5667715453253868, 67: 0.9298292601196936, 68: -4.571956471773966, 69: 4.567834626941158, 70: 1.5569135076615748, 71: 4.824924867670981, 72: -3.0674984677751205, 73: -2.440091627000789, 74: 2.319503493358474, 75: -2.060794774427822, 76: 4.931949980059364, 77: -3.3016131919816774, 78: 1.0731512456298713, 79: 1.1845575087866624} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END +INFO:pyaf.std:START_FORECASTING '['Signal']' +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Signal']' 23.28403091430664 +Forecast Columns Index(['Date', 'Signal', 'row_number', 'Date_Normalized', '_Signal', + '_Signal_ConstantTrend', '_Signal_ConstantTrend_residue', + 'cycle_internal', '_Signal_ConstantTrend_residue_bestCycle_byMAPE', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR', + '_Signal_ConstantTrend_residue_bestCycle_byMAPE_residue_NoAR_residue', + '_Signal_Trend', '_Signal_Trend_residue', '_Signal_Cycle', + '_Signal_Cycle_residue', '_Signal_AR', '_Signal_AR_residue', + '_Signal_TransformedForecast', 'Signal_Forecast', + '_Signal_TransformedResidue', 'Signal_Residue', + 'Signal_Forecast_Lower_Bound', 'Signal_Forecast_Upper_Bound'], + dtype='object') + +RangeIndex: 41148 entries, 0 to 41147 +Data columns (total 3 columns): + # Column Non-Null Count Dtype +--- ------ -------------- ----- + 0 Date 41148 non-null datetime64[ns] + 1 Signal 40988 non-null float64 + 2 Signal_Forecast 41148 non-null float64 +dtypes: datetime64[ns](1), float64(2) +memory usage: 964.5 KB +None +Forecasts + [[Timestamp('2004-09-03 20:00:00') nan 2.5575731546616454] + [Timestamp('2004-09-03 21:00:00') nan 8.556522027393362] + [Timestamp('2004-09-03 22:00:00') nan 5.445215711719179] + [Timestamp('2004-09-03 23:00:00') nan 5.307061561861757] + [Timestamp('2004-09-04 00:00:00') nan 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17:00:00') nan 8.681785874632318] + [Timestamp('2004-09-04 18:00:00') nan 5.307102358117555] + [Timestamp('2004-09-04 19:00:00') nan 1.562110731052587] + [Timestamp('2004-09-04 20:00:00') nan 9.556425938547953] + [Timestamp('2004-09-04 21:00:00') nan 6.551653096564758] + [Timestamp('2004-09-04 22:00:00') nan 8.55176844473094] + [Timestamp('2004-09-04 23:00:00') nan 5.811341131614799] + [Timestamp('2004-09-05 00:00:00') nan 2.821357300040818] + [Timestamp('2004-09-05 01:00:00') nan 7.186367687570161] + [Timestamp('2004-09-05 02:00:00') nan 1.4241376098438225] + [Timestamp('2004-09-05 03:00:00') nan 3.175585102539393] + [Timestamp('2004-09-05 04:00:00') nan 8.056467650836389] + [Timestamp('2004-09-05 05:00:00') nan 2.933755979468289] + [Timestamp('2004-09-05 06:00:00') nan 6.681579336058176] + [Timestamp('2004-09-05 07:00:00') nan 10.813539653886105] + [Timestamp('2004-09-05 08:00:00') nan 9.928816496455827] + [Timestamp('2004-09-05 09:00:00') nan 2.1867160969336306] + 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+INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='CumSum_Ozone' Min=2.7 Max=39125.00000000028 Mean=19605.363725490337 StdDev=11294.348459632758 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'CumSum_' +INFO:pyaf.std:BEST_DECOMPOSITION 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [Lag1Trend + Cycle + NoAR] +INFO:pyaf.std:TREND_DETAIL 'CumSum_Ozone_Lag1Trend' [Lag1Trend] +INFO:pyaf.std:CYCLE_DETAIL 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE' [Cycle] +INFO:pyaf.std:AUTOREG_DETAIL 'CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE_residue_NoAR' [NoAR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2167 MAPE_Forecast=0.2166 MAPE_Test=0.1912 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2147 SMAPE_Forecast=0.2146 SMAPE_Test=0.2239 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.9142 MASE_Forecast=0.9136 MASE_Test=0.899 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7711411042944856 L1_Forecast=0.7710009813544707 L1_Test=0.42500000000048516 +INFO:pyaf.std:MODEL_L2 L2_Fit=1.017215011867223 L2_Forecast=1.0169394519200639 L2_Test=0.5074445782552324 +INFO:pyaf.std:MODEL_COMPLEXITY 72 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES Integration None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:LAG1_TREND Lag1Trend 2.7 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:BEST_CYCLE_LENGTH_VALUES CumSum_Ozone_Lag1Trend_residue_bestCycle_byMAPE 12 3.7999999999992724 {0: 2.099999999998545, 1: 2.400000000000091, 2: 2.7000000000007276, 3: 3.7999999999992724, 4: 3.7999999999992724, 5: 4.299999999999272, 6: 4.900000000001455, 7: 5.0, 8: 4.799999999999272, 9: 4.799999999999272, 10: 2.7999999999992724, 11: 2.2000000000007276} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_100.log b/tests/references/perf_test_ozone_ar_speed_order_100.log new file mode 100644 index 000000000..5982aa128 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_100.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 36.77148389816284 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(100)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(100)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1376 MAPE_Forecast=0.1374 MAPE_Test=0.2341 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1347 SMAPE_Forecast=0.1345 SMAPE_Test=0.2311 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.5747 MASE_Forecast=0.5732 MASE_Test=1.2078 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.4847747374430702 L1_Forecast=0.4837075610510263 L1_Test=0.5709826667757363 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.6465396535544338 L2_Forecast=0.6438252890953697 L2_Test=0.7128249778075041 +INFO:pyaf.std:MODEL_COMPLEXITY 100 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.47308502041071965 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.3186179266469543 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.23866357449829342 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.22757561427267176 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag87 0.20658693809942721 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.19716488770967844 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag97 0.17935881914959179 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.1787415712301379 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag4 0.16990491275172798 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.16719937185282224 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_150.log b/tests/references/perf_test_ozone_ar_speed_order_150.log new file mode 100644 index 000000000..7939ebc82 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_150.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 69.9300467967987 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(150)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(150)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1151 MAPE_Forecast=0.114 MAPE_Test=0.2296 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1131 SMAPE_Forecast=0.1122 SMAPE_Test=0.2202 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.4806 MASE_Forecast=0.4759 MASE_Test=1.1891 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.40537524131974373 L1_Forecast=0.4015882302738338 L1_Test=0.5621372840947728 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.5311394579412342 L2_Forecast=0.5237706567242836 L2_Test=0.6545460221337277 +INFO:pyaf.std:MODEL_COMPLEXITY 150 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.5947151721662776 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.3859792149061864 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag97 0.3470922572809153 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag44 0.32389308559970453 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag88 -0.3054561110151719 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.30297756491043626 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag140 0.27935923649387895 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag128 -0.2781261838662811 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.27636844903312185 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag110 -0.2738335556231832 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_200.log b/tests/references/perf_test_ozone_ar_speed_order_200.log new file mode 100644 index 000000000..367dfe85a --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_200.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 187.13600611686707 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(200)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(200)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0672 MAPE_Forecast=0.0632 MAPE_Test=0.0905 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0669 SMAPE_Forecast=0.0627 SMAPE_Test=0.0865 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.2671 MASE_Forecast=0.2501 MASE_Test=0.4408 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.22529254188884426 L1_Forecast=0.21107599858154158 L1_Test=0.20836525169937734 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.29870968153860633 L2_Forecast=0.2541821407606399 L2_Test=0.258529536376885 +INFO:pyaf.std:MODEL_COMPLEXITY 200 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag96 -0.4903068319692706 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag164 -0.48075663894218773 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag160 0.3970138078342637 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag39 -0.39173250861564346 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag120 0.38862497639713045 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag56 -0.38251351087420776 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag108 -0.3661613162947641 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag44 0.3592112690532089 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.3548423678376884 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag127 0.3448081547009084 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_250.log b/tests/references/perf_test_ozone_ar_speed_order_250.log new file mode 100644 index 000000000..e6caad41a --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_250.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 235.4024806022644 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(250)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(250)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0088 MAPE_Forecast=0.0049 MAPE_Test=0.0058 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0087 SMAPE_Forecast=0.0049 SMAPE_Test=0.0058 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0359 MASE_Forecast=0.0188 MASE_Test=0.0273 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.030307356380726767 L1_Forecast=0.01587382602455877 L1_Test=0.01291514469252599 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.12108092799712486 L2_Forecast=0.019760565120536696 L2_Test=0.015573438356317332 +INFO:pyaf.std:MODEL_COMPLEXITY 250 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9345105216381727 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3908829226849115 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3900384236001172 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.17928655561750645 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag219 0.16542904823177937 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag240 -0.1649857214333443 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.1621368674335584 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.14802749123571246 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.14548217659742887 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.13715339856301567 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_300.log b/tests/references/perf_test_ozone_ar_speed_order_300.log new file mode 100644 index 000000000..2c1797841 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_300.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 266.81310415267944 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(300)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(300)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0074 MAPE_Forecast=0.0033 MAPE_Test=0.0035 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0073 SMAPE_Forecast=0.0033 SMAPE_Test=0.0035 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0303 MASE_Forecast=0.013 MASE_Test=0.0179 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.025546582629409287 L1_Forecast=0.010930580520685669 L1_Test=0.008474671244716939 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.11596559712577058 L2_Forecast=0.013414584061184339 L2_Test=0.009770670685919412 +INFO:pyaf.std:MODEL_COMPLEXITY 300 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9520873812395108 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3821398619228936 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3702598513081168 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.2032383669328519 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.18746881774482826 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.1742557772849714 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.1741826520960998 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.17232761249645182 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.16277834894133203 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag85 -0.16161573238694138 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_350.log b/tests/references/perf_test_ozone_ar_speed_order_350.log new file mode 100644 index 000000000..2e5e73c27 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_350.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 282.3083961009979 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(350)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(350)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0063 MAPE_Forecast=0.0024 MAPE_Test=0.0032 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0062 SMAPE_Forecast=0.0024 SMAPE_Test=0.0032 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0264 MASE_Forecast=0.0092 MASE_Test=0.0145 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.022280038066611958 L1_Forecast=0.00778965200803154 L1_Test=0.006854847953943606 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.11456268523471376 L2_Forecast=0.009854131082537214 L2_Test=0.009323682454962772 +INFO:pyaf.std:MODEL_COMPLEXITY 350 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9618112957907636 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.34760036144942064 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3326234742087 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.20343363394757877 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.19583272105950797 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.19373059335819276 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.1923412465423171 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.18729254241253507 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.18445251122839856 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.1697970836068921 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_400.log b/tests/references/perf_test_ozone_ar_speed_order_400.log new file mode 100644 index 000000000..b7f640b30 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_400.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 316.00434947013855 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(400)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(400)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0063 MAPE_Forecast=0.0023 MAPE_Test=0.0031 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0063 SMAPE_Forecast=0.0023 SMAPE_Test=0.0031 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0264 MASE_Forecast=0.0086 MASE_Test=0.0138 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.022234340591402002 L1_Forecast=0.007277633465034995 L1_Test=0.0065443203955065825 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.11648718069403617 L2_Forecast=0.008993381111994679 L2_Test=0.0076669341328632346 +INFO:pyaf.std:MODEL_COMPLEXITY 400 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.965855337898474 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35211259058292355 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3329309664958524 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag57 -0.21785955555605618 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.21281018203223517 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.20076957777878612 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.1991869421975928 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag261 0.19660518004308275 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag10 0.1919881594806609 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag250 0.18429111157929967 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_450.log b/tests/references/perf_test_ozone_ar_speed_order_450.log new file mode 100644 index 000000000..89988525e --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_450.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 330.0016779899597 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(450)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(450)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0061 MAPE_Forecast=0.0017 MAPE_Test=0.0021 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.006 SMAPE_Forecast=0.0017 SMAPE_Test=0.0021 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0252 MASE_Forecast=0.0066 MASE_Test=0.0092 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.021288154826919578 L1_Forecast=0.0055625690332575025 L1_Test=0.004338372823267582 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.12002731921962427 L2_Forecast=0.006907274160356605 L2_Test=0.005147831054884069 +INFO:pyaf.std:MODEL_COMPLEXITY 450 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9164592102191809 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.39418365179958204 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3325727795745933 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.2295027110769384 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.22283086782395337 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag57 -0.21882857709552916 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag100 0.2106733893492524 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.20812093651070762 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.20432243605700132 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.20320566537770263 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_50.log b/tests/references/perf_test_ozone_ar_speed_order_50.log new file mode 100644 index 000000000..b4ecbe11c --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_50.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 15.82571530342102 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(50)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(50)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1652 MAPE_Forecast=0.1653 MAPE_Test=0.1903 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1595 SMAPE_Forecast=0.1596 SMAPE_Test=0.1768 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.6901 MASE_Forecast=0.6898 MASE_Test=0.9253 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.5820898625007142 L1_Forecast=0.5821675672022341 L1_Test=0.4373917755728303 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.7784586364606513 L2_Forecast=0.7781974157435858 L2_Test=0.5140916849060585 +INFO:pyaf.std:MODEL_COMPLEXITY 50 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.46716516523134277 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag28 -0.16591202108286163 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag15 -0.15982183881941225 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag9 0.1417171675471157 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag2 0.1409110401759769 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag7 -0.14000940091747033 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag48 0.12963386728695633 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1211252731468459 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag32 -0.12091149023601114 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag37 0.1193596496924073 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_500.log b/tests/references/perf_test_ozone_ar_speed_order_500.log new file mode 100644 index 000000000..a64c1a85b --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_500.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 396.2407250404358 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(500)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(500)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0061 MAPE_Forecast=0.0016 MAPE_Test=0.0018 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0061 SMAPE_Forecast=0.0016 SMAPE_Test=0.0018 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0256 MASE_Forecast=0.0059 MASE_Test=0.0078 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02155763127987575 L1_Forecast=0.0049896766028562824 L1_Test=0.0036706256518068505 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.1253140666500127 L2_Forecast=0.006170182235641204 L2_Test=0.004159893727816083 +INFO:pyaf.std:MODEL_COMPLEXITY 500 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9266284416831129 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.4001125083231848 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3275235402294996 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.24663740706276047 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.24524129969230835 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.2375752591294788 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.23371277085314826 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.22381867648886494 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.2202012036774198 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2191643333085786 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_550.log b/tests/references/perf_test_ozone_ar_speed_order_550.log new file mode 100644 index 000000000..ddcb1d249 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_550.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 431.5891270637512 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(550)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(550)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0063 MAPE_Forecast=0.0012 MAPE_Test=0.0012 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0063 SMAPE_Forecast=0.0012 SMAPE_Test=0.0012 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0259 MASE_Forecast=0.0045 MASE_Test=0.0047 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02185442063208843 L1_Forecast=0.0037571723606069775 L1_Test=0.002232759234724454 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.13458000787970648 L2_Forecast=0.004680534596964971 L2_Test=0.0030051530877741803 +INFO:pyaf.std:MODEL_COMPLEXITY 550 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.906430585181047 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3638395743250211 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32520187070462697 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.28020799569860716 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.27969375016701026 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.27081992104349506 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.24829523925639527 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2462523124032715 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.23884710494446437 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.22978743762979542 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_600.log b/tests/references/perf_test_ozone_ar_speed_order_600.log new file mode 100644 index 000000000..ae317030e --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_600.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 490.11017203330994 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(600)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(600)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0065 MAPE_Forecast=0.001 MAPE_Test=0.0011 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0066 SMAPE_Forecast=0.001 SMAPE_Test=0.0011 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0266 MASE_Forecast=0.004 MASE_Test=0.0046 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.022406498328260797 L1_Forecast=0.0033564447177333633 L1_Test=0.0021625798999914867 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.14177262995492115 L2_Forecast=0.004158310493415702 L2_Test=0.0026212738349729306 +INFO:pyaf.std:MODEL_COMPLEXITY 600 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9039215279497292 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.34693581228757864 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33345938867353825 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.329124609969479 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3173010454543127 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.30136370934928164 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.2634973813455129 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.26089074230724096 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag236 0.246680989866069 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag243 0.24568153191725126 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_650.log b/tests/references/perf_test_ozone_ar_speed_order_650.log new file mode 100644 index 000000000..e6457b0c0 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_650.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 537.759642124176 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(650)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(650)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0065 MAPE_Forecast=0.0009 MAPE_Test=0.0009 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0068 SMAPE_Forecast=0.0009 SMAPE_Test=0.0009 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0267 MASE_Forecast=0.0033 MASE_Test=0.0044 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.022520033684121747 L1_Forecast=0.002781334340950565 L1_Test=0.0020816699164746266 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.1486763411701132 L2_Forecast=0.0035120063615152602 L2_Test=0.0024409838064478203 +INFO:pyaf.std:MODEL_COMPLEXITY 650 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.8949071648193107 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3431792721889384 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3353631518294812 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33194962949178075 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32774411792837876 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3216255905751091 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2669068158569821 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.26676839751954806 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.24929073912225203 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag86 -0.24012943262001818 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_700.log b/tests/references/perf_test_ozone_ar_speed_order_700.log new file mode 100644 index 000000000..21d6aa983 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_700.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 590.7197835445404 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(700)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(700)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0067 MAPE_Forecast=0.0007 MAPE_Test=0.0009 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.007 SMAPE_Forecast=0.0007 SMAPE_Test=0.0009 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0276 MASE_Forecast=0.0027 MASE_Test=0.0044 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02324252649320325 L1_Forecast=0.0023138164537433612 L1_Test=0.0020893555199669733 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.15539131542145818 L2_Forecast=0.0029604294728701 L2_Test=0.00261784965810577 +INFO:pyaf.std:MODEL_COMPLEXITY 700 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.8983630100597664 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35423600563247226 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3300663864879737 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3299298916852147 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3274687934640446 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.324117555190398 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.27573377246044817 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2742074513567465 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag211 0.2601385193759526 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.25905901909691564 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_750.log b/tests/references/perf_test_ozone_ar_speed_order_750.log new file mode 100644 index 000000000..0c71c0140 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_750.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 815.4102597236633 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(750)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(750)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0067 MAPE_Forecast=0.0006 MAPE_Test=0.001 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0069 SMAPE_Forecast=0.0006 SMAPE_Test=0.001 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0275 MASE_Forecast=0.0025 MASE_Test=0.0047 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.023234315422083776 L1_Forecast=0.0020976505575114653 L1_Test=0.0022256598624392963 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.15763413469038345 L2_Forecast=0.0026276824540217834 L2_Test=0.0026773460904021378 +INFO:pyaf.std:MODEL_COMPLEXITY 750 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.891909711250419 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.35835022816052753 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.33400931173461257 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3338321522546194 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.328533183464755 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.31818219771353967 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2858485080492868 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.27720433713079873 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.26223580641096245 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.25883717031443004 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_800.log b/tests/references/perf_test_ozone_ar_speed_order_800.log new file mode 100644 index 000000000..d47f43bb6 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_800.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 893.7026283740997 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(800)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(800)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0068 MAPE_Forecast=0.0006 MAPE_Test=0.0007 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.007 SMAPE_Forecast=0.0006 SMAPE_Test=0.0007 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0279 MASE_Forecast=0.0023 MASE_Test=0.0034 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.023544768371943227 L1_Forecast=0.0019245688102899543 L1_Test=0.001591321882642401 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.16004510619144224 L2_Forecast=0.0023560258644979132 L2_Test=0.002015419184996649 +INFO:pyaf.std:MODEL_COMPLEXITY 800 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.898903863021344 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3635505490434918 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.33922547996808844 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33443328703194897 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33126610337978474 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3272210624689798 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2913510370172112 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2827164184785814 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag353 -0.2684810417564479 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.26730268351685416 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_850.log b/tests/references/perf_test_ozone_ar_speed_order_850.log new file mode 100644 index 000000000..304920127 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_850.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 944.9494287967682 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(850)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(850)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0069 MAPE_Forecast=0.0005 MAPE_Test=0.0004 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0071 SMAPE_Forecast=0.0005 SMAPE_Test=0.0004 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0281 MASE_Forecast=0.0019 MASE_Test=0.0021 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.02373658939825961 L1_Forecast=0.0015908942172913791 L1_Test=0.000985997462375987 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.16207926547708698 L2_Forecast=0.001967937630960144 L2_Test=0.0011903548496953905 +INFO:pyaf.std:MODEL_COMPLEXITY 850 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9009148608228692 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.36774958196525365 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.34255247075100065 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.33651435140563 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.33195491963022317 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3278830743915233 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.29106698417854926 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.28360977835871004 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.27513719664019465 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.270673765977303 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_900.log b/tests/references/perf_test_ozone_ar_speed_order_900.log new file mode 100644 index 000000000..d331d2cab --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_900.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 1004.6457479000092 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(900)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(900)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.007 MAPE_Forecast=0.0005 MAPE_Test=0.0005 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0072 SMAPE_Forecast=0.0005 SMAPE_Test=0.0005 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0285 MASE_Forecast=0.0019 MASE_Test=0.0028 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.024078689227328517 L1_Forecast=0.0016180239625596987 L1_Test=0.0013333951655400227 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.16391619683045353 L2_Forecast=0.0019581052787011792 L2_Test=0.0016516630104200158 +INFO:pyaf.std:MODEL_COMPLEXITY 900 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9006326337891377 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3710268258384325 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.343079751980385 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.34133508841005783 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3297120887933968 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.32810392702059626 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2936446502975182 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.28746578969817926 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.2743900772964196 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.2702172201928167 +INFO:pyaf.std:AR_MODEL_DETAIL_END diff --git a/tests/references/perf_test_ozone_ar_speed_order_950.log b/tests/references/perf_test_ozone_ar_speed_order_950.log new file mode 100644 index 000000000..2f2083420 --- /dev/null +++ b/tests/references/perf_test_ozone_ar_speed_order_950.log @@ -0,0 +1,67 @@ +INFO:pyaf.std:START_TRAINING 'Ozone' + Month Ozone Time +0 1955-01 2.7 1955-01-01 +1 1955-02 2.0 1955-02-01 +2 1955-03 3.6 1955-03-01 +3 1955-04 5.0 1955-04-01 +4 1955-05 6.5 1955-05-01 +(10200,) (10200,) count 10200 +unique 10200 +top 1966-07-30 00:00:00 +freq 1 +first 1955-01-01 00:00:00 +last 2150-06-20 00:00:00 +dtype: object count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +count 10200.000000 +mean 3.835784 +std 1.491632 +min 1.200000 +25% 2.600000 +50% 3.750000 +75% 4.825000 +max 8.700000 +Name: Ozone, dtype: float64 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 1063.104126214981 +INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=2111-03-07T00:00:00.000000 TimeDelta= Horizon=12 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=10200 Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.8357843137254903 StdDev=1.4915592159401188 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(950)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_Ozone_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_ConstantTrend_residue_zeroCycle_residue_AR(950)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.0071 MAPE_Forecast=0.0004 MAPE_Test=0.0005 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.0073 SMAPE_Forecast=0.0004 SMAPE_Test=0.0005 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.0288 MASE_Forecast=0.0016 MASE_Test=0.0025 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.024326956478251543 L1_Forecast=0.001378721980223524 L1_Test=0.0011583989384891986 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.16507978841917972 L2_Forecast=0.0016889156300594285 L2_Test=0.0014557527712000138 +INFO:pyaf.std:MODEL_COMPLEXITY 950 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None +INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END +INFO:pyaf.std:TREND_DETAIL_START +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 3.8373128834355827 +INFO:pyaf.std:TREND_DETAIL_END +INFO:pyaf.std:CYCLE_MODEL_DETAIL_START +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _Ozone_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:CYCLE_MODEL_DETAIL_END +INFO:pyaf.std:AR_MODEL_DETAIL_START +INFO:pyaf.std:AR_MODEL_COEFF 1 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag204 0.9012607657295476 +INFO:pyaf.std:AR_MODEL_COEFF 2 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag205 -0.3759664579758385 +INFO:pyaf.std:AR_MODEL_COEFF 3 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag291 -0.3414413605023231 +INFO:pyaf.std:AR_MODEL_COEFF 4 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag214 -0.3397829973782326 +INFO:pyaf.std:AR_MODEL_COEFF 5 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag234 -0.3281736166189236 +INFO:pyaf.std:AR_MODEL_COEFF 6 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag1 0.3276318296453165 +INFO:pyaf.std:AR_MODEL_COEFF 7 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag46 -0.2913309000902372 +INFO:pyaf.std:AR_MODEL_COEFF 8 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag194 -0.2913026529727761 +INFO:pyaf.std:AR_MODEL_COEFF 9 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag252 -0.27875039027830695 +INFO:pyaf.std:AR_MODEL_COEFF 10 _Ozone_ConstantTrend_residue_zeroCycle_residue_Lag149 0.2732458486694681 +INFO:pyaf.std:AR_MODEL_DETAIL_END From c8601ebc6efb463225c3470208cb131787ed3294 Mon Sep 17 00:00:00 2001 From: Antoine Carme Date: Thu, 30 Jul 2020 10:13:32 +0200 Subject: [PATCH 15/15] Add Missing Data Imputation Methods #146 Corrected a typo (impoutation based on the mean) Updated logs --- pyaf/TS/MissingData.py | 2 +- ...g_data_air_passengers_Interpolate_Mean.log | 128 +++++++++--------- ..._missing_data_air_passengers_None_Mean.log | 128 +++++++++--------- ...st_missing_data_ozone_Interpolate_Mean.log | 66 ++++----- ...data_test_missing_data_ozone_None_Mean.log | 66 ++++----- 5 files changed, 195 insertions(+), 195 deletions(-) diff --git a/pyaf/TS/MissingData.py b/pyaf/TS/MissingData.py index 8d5e780a6..c1f2b3d85 100644 --- a/pyaf/TS/MissingData.py +++ b/pyaf/TS/MissingData.py @@ -61,7 +61,7 @@ def apply_signal_imputation_method(self, iInputDS, iSignal): iInputDS[iSignal] = lSignal elif(self.mOptions.mMissingDataOptions.mSignalMissingDataImputation == "Mean"): - lMean = iInputDS[iSignal].median() + lMean = iInputDS[iSignal].mean() lSignal = iInputDS[iSignal].fillna(lMean, method=None) iInputDS[iSignal] = lSignal diff --git a/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log index a49f160da..c706145b2 100644 --- a/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log +++ b/tests/references/missing_data_test_missing_data_air_passengers_Interpolate_Mean.log @@ -1,62 +1,62 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 4.316856622695923 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.683912515640259 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=267.5984848484849 StdDev=97.48409814756306 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_AirPassengers' Min=-212.0 Max=151.0 Mean=2.2196969696969697 StdDev=56.028985246807395 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL 'Diff_AirPassengers_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2241 MAPE_Forecast=0.1303 MAPE_Test=0.2116 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2724 SMAPE_Forecast=0.1324 SMAPE_Test=0.2475 -INFO:pyaf.std:MODEL_MASE MASE_Fit=1.5089 MASE_Forecast=0.6613 MASE_Test=2.0931 -INFO:pyaf.std:MODEL_L1 L1_Fit=48.268959262635995 L1_Forecast=45.890897343739745 L1_Test=94.19030363488753 -INFO:pyaf.std:MODEL_L2 L2_Fit=62.23048368433899 L2_Forecast=57.54047980050583 L2_Test=112.33526236154151 -INFO:pyaf.std:MODEL_COMPLEXITY 56 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=97.21561456213766 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=97.21561456213766 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1392 MAPE_Forecast=0.1271 MAPE_Test=0.0858 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1366 SMAPE_Forecast=0.1204 SMAPE_Test=0.0891 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8276 MASE_Forecast=0.6826 MASE_Test=0.8499 +INFO:pyaf.std:MODEL_L1 L1_Fit=29.29627936840609 L1_Forecast=42.51142846711589 L1_Test=38.24604206753729 +INFO:pyaf.std:MODEL_L2 L2_Fit=40.089960349860895 L2_Forecast=52.589427641045255 L2_Test=46.29821363851404 +INFO:pyaf.std:MODEL_COMPLEXITY 24 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 112.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.46875 +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 230.2475432389937 INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 -0.45828198289550787 -INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.36773663417749636 -INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.35890691259028823 -INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 0.3050145559710619 -INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.2458215455721469 -INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.20490553150780677 -INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.17377400337660448 -INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1654882362659333 -INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15806914068920735 -INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.14193238313007694 +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.2930737164707077 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.23430909408601383 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 0.20698049775290878 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 -0.16010021102939886 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.13915621527008698 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag23 0.11521148933195574 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.08417559250137918 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag15 0.08313889532635783 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.08090411325129698 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag14 0.07768180952243352 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_FORECASTING '['AirPassengers']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.9909157752990723 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.4356403350830078 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_AirPassengers ... 0.1263 0.2141 -1 None Diff_AirPassengers ... 0.1303 0.2116 -2 None _AirPassengers ... 0.1489 0.0795 -3 None _AirPassengers ... 0.1541 0.1560 -4 None _AirPassengers ... 0.1541 0.1560 +0 None Diff_AirPassengers ... 0.1192 0.2037 +1 None Diff_AirPassengers ... 0.1199 0.2060 +2 None _AirPassengers ... 0.1271 0.0858 +3 None _AirPassengers ... 0.1355 0.1529 +4 None _AirPassengers ... 0.1355 0.1529 [5 rows x 8 columns] Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', - 'Diff_AirPassengers', 'Diff_AirPassengers_ConstantTrend', - 'Diff_AirPassengers_ConstantTrend_residue', - 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle', - 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue', - 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', - 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', - 'Diff_AirPassengers_Trend', 'Diff_AirPassengers_Trend_residue', - 'Diff_AirPassengers_Cycle', 'Diff_AirPassengers_Cycle_residue', - 'Diff_AirPassengers_AR', 'Diff_AirPassengers_AR_residue', - 'Diff_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', - 'Diff_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', 'AirPassengers_Forecast_Lower_Bound', 'AirPassengers_Forecast_Upper_Bound'], dtype='object') @@ -73,18 +73,18 @@ memory usage: 3.5 KB None Forecasts time AirPassengers AirPassengers_Forecast -132 1960.000000 NaN 263.395460 -133 1960.083333 NaN 358.811402 -134 1960.166667 NaN 356.880321 -135 1960.250000 NaN 418.256565 -136 1960.333333 NaN 405.907959 -137 1960.416667 NaN 455.559807 -138 1960.500000 NaN 517.136444 -139 1960.583333 NaN 482.980264 -140 1960.666667 NaN 471.688999 -141 1960.750000 NaN 407.830463 -142 1960.833333 NaN 388.357460 -143 1960.916667 NaN 342.624174 +132 1960.000000 NaN 395.206493 +133 1960.083333 NaN 376.518144 +134 1960.166667 NaN 405.261245 +135 1960.250000 NaN 434.403402 +136 1960.333333 NaN 436.186880 +137 1960.416667 NaN 484.806263 +138 1960.500000 NaN 534.104817 +139 1960.583333 NaN 522.110069 +140 1960.666667 NaN 475.980384 +141 1960.750000 NaN 423.320606 +142 1960.833333 NaN 404.982100 +143 1960.916667 NaN 398.642046 @@ -104,17 +104,17 @@ Forecasts }, "Model": { "AR_Model": "AR", - "Best_Decomposition": "Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", "Cycle": "NoCycle", - "Signal_Transoformation": "Difference", + "Signal_Transoformation": "NoTransf", "Trend": "ConstantTrend" }, "Model_Performance": { - "COMPLEXITY": "56", - "MAE": "45.890897343739745", - "MAPE": "0.1303", - "MASE": "0.6613", - "RMSE": "57.54047980050583" + "COMPLEXITY": "24", + "MAE": "42.51142846711589", + "MAPE": "0.1271", + "MASE": "0.6826", + "RMSE": "52.589427641045255" } } } @@ -124,7 +124,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":365.5442810913,"121":366.83868392,"122":402.3261625211,"123":319.0394086405,"124":335.1599353305,"125":407.3971689594,"126":427.9053981254,"127":367.3764279551,"128":273.4256691304,"129":282.3166110423,"130":263.7148991496,"131":259.4376405383,"132":263.3954595823,"133":358.8114019757,"134":356.8803207615,"135":418.2565652055,"136":405.9079591852,"137":455.5598074419,"138":517.1364435327,"139":482.9802644552,"140":471.6889988825,"141":407.8304630844,"142":388.3574595565,"143":342.6241744893}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":324.731647099,"121":377.4990381642,"122":372.4529935597,"123":387.898640168,"124":402.5595081095,"125":457.9094855101,"126":479.1793611597,"127":454.3660169401,"128":425.3223164976,"129":448.7153644489,"130":373.7352192766,"131":354.5771480355,"132":395.2064925569,"133":376.5181440228,"134":405.2612454499,"135":434.403402321,"136":436.186879666,"137":484.8062633937,"138":534.1048169342,"139":522.110068778,"140":475.9803844589,"141":423.320606441,"142":404.9821004876,"143":398.6420463489}} diff --git a/tests/references/missing_data_test_missing_data_air_passengers_None_Mean.log b/tests/references/missing_data_test_missing_data_air_passengers_None_Mean.log index d7edac4a3..f34980f73 100644 --- a/tests/references/missing_data_test_missing_data_air_passengers_None_Mean.log +++ b/tests/references/missing_data_test_missing_data_air_passengers_None_Mean.log @@ -1,62 +1,62 @@ INFO:pyaf.std:START_TRAINING 'AirPassengers' -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.6368353366851807 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['AirPassengers']' 3.984853744506836 INFO:pyaf.std:TIME_DETAIL TimeVariable='time' TimeMin=1949.0 TimeMax=1956.91666666667 TimeDelta=0.08333333333336763 Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=267.5984848484849 StdDev=97.48409814756306 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='Diff_AirPassengers' Min=-212.0 Max=151.0 Mean=2.2196969696969697 StdDev=56.028985246807395 -INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE 'Diff_' -INFO:pyaf.std:BEST_DECOMPOSITION 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] -INFO:pyaf.std:TREND_DETAIL 'Diff_AirPassengers_ConstantTrend' [ConstantTrend] -INFO:pyaf.std:CYCLE_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] -INFO:pyaf.std:AUTOREG_DETAIL 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.2241 MAPE_Forecast=0.1303 MAPE_Test=0.2116 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.2724 SMAPE_Forecast=0.1324 SMAPE_Test=0.2475 -INFO:pyaf.std:MODEL_MASE MASE_Fit=1.5089 MASE_Forecast=0.6613 MASE_Test=2.0931 -INFO:pyaf.std:MODEL_L1 L1_Fit=48.268959262635995 L1_Forecast=45.890897343739745 L1_Test=94.19030363488753 -INFO:pyaf.std:MODEL_L2 L2_Fit=62.23048368433899 L2_Forecast=57.54047980050583 L2_Test=112.33526236154151 -INFO:pyaf.std:MODEL_COMPLEXITY 56 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='AirPassengers' Length=132 Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=97.21561456213766 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_AirPassengers' Min=112.0 Max=559.0 Mean=271.1792452830189 StdDev=97.21561456213766 +INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' +INFO:pyaf.std:BEST_DECOMPOSITION '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [ConstantTrend + NoCycle + AR] +INFO:pyaf.std:TREND_DETAIL '_AirPassengers_ConstantTrend' [ConstantTrend] +INFO:pyaf.std:CYCLE_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle' [NoCycle] +INFO:pyaf.std:AUTOREG_DETAIL '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)' [AR] +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1392 MAPE_Forecast=0.1271 MAPE_Test=0.0858 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1366 SMAPE_Forecast=0.1204 SMAPE_Test=0.0891 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.8276 MASE_Forecast=0.6826 MASE_Test=0.8499 +INFO:pyaf.std:MODEL_L1 L1_Fit=29.29627936840609 L1_Forecast=42.51142846711589 L1_Test=38.24604206753729 +INFO:pyaf.std:MODEL_L2 L2_Fit=40.089960349860895 L2_Forecast=52.589427641045255 L2_Test=46.29821363851404 +INFO:pyaf.std:MODEL_COMPLEXITY 24 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START -INFO:pyaf.std:DIFFERENCING_TRANSFORMATION Difference 112.0 +INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:CONSTANT_TREND ConstantTrend 1.46875 +INFO:pyaf.std:CONSTANT_TREND ConstantTrend 230.2475432389937 INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES Diff_AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} +INFO:pyaf.std:ZERO_CYCLE_MODEL_VALUES _AirPassengers_ConstantTrend_residue_zeroCycle 0.0 {} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START -INFO:pyaf.std:AR_MODEL_COEFF 1 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 -0.45828198289550787 -INFO:pyaf.std:AR_MODEL_COEFF 2 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag2 -0.36773663417749636 -INFO:pyaf.std:AR_MODEL_COEFF 3 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag3 -0.35890691259028823 -INFO:pyaf.std:AR_MODEL_COEFF 4 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 0.3050145559710619 -INFO:pyaf.std:AR_MODEL_COEFF 5 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.2458215455721469 -INFO:pyaf.std:AR_MODEL_COEFF 6 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag8 -0.20490553150780677 -INFO:pyaf.std:AR_MODEL_COEFF 7 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag5 -0.17377400337660448 -INFO:pyaf.std:AR_MODEL_COEFF 8 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.1654882362659333 -INFO:pyaf.std:AR_MODEL_COEFF 9 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag4 -0.15806914068920735 -INFO:pyaf.std:AR_MODEL_COEFF 10 Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag19 -0.14193238313007694 +INFO:pyaf.std:AR_MODEL_COEFF 1 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag1 0.2930737164707077 +INFO:pyaf.std:AR_MODEL_COEFF 2 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag24 0.23430909408601383 +INFO:pyaf.std:AR_MODEL_COEFF 3 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag25 0.20698049775290878 +INFO:pyaf.std:AR_MODEL_COEFF 4 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag26 -0.16010021102939886 +INFO:pyaf.std:AR_MODEL_COEFF 5 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag12 0.13915621527008698 +INFO:pyaf.std:AR_MODEL_COEFF 6 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag23 0.11521148933195574 +INFO:pyaf.std:AR_MODEL_COEFF 7 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag13 -0.08417559250137918 +INFO:pyaf.std:AR_MODEL_COEFF 8 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag15 0.08313889532635783 +INFO:pyaf.std:AR_MODEL_COEFF 9 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag11 0.08090411325129698 +INFO:pyaf.std:AR_MODEL_COEFF 10 _AirPassengers_ConstantTrend_residue_zeroCycle_residue_Lag14 0.07768180952243352 INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_FORECASTING '['AirPassengers']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.661602258682251 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['AirPassengers']' 0.5986931324005127 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_AirPassengers ... 0.1263 0.2141 -1 None Diff_AirPassengers ... 0.1303 0.2116 -2 None _AirPassengers ... 0.1489 0.0795 -3 None _AirPassengers ... 0.1541 0.1560 -4 None _AirPassengers ... 0.1541 0.1560 +0 None Diff_AirPassengers ... 0.1192 0.2037 +1 None Diff_AirPassengers ... 0.1199 0.2060 +2 None _AirPassengers ... 0.1271 0.0858 +3 None _AirPassengers ... 0.1355 0.1529 +4 None _AirPassengers ... 0.1355 0.1529 [5 rows x 8 columns] Forecast Columns Index(['time', 'AirPassengers', 'row_number', 'time_Normalized', - 'Diff_AirPassengers', 'Diff_AirPassengers_ConstantTrend', - 'Diff_AirPassengers_ConstantTrend_residue', - 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle', - 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue', - 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', - 'Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', - 'Diff_AirPassengers_Trend', 'Diff_AirPassengers_Trend_residue', - 'Diff_AirPassengers_Cycle', 'Diff_AirPassengers_Cycle_residue', - 'Diff_AirPassengers_AR', 'Diff_AirPassengers_AR_residue', - 'Diff_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', - 'Diff_AirPassengers_TransformedResidue', 'AirPassengers_Residue', + '_AirPassengers', '_AirPassengers_ConstantTrend', + '_AirPassengers_ConstantTrend_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)', + '_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)_residue', + '_AirPassengers_Trend', '_AirPassengers_Trend_residue', + '_AirPassengers_Cycle', '_AirPassengers_Cycle_residue', + '_AirPassengers_AR', '_AirPassengers_AR_residue', + '_AirPassengers_TransformedForecast', 'AirPassengers_Forecast', + '_AirPassengers_TransformedResidue', 'AirPassengers_Residue', 'AirPassengers_Forecast_Lower_Bound', 'AirPassengers_Forecast_Upper_Bound'], dtype='object') @@ -73,18 +73,18 @@ memory usage: 3.5 KB None Forecasts time AirPassengers AirPassengers_Forecast -132 1960.000000 NaN 263.395460 -133 1960.083333 NaN 358.811402 -134 1960.166667 NaN 356.880321 -135 1960.250000 NaN 418.256565 -136 1960.333333 NaN 405.907959 -137 1960.416667 NaN 455.559807 -138 1960.500000 NaN 517.136444 -139 1960.583333 NaN 482.980264 -140 1960.666667 NaN 471.688999 -141 1960.750000 NaN 407.830463 -142 1960.833333 NaN 388.357460 -143 1960.916667 NaN 342.624174 +132 1960.000000 NaN 395.206493 +133 1960.083333 NaN 376.518144 +134 1960.166667 NaN 405.261245 +135 1960.250000 NaN 434.403402 +136 1960.333333 NaN 436.186880 +137 1960.416667 NaN 484.806263 +138 1960.500000 NaN 534.104817 +139 1960.583333 NaN 522.110069 +140 1960.666667 NaN 475.980384 +141 1960.750000 NaN 423.320606 +142 1960.833333 NaN 404.982100 +143 1960.916667 NaN 398.642046 @@ -104,17 +104,17 @@ Forecasts }, "Model": { "AR_Model": "AR", - "Best_Decomposition": "Diff_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", + "Best_Decomposition": "_AirPassengers_ConstantTrend_residue_zeroCycle_residue_AR(33)", "Cycle": "NoCycle", - "Signal_Transoformation": "Difference", + "Signal_Transoformation": "NoTransf", "Trend": "ConstantTrend" }, "Model_Performance": { - "COMPLEXITY": "56", - "MAE": "45.890897343739745", - "MAPE": "0.1303", - "MASE": "0.6613", - "RMSE": "57.54047980050583" + "COMPLEXITY": "24", + "MAE": "42.51142846711589", + "MAPE": "0.1271", + "MASE": "0.6826", + "RMSE": "52.589427641045255" } } } @@ -124,7 +124,7 @@ Forecasts -{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":365.5442810913,"121":366.83868392,"122":402.3261625211,"123":319.0394086405,"124":335.1599353305,"125":407.3971689594,"126":427.9053981254,"127":367.3764279551,"128":273.4256691304,"129":282.3166110423,"130":263.7148991496,"131":259.4376405383,"132":263.3954595823,"133":358.8114019757,"134":356.8803207615,"135":418.2565652055,"136":405.9079591852,"137":455.5598074419,"138":517.1364435327,"139":482.9802644552,"140":471.6889988825,"141":407.8304630844,"142":388.3574595565,"143":342.6241744893}} +{"time":{"120":1959.0,"121":1959.0833333333,"122":1959.1666666667,"123":1959.25,"124":1959.3333333333,"125":1959.4166666667,"126":1959.5,"127":1959.5833333333,"128":1959.6666666667,"129":1959.75,"130":1959.8333333333,"131":1959.9166666667,"132":1960.0,"133":1960.0833333333,"134":1960.1666666667,"135":1960.25,"136":1960.3333333333,"137":1960.4166666667,"138":1960.5,"139":1960.5833333333,"140":1960.6666666667,"141":1960.75,"142":1960.8333333333,"143":1960.9166666667},"AirPassengers":{"120":360.0,"121":342.0,"122":406.0,"123":396.0,"124":420.0,"125":472.0,"126":548.0,"127":559.0,"128":463.0,"129":407.0,"130":362.0,"131":405.0,"132":null,"133":null,"134":null,"135":null,"136":null,"137":null,"138":null,"139":null,"140":null,"141":null,"142":null,"143":null},"AirPassengers_Forecast":{"120":324.731647099,"121":377.4990381642,"122":372.4529935597,"123":387.898640168,"124":402.5595081095,"125":457.9094855101,"126":479.1793611597,"127":454.3660169401,"128":425.3223164976,"129":448.7153644489,"130":373.7352192766,"131":354.5771480355,"132":395.2064925569,"133":376.5181440228,"134":405.2612454499,"135":434.403402321,"136":436.186879666,"137":484.8062633937,"138":534.1048169342,"139":522.110068778,"140":475.9803844589,"141":423.320606441,"142":404.9821004876,"143":398.6420463489}} diff --git a/tests/references/missing_data_test_missing_data_ozone_Interpolate_Mean.log b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Mean.log index 4e68abac2..bd951e227 100644 --- a/tests/references/missing_data_test_missing_data_ozone_Interpolate_Mean.log +++ b/tests/references/missing_data_test_missing_data_ozone_Interpolate_Mean.log @@ -5,40 +5,40 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 22.09389042854309 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 25.44646143913269 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-08-31T12:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.995705521472393 StdDev=1.341617025993791 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.995705521472393 StdDev=1.341617025993791 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.186 MAPE_Forecast=0.2019 MAPE_Test=0.3391 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1792 SMAPE_Forecast=0.2193 SMAPE_Test=0.3236 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7716 MASE_Forecast=0.7481 MASE_Test=0.8962 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.7097052035997504 L1_Forecast=0.683170439922372 L1_Test=0.8310116976326895 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.9319453089418573 L2_Forecast=0.8631899645276432 L2_Test=1.0573621390350647 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.188 MAPE_Forecast=0.2006 MAPE_Test=0.3519 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1803 SMAPE_Forecast=0.2168 SMAPE_Test=0.3324 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7753 MASE_Forecast=0.7285 MASE_Test=0.8791 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7145520691271712 L1_Forecast=0.6817689026379347 L1_Test=0.8610542867528806 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.936037373145944 L2_Forecast=0.8681323608664606 L2_Test=1.094596095941803 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.957004747946751, array([-1.51797933])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.965785535083154, array([-1.50051087])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.021757535043924747 {1: -1.658176842441088, 2: -1.5681158920836822, 3: -0.6127361256970136, 4: -0.3178345860081, 5: -0.3782307474080735, 6: 0.5314856051298322, 7: 1.0613262103845034, 8: 1.1326811347201735, 9: 0.9816731389750735, 10: 0.36126368848731394, 11: -0.09755797332098304, 12: -0.7674438878433212} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.014071375094074412 {1: -1.680753383303863, 2: -1.589427287176314, 3: -0.5309239020664922, 4: -0.286693376937565, 5: -0.34927144272171917, 6: 0.509028025734996, 7: 1.0408267745174866, 8: 1.1217174990609555, 9: 0.9609395567267165, 10: 0.43186741296269515, 11: -0.11162593681022503, 12: -0.7317040941973292} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_FORECASTING '['Ozone']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.5276765823364258 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4412376880645752 Split Transformation ... ForecastMAPE TestMAPE -0 None Diff_Ozone ... 0.2012 0.8251 -1 None _Ozone ... 0.2019 0.3391 -2 None _Ozone ... 0.2228 0.2746 -3 None _Ozone ... 0.2288 0.3513 -4 None _Ozone ... 0.2321 0.2598 +0 None _Ozone ... 0.2006 0.3519 +1 None _Ozone ... 0.2241 0.3282 +2 None _Ozone ... 0.2315 0.3516 +3 None _Ozone ... 0.2378 0.5886 +4 None _Ozone ... 0.2383 0.7264 [5 rows x 8 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -66,18 +66,18 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1971-12-31 10:00:00 NaN 2.152106 -205 1972-01-30 20:00:00 NaN 1.251391 -206 1972-03-01 06:00:00 NaN 2.286850 -207 1972-03-31 16:00:00 NaN 2.276868 -208 1972-05-01 02:00:00 NaN 2.501391 -209 1972-05-31 12:00:00 NaN 2.491409 -210 1972-06-30 22:00:00 NaN 3.391143 -211 1972-07-31 08:00:00 NaN 3.911002 -212 1972-08-30 18:00:00 NaN 3.972375 -213 1972-09-30 04:00:00 NaN 3.811385 -214 1972-10-30 14:00:00 NaN 3.180993 -215 1972-11-30 00:00:00 NaN 2.712189 +204 1971-12-31 10:00:00 NaN 2.220073 +205 1972-01-30 20:00:00 NaN 1.261156 +206 1972-03-01 06:00:00 NaN 2.401119 +207 1972-03-31 16:00:00 NaN 2.391252 +208 1972-05-01 02:00:00 NaN 2.563037 +209 1972-05-31 12:00:00 NaN 2.553170 +210 1972-06-30 22:00:00 NaN 3.401602 +211 1972-07-31 08:00:00 NaN 3.923534 +212 1972-08-30 18:00:00 NaN 3.994557 +213 1972-09-30 04:00:00 NaN 3.823912 +214 1972-10-30 14:00:00 NaN 3.284973 +215 1972-11-30 00:00:00 NaN 2.731612 @@ -104,10 +104,10 @@ Forecasts }, "Model_Performance": { "COMPLEXITY": "20", - "MAE": "0.683170439922372", - "MAPE": "0.2019", - "MASE": "0.7481", - "RMSE": "0.8631899645276432" + "MAE": "0.6817689026379347", + "MAPE": "0.2006", + "MASE": "0.7285", + "RMSE": "0.8681323608664606" } } } @@ -117,7 +117,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-01-30T12:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-05-31T12:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1971-12-31T10:00:00.000Z","205":"1972-01-30T20:00:00.000Z","206":"1972-03-01T06:00:00.000Z","207":"1972-03-31T16:00:00.000Z","208":"1972-05-01T02:00:00.000Z","209":"1972-05-31T12:00:00.000Z","210":"1972-06-30T22:00:00.000Z","211":"1972-07-31T08:00:00.000Z","212":"1972-08-30T18:00:00.000Z","213":"1972-09-30T04:00:00.000Z","214":"1972-10-30T14:00:00.000Z","215":"1972-11-30T00:00:00.000Z"},"Ozone":{"192":1.8,"193":3.9,"194":2.2,"195":3.0,"196":2.4,"197":3.9,"198":3.5,"199":3.9,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.3809657977,"193":1.3712845978,"194":2.4070441147,"195":2.6917721901,"196":2.6215307406,"197":2.6115213645,"198":4.0410689461,"199":4.1022504061,"200":3.9410689461,"201":3.3108142076,"202":2.8418190815,"203":2.1620878789,"204":2.1521058508,"205":1.2513908681,"206":2.2868495567,"207":2.2768675285,"208":2.5013908787,"209":2.4914088505,"210":3.3911431749,"211":3.911001752,"212":3.9723746482,"213":3.8113846244,"214":3.1809931457,"215":2.7121894558}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-01-30T12:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-05-31T12:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1971-12-31T10:00:00.000Z","205":"1972-01-30T20:00:00.000Z","206":"1972-03-01T06:00:00.000Z","207":"1972-03-31T16:00:00.000Z","208":"1972-05-01T02:00:00.000Z","209":"1972-05-31T12:00:00.000Z","210":"1972-06-30T22:00:00.000Z","211":"1972-07-31T08:00:00.000Z","212":"1972-08-30T18:00:00.000Z","213":"1972-09-30T04:00:00.000Z","214":"1972-10-30T14:00:00.000Z","215":"1972-11-30T00:00:00.000Z"},"Ozone":{"192":1.8,"193":3.9957055215,"194":2.2,"195":3.0,"196":2.4,"197":3.9957055215,"198":3.5,"199":3.9957055215,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.3892402287,"193":1.3796704372,"194":2.5199301269,"195":2.7541042609,"196":2.6817942037,"197":2.6719000125,"198":4.0521040385,"199":4.1229383719,"200":3.9521040385,"201":3.4132999033,"202":2.8597501624,"203":2.2299400137,"204":2.2200728557,"205":1.2611564087,"206":2.401118732,"207":2.391251574,"208":2.5630368754,"209":2.5531697175,"210":3.401602028,"211":3.9235336189,"212":3.9945571855,"213":3.8239120852,"214":3.2849727835,"215":2.7316122758}} diff --git a/tests/references/missing_data_test_missing_data_ozone_None_Mean.log b/tests/references/missing_data_test_missing_data_ozone_None_Mean.log index 47a97bb7c..f3395af04 100644 --- a/tests/references/missing_data_test_missing_data_ozone_None_Mean.log +++ b/tests/references/missing_data_test_missing_data_ozone_None_Mean.log @@ -5,40 +5,40 @@ INFO:pyaf.std:START_TRAINING 'Ozone' 2 1955-03 3.6 1955-03-01 3 1955-04 5.0 1955-04-01 4 1955-05 6.5 1955-05-01 -INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 4.239546775817871 +INFO:pyaf.std:END_TRAINING_TIME_IN_SECONDS '['Ozone']' 8.153930902481079 INFO:pyaf.std:TIME_DETAIL TimeVariable='Time' TimeMin=1955-01-01T00:00:00.000000 TimeMax=1967-09-01T00:00:00.000000 TimeDelta= Horizon=12 -INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 -INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.9764705882352938 StdDev=1.3421650983004036 +INFO:pyaf.std:SIGNAL_DETAIL_ORIG SignalVariable='Ozone' Length=204 Min=1.2 Max=8.7 Mean=3.995705521472393 StdDev=1.341617025993791 +INFO:pyaf.std:SIGNAL_DETAIL_TRANSFORMED TransformedSignalVariable='_Ozone' Min=1.2 Max=8.7 Mean=3.995705521472393 StdDev=1.341617025993791 INFO:pyaf.std:BEST_TRANSOFORMATION_TYPE '_' INFO:pyaf.std:BEST_DECOMPOSITION '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [LinearTrend + Seasonal_MonthOfYear + NoAR] INFO:pyaf.std:TREND_DETAIL '_Ozone_LinearTrend' [LinearTrend] INFO:pyaf.std:CYCLE_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear' [Seasonal_MonthOfYear] INFO:pyaf.std:AUTOREG_DETAIL '_Ozone_LinearTrend_residue_Seasonal_MonthOfYear_residue_NoAR' [NoAR] -INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1822 MAPE_Forecast=0.1997 MAPE_Test=0.33 -INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1756 SMAPE_Forecast=0.216 SMAPE_Test=0.3036 -INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7609 MASE_Forecast=0.7309 MASE_Test=0.8424 -INFO:pyaf.std:MODEL_L1 L1_Fit=0.6998707162064494 L1_Forecast=0.6674497132372681 L1_Test=0.7811220833031284 -INFO:pyaf.std:MODEL_L2 L2_Fit=0.9283159076128001 L2_Forecast=0.8437799795537803 L2_Test=0.9944646980528729 +INFO:pyaf.std:MODEL_MAPE MAPE_Fit=0.1838 MAPE_Forecast=0.1989 MAPE_Test=0.3363 +INFO:pyaf.std:MODEL_SMAPE SMAPE_Fit=0.1769 SMAPE_Forecast=0.2141 SMAPE_Test=0.3057 +INFO:pyaf.std:MODEL_MASE MASE_Fit=0.7653 MASE_Forecast=0.7128 MASE_Test=0.8103 +INFO:pyaf.std:MODEL_L1 L1_Fit=0.7053110237633853 L1_Forecast=0.6670552716111284 L1_Test=0.7936697403781885 +INFO:pyaf.std:MODEL_L2 L2_Fit=0.9333702519353555 L2_Forecast=0.8578933504042751 L2_Test=1.0259339752958363 INFO:pyaf.std:MODEL_COMPLEXITY 20 INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_START INFO:pyaf.std:SIGNAL_TRANSFORMATION_MODEL_VALUES NoTransf None INFO:pyaf.std:SIGNAL_TRANSFORMATION_DETAIL_END INFO:pyaf.std:TREND_DETAIL_START -INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.956972420759976, array([-1.51809377])) +INFO:pyaf.std:LINEAR_RIDGE_TREND LinearTrend (4.965755611314433, array([-1.50062806])) INFO:pyaf.std:TREND_DETAIL_END INFO:pyaf.std:CYCLE_MODEL_DETAIL_START -INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.021764394693804867 {1: -1.7170834143981075, 2: -1.4871298623881208, 3: -0.8992611126075549, 4: -0.3080986690256613, 5: -0.33858427618918574, 6: 0.39136927582080006, 7: 1.061325235520295, 8: 1.1916069530656594, 9: 0.9022216515254193, 10: 0.7721209105610938, 11: -0.10781574763554258, 12: -0.7675304185559155} +INFO:pyaf.std:SEASONAL_MODEL_VALUES _Ozone_LinearTrend_residue_Seasonal_MonthOfYear 0.014077907172760362 {1: -1.7286265339375042, 2: -1.5001681007757202, 3: -0.9248153080198773, 4: -0.2297921492481243, 5: -0.3384627934632696, 6: 0.3682238829972482, 7: 1.0408265051022725, 8: 1.1696093282467883, 9: 0.8870049192605158, 10: 0.756099985582384, 11: -0.07678999236818496, 12: -0.7317908858088091} INFO:pyaf.std:CYCLE_MODEL_DETAIL_END INFO:pyaf.std:AR_MODEL_DETAIL_START INFO:pyaf.std:AR_MODEL_DETAIL_END INFO:pyaf.std:START_FORECASTING '['Ozone']' -INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4609558582305908 +INFO:pyaf.std:END_FORECAST_TIME_IN_SECONDS '['Ozone']' 0.4246695041656494 Split Transformation ... ForecastMAPE TestMAPE -0 None _Ozone ... 0.1997 0.3300 -1 None _Ozone ... 0.2126 0.2566 -2 None _Ozone ... 0.2156 0.4677 -3 None Diff_Ozone ... 0.2278 0.3931 -4 None _Ozone ... 0.2321 0.2598 +0 None _Ozone ... 0.1989 0.3363 +1 None _Ozone ... 0.2235 0.4781 +2 None _Ozone ... 0.2308 0.2754 +3 None Diff_Ozone ... 0.2314 0.6299 +4 None _Ozone ... 0.2369 0.7358 [5 rows x 8 columns] Forecast Columns Index(['Time', 'Ozone', 'row_number', 'Time_Normalized', '_Ozone', @@ -66,18 +66,18 @@ memory usage: 5.2 KB None Forecasts Time Ozone Ozone_Forecast -204 1972-01-01 NaN 1.202309 -205 1972-02-01 NaN 1.422090 -206 1972-03-01 NaN 2.000442 -207 1972-04-01 NaN 2.581431 -208 1972-05-01 NaN 2.541100 -209 1972-06-01 NaN 3.260881 -210 1972-07-01 NaN 3.920992 -211 1972-08-01 NaN 4.041100 -212 1972-09-01 NaN 3.741542 -213 1972-10-01 NaN 3.601596 -214 1972-11-01 NaN 2.711486 -215 1972-12-01 NaN 2.041927 +204 1972-01-01 NaN 1.222992 +205 1972-02-01 NaN 1.441394 +206 1972-03-01 NaN 2.007340 +207 1972-04-01 NaN 2.692307 +208 1972-05-01 NaN 2.573904 +209 1972-06-01 NaN 3.270535 +210 1972-07-01 NaN 3.933406 +211 1972-08-01 NaN 4.052132 +212 1972-09-01 NaN 3.759472 +213 1972-10-01 NaN 3.618835 +214 1972-11-01 NaN 2.775889 +215 1972-12-01 NaN 2.111157 @@ -104,10 +104,10 @@ Forecasts }, "Model_Performance": { "COMPLEXITY": "20", - "MAE": "0.6674497132372681", - "MAPE": "0.1997", - "MASE": "0.7309", - "RMSE": "0.8437799795537803" + "MAE": "0.6670552716111284", + "MAPE": "0.1989", + "MASE": "0.7128", + "RMSE": "0.8578933504042751" } } } @@ -117,7 +117,7 @@ Forecasts -{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":3.9,"194":2.2,"195":3.0,"196":2.4,"197":3.9,"198":3.5,"199":3.9,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.3220896176,"193":1.541870038,"194":2.1205501528,"195":2.7015394648,"196":2.6612088916,"197":3.380989312,"198":4.0411003056,"199":4.1612088916,"200":3.8616504584,"201":3.7217047514,"202":2.8315949616,"203":2.1620353246,"204":1.2023091972,"205":1.4220896176,"206":2.0004415669,"207":2.5814308788,"208":2.5411003056,"209":3.260880726,"210":3.9209917197,"211":4.0411003056,"212":3.7415418725,"213":3.6015961655,"214":2.7114863757,"215":2.0419267387}} +{"Time":{"192":"1971-01-01T00:00:00.000Z","193":"1971-02-01T00:00:00.000Z","194":"1971-03-01T00:00:00.000Z","195":"1971-04-01T00:00:00.000Z","196":"1971-05-01T00:00:00.000Z","197":"1971-06-01T00:00:00.000Z","198":"1971-07-01T00:00:00.000Z","199":"1971-08-01T00:00:00.000Z","200":"1971-09-01T00:00:00.000Z","201":"1971-10-01T00:00:00.000Z","202":"1971-11-01T00:00:00.000Z","203":"1971-12-01T00:00:00.000Z","204":"1972-01-01T00:00:00.000Z","205":"1972-02-01T00:00:00.000Z","206":"1972-03-01T00:00:00.000Z","207":"1972-04-01T00:00:00.000Z","208":"1972-05-01T00:00:00.000Z","209":"1972-06-01T00:00:00.000Z","210":"1972-07-01T00:00:00.000Z","211":"1972-08-01T00:00:00.000Z","212":"1972-09-01T00:00:00.000Z","213":"1972-10-01T00:00:00.000Z","214":"1972-11-01T00:00:00.000Z","215":"1972-12-01T00:00:00.000Z"},"Ozone":{"192":1.8,"193":3.9957055215,"194":2.2,"195":3.0,"196":2.4,"197":3.9957055215,"198":3.5,"199":3.9957055215,"200":2.7,"201":2.5,"202":1.6,"203":1.2,"204":null,"205":null,"206":null,"207":null,"208":null,"209":null,"210":null,"211":null,"212":null,"213":null,"214":null,"215":null},"Ozone_Forecast":{"192":1.3413940183,"193":1.559796362,"194":2.1260662352,"195":2.8110333045,"196":2.6926309608,"197":3.3892615478,"198":4.0521324705,"199":4.1708592041,"200":3.8781987057,"201":3.7375620725,"202":2.8946160051,"203":2.2298834122,"204":1.2229916746,"205":1.4413940183,"206":2.0073395015,"207":2.6923065709,"208":2.5739042272,"209":3.2705348142,"210":3.9334057368,"211":4.0521324705,"212":3.759471972,"213":3.6188353388,"214":2.7758892714,"215":2.1111566785}}